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5 Reasons Why Data Analytics is Important in Problem Solving

Data analytics  is important in problem solving and it is a key sub-branch of data science. Even though there are endless data analytics applications in a business, one of the most crucial roles it plays is problem-solving. 

Using data analytics not only boosts your problem-solving skills, but it also makes them a whole lot faster and efficient, automating a majority of the long and repetitive processes.

Whether you’re fresh out of university graduate or a professional who works for an organization, having top-notch  problem-solving skills  is a necessity and always comes in handy. 

Everybody keeps facing new kinds of complex problems every day, and a lot of time is invested in overcoming these obstacles. Moreover, much valuable time is lost while trying to find solutions to unexpected problems, and your plans also get disrupted often.

This is where data analytics comes in. It lets you find and analyze the relevant data without too much of human-support. It’s a real time-saver and has become a necessity in problem-solving nowadays. So if you don’t already use data analytics in solving these problems, you’re probably missing out on a lot!

As the saying goes from the chief analytics officer of TIBCO, 

“Think analytically, rigorously, and systematically about a  business problem  and come up with a  solution that leverages the available data .”  

– Michael O’Connell.

In this article, I will explain the importance of data analytics in problem-solving and go through the top 5 reasons why it cannot be ignored. So, let’s dive into it right away.

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What is Data Analytics?

Whenever you perform any operation on any data, intending to explore that data and find different trends or conclusions in it, you’re analyzing the data, which’s exactly what we call data analytics.

Data analytics is the art of automating processes using algorithms to collect raw data from multiple sources and transform it. This results in achieving the data that’s ready to be studied and used for analytical purposes, such as finding the trends, patterns, and so forth.

Businesses are using data analytics in a  variety  of ways. From predicting their customer behaviors to making more informed business decisions, analytics is everywhere. A recent survey shows that around  94%  of enterprises believe that data and analytics are important to their growth.

Why is Data Analytics Important in Problem Solving?

Problem-solving and data analytics often proceed hand in hand. When a particular problem is faced, everybody’s first instinct is to look for supporting data. Data analytics plays a pivotal role in finding this data and analyzing it to be used for tackling that specific problem.

Although the analytical part sometimes adds further complexities, since it’s a whole different process that might get  challenging  sometimes, it eventually helps you get a better hold of the situation. 

Also, you come up with a more informed solution, not leaving anything out of the equation.

Having strong analytical skills help you dig deeper into the problem and get all the insights you need. Once you have extracted enough relevant knowledge, you can proceed with solving the problem. 

However, you need to make sure you’re using the  right, and complete  data, or using data analytics may even backfire for you. Misleading data can make you believe things that don’t exist, and that’s bound to take you off the track, making the problem appear more complex or simpler than it is.

Let’s see a very straightforward daily life example to examine the importance of data analytics in problem-solving; what would you do if a question appears on your exam, but it doesn’t have enough data provided for you to solve the question? 

Obviously, you won’t be able to solve that problem. You need a certain level of facts and figures about the situation first, or you’ll be wandering in the dark.

However, once you get the information you need, you can analyze the situation and quickly develop a solution. Moreover, getting more and more knowledge of the situation will further ease your ability to solve the given problem. This is precisely how data analytics assists you. It eases the process of collecting information and processing it to solve real-life problems.

Data analytics is important in problem-solving

5 Reasons Why Data Analytics Is Important in Problem Solving

Now that we’ve established a general idea of how strongly connected analytical skills and problem-solving are, let’s dig deeper into the top 5 reasons  why data analytics is important in problem-solving .

1. Uncover Hidden Details

Data analytics is great at putting the minor details out in the spotlight. Sometimes, even the most qualified data scientists might not be able to spot tiny details existing in the data used to solve a certain problem. However, computers don’t miss. This enhances your ability to solve problems, and you might be able to come up with solutions a lot quicker.

Data analytics tools have a wide variety of features that let you study the given data very thoroughly and catch any hidden or recurring trends using built-in features without needing any effort. These tools are entirely automated and require very little programming support to work. They’re great at excavating the depths of data, going back way into the past.

2. Automated Models

Automation is the future. Businesses don’t have enough time nor the budget to let manual workforces go through tons of data to solve business problems. 

Instead, what they do is hire a data analyst who automates problem-solving processes, and once that’s done, problem-solving becomes completely independent of any human intervention.

The tools can collect, combine, clean, and transform the relevant data all by themselves and finally using it to predict the solutions. Pretty impressive, right? 

However, there might be some complex problems appearing now and then, which cannot be handled by algorithms since they’re completely new and nothing similar has come up before. But a lot of the work is still done using the algorithms, and it’s only once in a blue moon that they face something that rare.

However, there’s one thing to note here; the process of automation by designing complex analytical and  ML algorithms  might initially be a bit challenging. Many factors need to be kept in mind, and a lot of different scenarios may occur. But once it goes up and running, you’ll be saving a significant amount of manpower as well as resources.

3. Explore Similar Problems

If you’re using a data analytics approach for solving your problems, you will have a lot of data available at your disposal. Most of the data would indirectly help you in the form of similar problems, and you only have to figure out how these problems are related. 

Once you’re there, the process gets a lot smoother because you get references to how such problems were tackled in the past.

Such data is available all over the internet and is automatically extracted by the data analytics tools according to the current problems. People run into difficulties all over the world, and there’s no harm if you follow the guidelines of someone who has gone through a similar situation before.

Even though exploring similar problems is also possible without the help of data analytics, we’re generating a lot of data  nowadays , and searching through tons of this data isn’t as easy as you might think. So, using analytical tools is the smart choice since they’re quite fast and will save a lot of your time.

4. Predict Future Problems

While we have already gone through the fact that data analytics tools let you analyze the data available from the past and use it to predict the solutions to the problems you’re facing in the present, it also goes the other way around.

Whenever you use data analytics to solve any present problem, the tools you’re using store the data related to the problem and saves it in the form of variables forever. This way, similar problems faced in the future don’t need to be analyzed again. Instead, you can reuse the previous solutions you have, or the algorithms can predict the solutions for you even if the problems have evolved a bit.

This way, you’re not wasting any time on the problems that are recurring in nature. You jump directly onto the solution whenever you face a situation, and this makes the job quite simple.

5. Faster Data Extraction

New data analytics tools are coming out every day, with each of them getting better and better. When businesses approach solving a problem, most of their time is spent on data acquisition and getting the data ready to be used in solving the problem.

However, with the latest tools, the  data extraction  is greatly reduced, and everything is done automatically with no human intervention whatsoever. 

Moreover, once the appropriate data is mined and cleaned, there are not many hurdles that remain, and the rest of the processes are done without a lot of delays.

When businesses come across a problem, around  70%-80%  is their time is consumed while gathering the relevant data and transforming it into usable forms. So, you can estimate how quick the process could get if the data analytics tools automate all this process.

Even though many of the tools are open-source, if you’re a bigger organization that can spend a bit on paid tools, problem-solving could get even better. The paid  tools  are literal workhorses, and in addition to generating the data, they could also develop the models to your solutions, unless it’s a very complex one, without needing any support of data analysts.

What problems can data analytics solve? 3 Real-World Examples

Employee performance problems .

Imagine a Call Center with over 100 agents

By Analyzing data sets of employee attendance, productivity, and issues that tend to delay in resolution. Through that, preparing refresher training plans, and mentorship plans according to key weak areas identified.

Sales Efficiency Problems 

Imagine a Business that is spread out across multiple cities or regions

By analyzing the number of sales per area, the size of the sales reps’ team, the overall income and disposable income of potential customers, you can come up with interesting insights as to why some areas sell more or less than the others. Through that, prepping a recruitment and training plan or area expansion in order to boost sales could be a good move.

Business Investment Decisions Problems

Imagine an Investor with a portfolio of apps/software)

By analyzing the number of subscribers, sales, the trends in usage, the demographics, you can decide which peace of software has a better Return on Investment over the long term.

Data analytics is a sub-field of data science and plays a major role in problem-solving. It makes it easy for us to extract and gather information from various sources and combine it to use it in our solutions effectively. More the information we can gather, the easier tackling the problem gets for us.

Throughout the article, we’ve seen various reasons why data analytics is very important for problem-solving. 

Many different problems that may seem very complex in the start are made seamless using data analytics, and there are hundreds of analytical tools that can help us solve problems in our everyday lives.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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solving problems through data analytics

7 Common Data Analytics Problems – & How to Solve Them

solving problems through data analytics

By Rotem Yifat, Product Marketing Manager

May 30, 2023

In a nutshell:

  • Data analysts often face issues with limited value of historical insights and unused insights.
  • Data goes unused due to limited capacity to process and analyze it.
  • Bias is unavoidable in traditional predictive modeling.
  • Long time to value and data-security concerns are common problems.
  • Predictive analytics platforms can overcome these issues by providing accurate predictions, easy integration, and automated processes.

As a data analyst, your job is to make sense of data by breaking it down into manageable parts, processing it, and performing statistical analyses that reveal trends, patterns, and relationships. And you typically need to present those insights in a way that’s easy for stakeholders to understand.

This process is crucial for organizations that are looking for a data-driven way to make informed decisions, improve business outcomes, and gain a competitive advantage. And thanks to the emergence of new tools, technologies, and techniques, the realm of what’s possible is constantly expanding. Indeed, with the advent of AI, data analytics has become more powerful and efficient than ever before.

However, like many analysts, you may be grappling with some all-too-familiar issues that prevent analysts from doing their best work and making a significant business impact.

In this article, we’ll explore seven common issues faced by data analysts, and how using  predictive analytics  can be a great way to overcome them.

Problem 1: Limited value of historical insights

The most common application of data analysis is  descriptive analytics , where historical data is analyzed in order to understand past trends and events.

The problem? Relying solely on historical insights has limited value, especially in fast-changing businesses where consumer behavior and preferences, as well as market conditions, are constantly evolving. By definition, such insights are based on past trends and events, which may not be applicable to current or future scenarios, and can lead to inaccurate or incomplete analyses.

Relying only on past data can also create bias towards the status quo, which limits your ability to identify new opportunities and potential risks. Take the example of a retailer that relies solely on historical data to determine which products to stock: they’re likely to miss out on new trends or shifts in customer preferences, which leads to missed sales opportunities.

To address this problem, you should strive to complement historical insights with predictive analytics. With this approach, you can identify emerging trends and quickly adapt to changing market conditions.

And by leveraging machine learning within a predictive analytics platform , you can identify meaningful patterns that would otherwise be undetectable. This leads to highly accurate predictions that your business can take advantage of in order to make proactive, well-informed decisions.

Problem 2: Insights aren’t utilized

No one loves the idea of toiling away for months or years, only to discover that their work has been overlooked, undervalued, or not put into practice.

Unfortunately, statistical insights are often viewed as unusable, or even meaningless, if stakeholders can’t easily identify and take relevant action. As mentioned above, this is often the case with descriptive analytics, where there is a focus on the past rather than the future. And as a result, analysts often invest lots of energy into preparing dashboards and reports that are rarely ever used or incorporated into the business workflow.

One way to overcome this challenge is by using a predictive analytics platform that allows you to choose from a variety of pre-built models that can be customized to fit specific use cases. This way, you can easily generate actionable predictions that serve a particular goal, and also enjoy the benefit of automatically generated, easy-to-use dashboards.

In addition, predictions can be integrated directly into your existing work tools. For example: if your goal is to reduce customer churn, you can integrate churn predictions alongside your existing CRM data. This would allow your colleagues to strategize something like an email campaign built on segments reflecting how likely each customer is to churn.

The best way to ensure all your hard work is put to use is by generating insights that can truly guide business decisions and help your company achieve mission-critical KPIs.

Problem 3: Data goes unused

Businesses collect and generate massive amounts of data. But even when they have sufficient resources, they’re not able to use much of this data due to humans’ limited capacity to think about and process data. In many cases, analysts aren’t even sure of whether particular data is worth using due to data quality issues or questions about its meaning.

For these reasons and more, data professionals are generally only able to build a small number of rule-based models, which can only account for two or three variables at a time.

This common challenge can be overcome with a predictive analytics platform like Pecan. By leveraging  automated machine learning  to analyze vast amounts of data, you can slash the time and effort it takes to decide which data is relevant. Here’s what we mean…

You can instantly feed raw data into a predictive model, and this data can come from any source (such as sales data, user engagement data , customer demographics, and social media). And automating this process means you can use updated data to generate fresh predictions regularly, helping you stay on top of changing customer behavior and market conditions. 

The platform will then automatically determine which data is relevant (through behind-the-scenes processes like feature selection and feature engineering), and then will find the best predictive model that can be built using that data.These  automated processes  enable you to analyze massive datasets and generate accurate predictions within a matter of hours, instead of months. This means you can focus on communicating and creating real value out of your insights, without doing all the heavy lifting.

issues with hand-built machine learning models also included in text

Hand-built models might seem like the ideal solution to data problems, but they can introduce their own issues.

Problem 4: Bias is unavoidable

Traditional predictive modeling involves the use of statistical and mathematical techniques to uncover relationships and identify trends. But as scientific as it may be, there is always human bias in the process of selecting variables.

This means that hand-built models will inevitably, at some point:

  • Include variables that are not actually important for the model (but possibly correlate with the outcome)
  • Leave out important variables because they don’t fit with the model builder’s preconceived ideas
  • Perpetuate or even amplify existing bias (e.g., by restricting your model to a certain gender or zip code)
  • Fail to generalize beyond your sample set (e.g., if the model is based on data from a limited time period)

This bias doesn’t happen when you use a  predictive analytics platform . One key reason is because automated feature engineering will evaluate and construct thousands of potential variables that could be used in your model, and then determine which are most relevant. (Naturally, this multi-variable approach also leads to more accurate and reliable predictions.)

In the case of Pecan, a simple dashboard will reveal how your model arrived at its predictions, by showing the degree to which each variable (a.k.a. feature) influenced its outcomes. This knowledge also enables you to identify and mitigate any potential biases that may be introduced through your raw data itself.

Problem 5: Long time to value

To be implemented and adopted, many analytics tools require significant change management and engineering assistance. And, of course, analytics projects themselves demand a significant amount of time and resources. Sometimes they will bear fruit, and other times they won’t.

Contrast that with a predictive analytics platform, which can do in  hours  what it might take a data analyst or scientist  months  to achieve. Not only can you connect multiple data sources to the platform for automatic importing, but you can use pre-built model templates to generate highly accurate predictions with speed and ease. This means your business colleagues will be able to act quickly on the provided insights to achieve their business goals.

Another thing to keep in mind: data changes over time, and models require ongoing maintenance in order to remain accurate and effective. In the case of traditional rule-based models, this often means restarting the modeling process from scratch. 

But with an automated approach, a predictive model only needs to be built once. With automated model-retraining and monitoring capabilities, all you need to do is feed it new data and/or adjust the variables you wish to use in your predictions.

automated predictive analytics vs traditional data science

The time required to resolve data problems with an automated platform can be far less than preparing data by hand for modeling.

Problem 6: Data-security concerns

Technical difficulties aside, concerns and regulations around data security can make it extremely challenging to integrate different data tools and achieve smooth adoption.

According to a  2020 report by IBM , the average cost of a data breach is $3.86 million. Navigating security best practices, and avoiding potential security issues, is a lot to ask of a data analyst who is not a security professional, yet is tasked with managing and integrating sensitive data across multiple tools, whether locally or in the cloud.

This is where an all-in-one predictive analytics platform again proves advantageous. For example, Pecan prioritizes data security and takes a variety of measures to ensure sensitive information is protected at all times. Let an enterprise-grade solution take care of security business – so you can focus on yours.

Problem 7: Tedious, time-consuming processes

If we haven’t made it clear by this point, turning raw data into actionable insights is no small feat. And we’d be remiss not to spend some time talking about the person at the center of all this: the data analyst.

In data analytics projects, manual processes add multiple layers of complexity, difficulty, and stress. Analysts often need to carry out tedious and/or time-consuming tasks like data collection, data cleaning, data transformation, data visualization, and of course, statistical analysis itself. Mix in a variety of tools and techniques – from programming languages like Python or R, to data-visualization tools like Tableau or Power BI, to statistical software like SPSS or SAS – and an analyst’s work is cut out for them.

Furthermore, data that’s incomplete, inconsistent, inaccurate, or outdated can significantly impact the effectiveness of a statistical model. So when you pile issues of data quality onto an already-heavy load, fulfilling a data analytics project can seem like an insurmountable feat.

Fortunately, predictive analytics platforms can take care of all of the most tedious and complex processes. For example, Pecan automatically performs tasks like data prep , feature engineering, model tuning, and model deployment and monitoring. It can identify and remove incomplete or inaccurate data, and automatically transform your data into the right format for training accurate machine learning models .

What this means is that data analysts, instead of being weighed down by outdated data practices, can focus on building business use cases and imagining how their predictive insights can help solve real business needs.

Wrapping up

An honest assessment of the “old way” of doing things will lead to one obvious conclusion: It’s time to update the way your team performs data analytics. You and your organization should aim for a more holistic approach that maximizes the value of your data. You can gain a complete understanding of your customers and business processes, and make more informed (and profitable) decisions.

Predictive analytics platforms are a great solution for overcoming many of the pain points that plague data analysts. By leveraging machine learning algorithms and automated ML processes, you can quickly analyze huge volumes of data, generate accurate predictions that target a specific business need, and help your business make better decisions that keep you ahead of the competition.

Ready to see how easy it can be to use predictive analytics to supercharge your data analytics role and solve your data problems?  Sign up for a free trial  now and try it yourself!

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What is data analysis? Examples and how to get started

A hero image with an icon of a line graph / chart

Even with years of professional experience working with data, the term "data analysis" still sets off a panic button in my soul. And yes, when it comes to serious data analysis for your business, you'll eventually want data scientists on your side. But if you're just getting started, no panic attacks are required.

Table of contents:

Quick review: What is data analysis?

Why is data analysis important, types of data analysis (with examples), data analysis process: how to get started, frequently asked questions.

Zapier is the leader in workflow automation—integrating with 6,000+ apps from partners like Google, Salesforce, and Microsoft. Use interfaces, data tables, and logic to build secure, automated systems for your business-critical workflows across your organization's technology stack. Learn more .

Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals. 

Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative data (e.g., surveys and case studies) to paint the whole picture. Here are two simple examples (of a nuanced topic) to show you what I mean.

An example of quantitative data analysis is an online jewelry store owner using inventory data to forecast and improve reordering accuracy. The owner looks at their sales from the past six months and sees that, on average, they sold 210 gold pieces and 105 silver pieces per month, but they only had 100 gold pieces and 100 silver pieces in stock. By collecting and analyzing inventory data on these SKUs, they're forecasting to improve reordering accuracy. The next time they order inventory, they order twice as many gold pieces as silver to meet customer demand.

An example of qualitative data analysis is a fitness studio owner collecting customer feedback to improve class offerings. The studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.

Here's why it's worth implementing data analysis for your business:

Understand your target audience: You might think you know how to best target your audience, but are your assumptions backed by data? Data analysis can help answer questions like, "What demographics define my target audience?" or "What is my audience motivated by?"

Inform decisions: You don't need to toss and turn over a decision when the data points clearly to the answer. For instance, a restaurant could analyze which dishes on the menu are selling the most, helping them decide which ones to keep and which ones to change.

Adjust budgets: Similarly, data analysis can highlight areas in your business that are performing well and are worth investing more in, as well as areas that aren't generating enough revenue and should be cut. For example, a B2B software company might discover their product for enterprises is thriving while their small business solution lags behind. This discovery could prompt them to allocate more budget toward the enterprise product, resulting in better resource utilization.

Identify and solve problems: Let's say a cell phone manufacturer notices data showing a lot of customers returning a certain model. When they investigate, they find that model also happens to have the highest number of crashes. Once they identify and solve the technical issue, they can reduce the number of returns.

There are five main types of data analysis—with increasingly scary-sounding names. Each one serves a different purpose, so take a look to see which makes the most sense for your situation. It's ok if you can't pronounce the one you choose. 

Types of data analysis including text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.

Text analysis: What is happening?

Text analysis, AKA data mining , involves pulling insights from large amounts of unstructured, text-based data sources : emails, social media, support tickets, reviews, and so on. You would use text analysis when the volume of data is too large to sift through manually. 

Here are a few methods used to perform text analysis, to give you a sense of how it's different from a human reading through the text: 

Word frequency identifies the most frequently used words. For example, a restaurant monitors social media mentions and measures the frequency of positive and negative keywords like "delicious" or "expensive" to determine how customers feel about their experience. 

Language detection indicates the language of text. For example, a global software company may use language detection on support tickets to connect customers with the appropriate agent. 

Keyword extraction automatically identifies the most used terms. For example, instead of sifting through thousands of reviews, a popular brand uses a keyword extractor to summarize the words or phrases that are most relevant. 

Because text analysis is based on words, not numbers, it's a bit more subjective. Words can have multiple meanings, of course, and Gen Z makes things even tougher with constant coinage. Natural language processing (NLP) software will help you get the most accurate text analysis, but it's rarely as objective as numerical analysis. 

Statistical analysis: What happened?

Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.

Descriptive analysis

Descriptive analysis looks at numerical data and calculations to determine what happened in a business. Companies use descriptive analysis to determine customer satisfaction , track campaigns, generate reports, and evaluate performance. 

Here are a few methods used to perform descriptive analysis: 

Measures of frequency identify how frequently an event occurs. For example, a popular coffee chain sends out a survey asking customers what their favorite holiday drink is and uses measures of frequency to determine how often a particular drink is selected. 

Measures of central tendency use mean, median, and mode to identify results. For example, a dating app company might use measures of central tendency to determine the average age of its users.

Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field. 

Inferential analysis

Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large. 

Here are a few methods used when performing inferential analysis: 

Hypothesis testing identifies which variables impact a particular topic. For example, a business uses hypothesis testing to determine if increased sales were the result of a specific marketing campaign. 

Confidence intervals indicates how accurate an estimate is. For example, a company using market research to survey customers about a new product may want to determine how confident they are that the individuals surveyed make up their target market. 

Regression analysis shows the effect of independent variables on a dependent variable. For example, a rental car company may use regression analysis to determine the relationship between wait times and number of bad reviews. 

Diagnostic analysis: Why did it happen?

Diagnostic analysis, also referred to as root cause analysis, uncovers the causes of certain events or results. 

Here are a few methods used to perform diagnostic analysis: 

Time-series analysis analyzes data collected over a period of time. A retail store may use time-series analysis to determine that sales increase between October and December every year. 

Data drilling uses business intelligence (BI) to show a more detailed view of data. For example, a business owner could use data drilling to see a detailed view of sales by state to determine if certain regions are driving increased sales.

Correlation analysis determines the strength of the relationship between variables. For example, a local ice cream shop may determine that as the temperature in the area rises, so do ice cream sales. 

Predictive analysis: What is likely to happen?

Predictive analysis aims to anticipate future developments and events. By analyzing past data, companies can predict future scenarios and make strategic decisions.  

Here are a few methods used to perform predictive analysis: 

Machine learning uses AI and algorithms to predict outcomes. For example, search engines employ machine learning to recommend products to online shoppers that they are likely to buy based on their browsing history. 

Decision trees map out possible courses of action and outcomes. For example, a business may use a decision tree when deciding whether to downsize or expand. 

Prescriptive analysis: What action should we take?

The highest level of analysis, prescriptive analysis, aims to find the best action plan. Typically, AI tools model different outcomes to predict the best approach. While these tools serve to provide insight, they don't replace human consideration, so always use your human brain before going with the conclusion of your prescriptive analysis. Otherwise, your GPS might drive you into a lake.

Here are a few methods used to perform prescriptive analysis: 

Lead scoring is used in sales departments to assign values to leads based on their perceived interest. For example, a sales team uses lead scoring to rank leads on a scale of 1-100 depending on the actions they take (e.g., opening an email or downloading an eBook). They then prioritize the leads that are most likely to convert. 

Algorithms are used in technology to perform specific tasks. For example, banks use prescriptive algorithms to monitor customers' spending and recommend that they deactivate their credit card if fraud is suspected. 

The actual analysis is just one step in a much bigger process of using data to move your business forward. Here's a quick look at all the steps you need to take to make sure you're making informed decisions. 

Circle chart with data decision, data collection, data cleaning, data analysis, data interpretation, and data visualization.

Data decision

As with almost any project, the first step is to determine what problem you're trying to solve through data analysis. 

Make sure you get specific here. For example, a food delivery service may want to understand why customers are canceling their subscriptions. But to enable the most effective data analysis, they should pose a more targeted question, such as "How can we reduce customer churn without raising costs?" 

These questions will help you determine your KPIs and what type(s) of data analysis you'll conduct , so spend time honing the question—otherwise your analysis won't provide the actionable insights you want.

Data collection

Next, collect the required data from both internal and external sources. 

Internal data comes from within your business (think CRM software, internal reports, and archives), and helps you understand your business and processes.

External data originates from outside of the company (surveys, questionnaires, public data) and helps you understand your industry and your customers. 

You'll rely heavily on software for this part of the process. Your analytics or business dashboard tool, along with reports from any other internal tools like CRMs , will give you the internal data. For external data, you'll use survey apps and other data collection tools to get the information you need.

Data cleaning

Data can be seriously misleading if it's not clean. So before you analyze, make sure you review the data you collected.  Depending on the type of data you have, cleanup will look different, but it might include: 

Removing unnecessary information 

Addressing structural errors like misspellings

Deleting duplicates

Trimming whitespace

Human checking for accuracy 

You can use your spreadsheet's cleanup suggestions to quickly and effectively clean data, but a human review is always important.

Data analysis

Now that you've compiled and cleaned the data, use one or more of the above types of data analysis to find relationships, patterns, and trends. 

Data analysis tools can speed up the data analysis process and remove the risk of inevitable human error. Here are some examples.

Spreadsheets sort, filter, analyze, and visualize data. 

Business intelligence platforms model data and create dashboards. 

Structured query language (SQL) tools manage and extract data in relational databases. 

Data interpretation

After you analyze the data, you'll need to go back to the original question you posed and draw conclusions from your findings. Here are some common pitfalls to avoid:

Correlation vs. causation: Just because two variables are associated doesn't mean they're necessarily related or dependent on one another. 

Confirmation bias: This occurs when you interpret data in a way that confirms your own preconceived notions. To avoid this, have multiple people interpret the data. 

Small sample size: If your sample size is too small or doesn't represent the demographics of your customers, you may get misleading results. If you run into this, consider widening your sample size to give you a more accurate representation. 

Data visualization

Last but not least, visualizing the data in the form of graphs, maps, reports, charts, and dashboards can help you explain your findings to decision-makers and stakeholders. While it's not absolutely necessary, it will help tell the story of your data in a way that everyone in the business can understand and make decisions based on. 

Automate your data collection

Data doesn't live in one place. To make sure data is where it needs to be—and isn't duplicative or conflicting—make sure all your apps talk to each other. Zapier automates the process of moving data from one place to another, so you can focus on the work that matters to move your business forward.

Need a quick summary or still have a few nagging data analysis questions? I'm here for you.

What are the five types of data analysis?

The five types of data analysis are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Each type offers a unique lens for understanding data: text analysis provides insights into text-based content, statistical analysis focuses on numerical trends, diagnostic analysis looks into problem causes, predictive analysis deals with what may happen in the future, and prescriptive analysis gives actionable recommendations.

What is the data analysis process?

The data analysis process involves data decision, collection, cleaning, analysis, interpretation, and visualization. Every stage comes together to transform raw data into meaningful insights. Decision determines what data to collect, collection gathers the relevant information, cleaning ensures accuracy, analysis uncovers patterns, interpretation assigns meaning, and visualization presents the insights.

What is the main purpose of data analysis?

In business, the main purpose of data analysis is to uncover patterns, trends, and anomalies, and then use that information to make decisions, solve problems, and reach your business goals.

Related reading: 

How to get started with data collection and analytics at your business

How to conduct your own market research survey

Automatically find and match related data across apps

How to build an analysis assistant with ChatGPT

What can the ChatGPT data analysis chatbot do?

This article was originally published in October 2022 and has since been updated with contributions from Cecilia Gillen. The most recent update was in September 2023.

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Shea is a content writer currently living in Charlotte, North Carolina. After graduating with a degree in Marketing from East Carolina University, she joined the digital marketing industry focusing on content and social media. In her free time, you can find Shea visiting her local farmers market, attending a country music concert, or planning her next adventure.

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Data extraction is the process of taking actionable information from larger, less structured sources to be further refined or analyzed. Here's how to do it.

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Data Analysis: Identify the Problem You’re Trying to Solve

Data Analysis: Identify the Problem You’re Trying to Solve

One of the most important steps in data analysis is often missed or not done with enough care: identifying the problem you're trying to solve.

It's like Alice in  Alice's Adventures in Wonderland.  She follows the rabbit down the hole with no idea where she is going, what she is facing, or how to get home. Once down the hole, she gets distracted by one adventure after another. When she eventually realizes she is lost and asks the Cheshire Cat for directions, he gives her the sage advice, "It doesn't matter where you're going if you don't know where you want to go."

Distractions, Confusion, and Rabbit Holes

Similarly, we can get lost in metaphorical rabbit holes when doing analysis. Going down an analysis rabbit hole means spending time on something that ends up being a waste. Imagine a situation where we are tasked with assessing the success of a bus rapid transit (BRT) system in our community. There are many potential ways to get lost: 

  • Confusion about purpose — A team member might have a different definition of success from us. We might assume our project is going to focus on the economic benefits of the BRT and our partner could be exploring environmental impact. One of us ends up wasting our time.
  • Missing data — We could not fully thinking our plan through. We might spend hours researching the economic benefits of BRTs before realizing we don't have access to any publicly available economic data.
  • Distractions — We could spend too much time on a small part of the analysis — for example, making a graph look pretty — while ignoring the rest of the analysis.

Ultimately, there are lots of rabbit holes we could get lost in. It might not be until we finally finish the project that we realize how much time we spent that wasn't really productive.

Define Your Purpose

What the Cheshire Cat knew was that in order avoid going down rabbit holes, we need to know our purpose.

Socrata has created the  Socrata Data Academy , a series of free, online courses designed for government workers, in order to teach basic to advance data analysis skills and steer all students away from rabbit holes.

The first lesson in the online training is to identify the problem we are trying to solve. In the training, we learn about the importance of doing diligence upfront, such as:

  • Scope the problem correctly
  • Gather information
  • Understand the true goals of the analysis
  • Define what needs to get done by when
  • Put this all together in a clear and concise written problem statement that gets signed off on by all stakeholders

Identifying the problem is the crucial first step of the analysis and is often overlooked. However, doing it well helps us avoid going down analysis rabbit holes.

To learn more about identifying the problem and other data analysis skills, sign up for our  free online course  through the Socrata Data Academy.

Related content.

How to analyze a problem

May 7, 2023 Companies that harness the power of data have the upper hand when it comes to problem solving. Rather than defaulting to solving problems by developing lengthy—sometimes multiyear—road maps, they’re empowered to ask how innovative data techniques could resolve challenges in hours, days or weeks, write  senior partner Kayvaun Rowshankish  and coauthors. But when organizations have more data than ever at their disposal, which data should they leverage to analyze a problem? Before jumping in, it’s crucial to plan the analysis, decide which analytical tools to use, and ensure rigor. Check out these insights to uncover ways data can take your problem-solving techniques to the next level, and stay tuned for an upcoming post on the potential power of generative AI in problem-solving.

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  • Data Management

The “problem-solver” approach to data preparation for analytics

By david loshin, president, knowledge integrity, inc..

In many environments, the maturity of your reporting and business analytics functions depends on how effective you are at managing data before it’s time to analyze it. Traditional environments relied on a provisioning effort to conduct data preparation for analytics. After extracting data from source systems, the data landed at a staging area for cleansing, standardization and reorganization before loading it in a data warehouse.

Recently, there has been signification innovation in the evolution of end-user discovery and analysis tools. Often, these systems allow the analyst to bypass the traditional data warehouse by accessing the source data sets directly.

This is putting more data – and analysis of that data – in the hands of more people. This encourages “undirected analysis,” which doesn’t pose any significant problems; the analysts are free to point their tools at any (or all!) data sets, with the hope of identifying some nugget of actionable knowledge that can be exploited.

It’s important to ask the IT department to facilitate a problem-solver approach to data preparation by adjusting the methods by which data sets are made available.

However, it would be naïve to presume that many organizations are willing to allow a significant amount of “data-crunching” time to be spent on purely undirected discovery. Rather, data scientists have specific directions to solve particular types of business problems, such as analyzing:

  • Global spend to identify opportunities for cost reduction.
  • Logistics and facets of the supply chain to optimize the delivery channels.
  • Customer interactions to increase customer lifetime value.

Different challenges have different data needs, but if the analysts need to use data from the original sources, it’s worth considering an alternate approach to the conventional means of data preparation. The data warehouse approach balances two key goals: organized data inclusion (a large amount of data is integrated into a single data platform), and objective presentation (data is managed in an abstract data model specifically suited for querying and reporting).

A new approach to data preparation for analytics

Does the data warehouse approach work in more modern, “built-to-suit” analytics? Maybe not, especially if data scientists go directly to the data – bypassing the data warehouse altogether. For data scientists, armed with analytics at their fingertips, let’s consider a rational, five-step approach to problem-solving.

  • Clarify the question you want to answer.
  • Identify the information necessary to answer the question.
  • Determine what information is available and what is not available.
  • Acquire the information that is not available.
  • Solve the problem.

In this process, steps 2, 3, and 4 all deal with data assessment and acquisition – but in a way that is parametrically opposed to the data warehouse approach. First, the warehouse’s data inclusion is predefined, which means that the data that is not available at step 3 may not be immediately accessible from the warehouse in step 4. Second, the objectiveness of the warehouse’s data poses a barrier to creativity on the analyst’s behalf. In fact, this is why data discovery tools that don’t rely on the data warehouse are becoming more popular. By acquiring or accessing alternate data sources, the analyst can be more innovative in problem-solving!

Preparing data with the problem in mind

A problem-solver approach to data preparation for analytics lets the analyst decide what information needs to be integrated into the analysis platform, what transformations are to be done, and how the data is to be used. This approach differs from the conventional extract/transform/load cycle in three key ways:

  • First, the determination of the data sources is done by the analyst based on data accessibility, not what the IT department has interpreted as a set of requirements.
  • Second, the analyst is not constrained by the predefined transformations embedded in the data warehouse ETL processes.
  • Third, the analyst decides the transformations and standardizations that are relevant for the analysis, not the IT department.

While it’s a departure from “standard operating procedure,” it’s important to ask the IT department to facilitate a problem-solver approach to data preparation by adjusting the methods by which data sets are made available. In particular, instead of loading all data into a data warehouse, IT can create an inventory or catalog of data assets that are available for consumption. And instead of applying a predefined set of data transformations, a data management center of excellence can provide a library of available transformations – and a services and rendering layer that an analyst can use for customized data preparation.

Both of these capabilities require some fundamental best practices and enterprise information management tools aside from the end-user discovery technology, such as:

  • Metadata management as a framework for creating the data asset catalog and ensuring consistency in each data artifact’s use.
  • Data integration and standardization tools that have an “easy-to-use” interface that can be employed by practitioner and analyst alike.
  • Business rules-based data transformations that can be performed as part of a set of enterprise data services.
  • Data federation and virtualization to enable access to virtual data sets whose storage footprint may span multiple sources.
  • Event stream processing to enable acquisition of data streams as viable and usable data sources.

An evolving environment that encourages greater freedom for the data analyst community should not confine those analysts based on technology decisions for data preparation. Empowering the analysts with flexible tools for data preparation will help speed the time from the initial question to a practical, informed and data-driven decision.

David Loshin

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at b-eye-network.com and numerous books, white papers, and web seminars on a variety of data management best practices.

David Loshin

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Data Analytics with R

1 problem solving with data, 1.1 introduction.

This chapter will introduce you to a general approach to solving problems and answering questions using data. Throughout the rest of the module, we will reference back to this chapter as you work your way through your own data analysis exercises.

The approach is applicable to actuaries, data scientists, general data analysts, or anyone who intends to critically analyze data and develop insights from data.

This framework, which some may refer to as The Data Science Process includes the following five main components:

  • Data Collection
  • Data Cleaning
  • Exploratory Data Analysis
  • Model Building
  • Inference and Communication

solving problems through data analytics

Note that all five steps may not be applicable in every situation, but these steps should guide you as you think about how to approach each analysis you perform.

In the subsections below, we’ll dive into each of these in more detail.

1.2 Data Collection

In order to solve a problem or answer a question using data, it seems obvious that you must need some sort of data to start with. Obtaining data may come in the form of pre-existing or generating new data (think surveys). As an actuary, your data will often come from pre-existing sources within your company. This could include querying data from databases or APIs, being sent excel files, text files, etc. You may also find supplemental data online to assist you with your project.

For example, let’s say you work for a health insurance company and you are interested in determining the average drive time for your insured population to the nearest in-network primary care providers to see if it would be prudent to contract with additional doctors in the area. You would need to collect at least three pieces of data:

  • Addresses of your insured population (internal company source/database)
  • Addresses of primary care provider offices (internal company source/database)
  • Google Maps travel time API to calculate drive times between addresses (external data source)

In summary, data collection provides the fundamental pieces needed to solve your problem or answer your question.

1.3 Data Cleaning

We’ll discuss data cleaning in a little more detail in later chapters, but this phase generally refers to the process of taking the data you collected in step 1, and turning it into a usable format for your analysis. This phase can often be the most time consuming as it may involve handling missing data as well as pre-processing the data to be as error free as possible.

Depending on where you source your data will have major implications for how long this phase takes. For example, many of us actuaries benefit from devoted data engineers and resources within our companies who exert much effort to make our data as clean as possible for us to use. However, if you are sourcing your data from raw files on the internet, you may find this phase to be exceptionally difficult and time intensive.

1.4 Exploratory Data Analysis

Exploratory Data Analysis , or EDA, is an entire subject itself. In short, EDA is an iterative process whereby you:

  • Generate questions about your data
  • Search for answers, patterns, and characteristics of your data by transforming, visualizing, and summarizing your data
  • Use learnings from step 2 to generate new questions and insights about your data

We’ll cover some basics of EDA in Chapter 4 on Data Manipulation and Chapter 5 on Data Visualization, but we’ll only be able to scratch the surface of this topic.

A successful EDA approach will allow you to better understand your data and the relationships between variables within your data. Sometimes, you may be able to answer your question or solve your problem after the EDA step alone. Other times, you may apply what you learned in the EDA step to help build a model for your data.

1.5 Model Building

In this step, we build a model, often using machine learning algorithms, in an effort to make sense of our data and gain insights that can be used for decision making or communicating to an audience. Examples of models could include regression approaches, classification algorithms, tree-based models, time-series applications, neural networks, and many, many more. Later in this module, we will practice building our own models using introductory machine learning algorithms.

It’s important to note that while model building gets a lot of attention (because it’s fun to learn and apply new types of models), it typically encompasses a relatively small portion of your overall analysis from a time perspective.

It’s also important to note that building a model doesn’t have to mean applying machine learning algorithms. In fact, in actuarial science, you may find more often than not that the actuarial models you create are Microsoft Excel-based models that blend together historical data, assumptions about the business, and other factors that allow you make projections or understand the business better.

1.6 Inference and Communication

The final phase of the framework is to use everything you’ve learned about your data up to this point to draw inferences and conclusions about the data, and to communicate those out to an audience. Your audience may be your boss, a client, or perhaps a group of actuaries at an SOA conference.

In any instance, it is critical for you to be able to condense what you’ve learned into clear and concise insights and convince your audience why your insights are important. In some cases, these insights will lend themselves to actionable next steps, or perhaps recommendations for a client. In other cases, the results will simply help you to better understand the world, or your business, and to make more informed decisions going forward.

1.7 Wrap-Up

As we conclude this chapter, take a few minutes to look at a couple alternative visualizations that others have used to describe the processes and components of performing analyses. What do they have in common?

  • Karl Rohe - Professor of Statistics at the University of Wisconsin-Madison
  • Chanin Nantasenamat - Associate Professor of Bioinformatics and Youtuber at the “Data Professor” channel

solving problems through data analytics

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David Simchi-Levi's research focuses on developing and implementing robust and efficient techniques for supply chains and revenue management.

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David Simchi-Levi is a professor of engineering systems with appointments at the Institute for Data, Systems, and Society and the Department of Civil and Environmental Engineering (CEE) at MIT. His research focuses on developing and implementing robust and efficient techniques for supply chains and revenue management. He has founded three companies in the fields of supply chain and business analytics: LogicTools, a venture focused on supply chain analytics, which became a part of IBM; OPS Rules, a business analytics venture that was acquired by Accenture Analytics; and Opalytics, which focuses on cloud computing for business analytics.

In addition to his role as a professor of engineering systems, Simchi-Levi leads the Accenture and MIT Alliance in Business Analytics. The alliance brings together MIT faculty, PhD students, and a host of partner companies to solve some of the most pressing challenges global organizations are facing today. The alliance is cross-industry, collaborating with companies in sectors ranging from retail space, to government and financial services, to the airline industry. This diversity enables the alliance to be cross-functional, with projects that focus on everything from supply chain optimization to revenue generation and from predictive maintenance to fraud detection. In many cases, these endeavors have led to companywide adoption of MIT technology, analytics, and algorithms to increase productivity and profits.

Putting theory to practice, Simchi-Levi and his team worked with a large mining company in Latin America to improve its mining operations. Their algorithm receives data every five seconds from thousands of sensors and predicts product quality 10, 15, and 20 hours prior to product completion. Specifically, they used these data to identify impurities, such as silica level in the finished product, and to suggest corrective strategies to improve quality. In the realm of price optimization, Simchi-Levi’s alliance has worked with a number of major online retailers, including Groupon; B2W, Latin America’s largest online retailer; and Rue La La. Rue La La operates in the flash-sale industry, in which online retailers use events to temporarily discount products.

“But how do you price a product on the website the first time if you have no historical data?” Simchi-Levi asks. “We applied machine learning algorithms to learn from similar products and then optimization algorithms to price products the company never sold before, and the impact was dramatic, increasing revenue by about 11 percent.”

It’s a deceptively simple answer. But for Simchi-Levi, well known as a visionary thought leader in his field, solving tough problems is at the heart of the work of the Accenture and MIT Alliance in Business Analytics. “In the case of Groupon and B2W, we developed a three-step process to optimize and automate pricing decisions,” he says. First, they utilize machine learning to combine internal historical data with external data to create a complete profile of consumer behavior. Second, they post pricing decisions on the website and observe consumer behavior. Third, they learn and improve pricing decisions based on that behavior in order to optimize the final price. “In all of these cases, we made a big impact on the bottom line: increasing revenue, increasing profit, and increasing market share,” he says.

At any point in time, Simchi-Levi’s business analytics alliance, which has been going strong since 2013, has between 10 and 20 projects running simultaneously. He suggests the reason so many companies are turning to MIT for their business challenges has a lot to do with recent technology trends and the Alliance’s role at the forefront of those developments. Specifically, he mentions three technology trends: digitization; automation; and analytics, including the application of machine learning and artificial intelligence algorithms. However, he observes that initially it is difficult for executives to accept that black box analytics can do a better job at pricing a product than the merchants who know the product and have been working in the industry for 25 years. While Simchi-Levi concedes that this is partially true, he notes that with thousands upon thousands of products to price, merchants can focus only on the top 10 percent, whereas MIT’s analytics can achieve the same performance on the top 10 percent, while achieving an equally impressive performance on the middle 50 percent and equally similar performance on the long tail.

More precisely, “While the company merchant will focus on a small portion, we can focus on the entire company portfolio,” he says. “We’re talking about the ability to use data and analytics to optimize prices for thousands of products.” “Business analytics is a very exciting area. If you open any business journal you will see references to data science and data analytics,” Simchi-Levi says. But his expertise has led him to explore a deeper truth about this obsession with data analytics: “My experience is that while there is a lot of excitement around this area, industry actually does very little [in the way of] using data and analytics to automate and improve processes.”

He says there are three main challenges industry faces in the area of data analytics: data quality, information silos, and internal resistance. “What we do at MIT is bring all of these opportunities together by improving the data quality, convincing executives to start experimenting with some of the technology, and connecting different data sources into an effective platform for analytics.”

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3 business problems data analytics can help solve

Sep 18, 2023

Generative artificial intelligence is booming, the post-COVID economy wobbles on, and the climate crisis is growing. Amid this disruption, what practical problems are global businesses trying to solve in 2023?

Each year, the MIT Sloan Master of Business Analytics Capstone Project  partners students with companies that are looking to solve a business problem with data analytics. The program offers unique and up-close insight into what companies were grappling with at the beginning of 2023. This year, students worked on 41 different projects with 33 different companies. The winning projects looked at measuring innovation through patents for Accenture and using artificial intelligence to improve drug safety for Takeda.

“This annual tradition is an insightful pulse check on the ‘data wish list’ of the industry’s top analytics leaders,” said MIT Sloan lecturer  Jordan Levine,  who leads the Capstone program.

Here are three questions that companies are seeking to answer with analytics.  

1. How can data help us identify growth in specific geographic regions?  

Businesses looking to open new locations or invest in real estate are using data to find areas that are poised for growth.

Understanding urbanization is important for firms like JPMorgan Chase , which aims to reach new clients and serve existing customers by opening new bank branches in U.S. cities. To get a handle on what areas are likely to grow in the future, the company is using satellite images — including land-cover segmentation from Google — to predict urbanization rates and identify hot spots . 

Small and medium-sized businesses account for about 99% of U.S. companies but only 40% of the U.S. economy. Using historic transaction data and U.S. census data, Visa is looking at what parts of the U.S. have the most potential for SMB growth  and what levers it can use to help develop those areas, such as helping businesses accept digital transactions. 

Asset management firm Columbia Threadneedle wants to identify promising areas for real estate investment in Europe by building a predictive tool for location growth, using factors such as economic drivers, livability, connectivity, and demographics. MBAn students created a tool that predicts long-term growth potential for more than 600 cities and identifies key factors used to make those predictions.

2. How can data help us empower front-line workers?

Employees working directly with customers or in the field often have to make educated guesses and snap decisions. Companies are turning to data analytics to create support tools that will improve efficiency, accuracy, and sales. 

Coca-Cola Southwest Beverages is looking to improve how front-line workers assess store inventory and create orders — a process that is now time-consuming and prone to errors. Using demographics, consumption trends, historical sales data, and out-of-stock information, a sales forecast algorithm will improve forecasting, increase sales, and simplify operations.

Handle Global , a health care supply chain technology company, is looking to help hospitals estimate budget allocation and capital expenditures for medical devices, given the churn of assets, variations in types and models, and mergers and acquisitions between manufacturers and hospital systems. The company is looking to develop a decision support tool that uses historic data to make better purchasing decisions.

3. What’s the best way to get the most from large or unwieldy datasets?

While data analytics can produce powerful results, some data is still hard to process, such as unstructured data — data that does not conform to a specific format — or large datasets. Companies are looking for ways to efficiently process and gain insight from this kind of data, which can be time-consuming and inefficient to process. 

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Health insurance pricing data is now available to competing companies, thanks to a new U.S. government regulation . But this information isn’t easy to access because of the sheer volume of data, insurer noncompliance with disclosure requirements, and data that’s broken into several different categories. Wellmark Blue Cross and Blue Shield is looking to create a coverage rate transparency tool that recommends pricing and areas for negotiation to help it maintain competitive advantage and see optimal profits.

Information services company Wolters Kluwer ’s compliance business unit helps firms meet regulatory requirements while managing risk and increasing efficiency. But verifying government documents, such as vehicle registrations, can be an error-prone and time-consuming process, and the documents have a high rejection rate. The company is looking to create a document classification system using natural language processing and computer vision that makes paperwork that is usually handled manually more accurate and easier to process.

CogniSure AI was created in 2019 to use technology to solve the problem of unstructured data, which makes it difficult to digitize the insurance underwriting industry. The company is looking to build a generic machine learning tool to process documents that are not yet automated , such as loss runs — claims histories of past losses — which have complex and varied formats and structures.

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4 Examples of Business Analytics in Action

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  • 15 Jan 2019

Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.

“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond , who teaches the online course Business Analytics . “It’s the way the world is going.”

Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.

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What Is Business Analytics?

Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions.

There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. Diagnostic analytics can be used to identify the root cause of a problem. In the case of prescriptive analytics , testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.

Related : 4 Types of Data Analytics to Improve Decision-Making

Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.

Check out the video below to learn more about business analytics, and subscribe to our YouTube channel for more explainer content!

Business Analytics vs. Data Science

It’s important to highlight the difference between business analytics and data science . While both processes use big data to solve business problems they’re separate fields.

The main goal of business analytics is to extract meaningful insights from data to guide organizational decisions, while data science is focused on turning raw data into meaningful conclusions through using algorithms and statistical models. Business analysts participate in tasks such as budgeting, forecasting, and product development, while data scientists focus on data wrangling , programming, and statistical modeling.

While they consist of different functions and processes, business analytics and data science are both vital to today’s organizations. Here are four examples of how organizations are using business analytics to their benefit.

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Business Analytics Examples

According to a recent survey by McKinsey , an increasing share of organizations report using analytics to generate growth. Here’s a look at how four companies are aligning with that trend and applying data insights to their decision-making processes.

1. Improving Productivity and Collaboration at Microsoft

At technology giant Microsoft , collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by increasing the number of employees per building and reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft , which found that people are more likely to collaborate when they’re more closely located to one another.

In an article for the Harvard Business Review , the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members and an estimated savings of $520,000 per year in employee time.

The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.

For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.

2. Enhancing Customer Support at Uber

Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.

COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.

For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing —a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.

Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.

At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.

With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand , with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, which typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s past recipe preferences, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has successfully measured the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.

By employing predictive analytics to better understand customers, Blue Apron has improved its user experience, identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings.

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

Consumers are crucial to the success of multinational food and beverage company PepsiCo. The company supplies retailers in more than 200 countries worldwide , serving a billion customers every day. To ensure the right quantities and types of products are available to consumers in certain locations, PepsiCo uses big data and predictive analytics.

PepsiCo created a cloud-based data and analytics platform called Pep Worx to make more informed decisions regarding product merchandising. With Pep Worx, the company identifies shoppers in the United States who are likely to be highly interested in a specific PepsiCo brand or product.

For example, Pep Worx enabled PepsiCo to distinguish 24 million households from its dataset of 110 million US households that would be most likely to be interested in Quaker Overnight Oats. The company then identified specific retailers that these households might shop at and targeted their unique audiences. Ultimately, these customers drove 80 percent of the product’s sales growth in its first 12 months after launch.

PepsiCo’s analysis of consumer data is a prime example of how data-driven decision-making can help today’s organizations maximize profits.

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Developing a Data Mindset

As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” Hammond says . “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Do you want to leverage the power of data within your organization? Explore Business Analytics —one of our online business essentials courses —to learn how to use data analysis to solve business problems.

This post was updated on March 24, 2023. It was originally published on January 15, 2019.

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Introducing Data Analytics and Data Science into Your Organisation with Carefully Crafted Solutions.

“I only believe in statistics that I doctored myself.” Winston Churchill

Data analytics, or data analysis , is the process of screening, cleaning, transforming, and modeling data with the objective of discovering useful information, suggesting conclusions, and supporting problem solving as well as decision making. There are multiple approaches, including a variety of techniques and tools used for data analytics. Data analytics finds applications in many different environments. As such, it usually covers two steps, graphical analysis and statistical analysis. The selection of tools for a given data analytics task depends on the overall objective, the source and types of data given.

Above all, Data Analytics, as part of Data Science, marks the foundation of all disciplines that are part of Artificial Intelligence (AI).

Objectives of Data Analytics

The objective of the data analytics task can be to screen or inspect the data in order to find out whether the data fulfils certain requirements. These requirements can be a certain distribution, a certain homogeneity of the dataset (no outliers) or just the behaviour of the data under certain stratification conditions (using demographics).

More often than not, another objective would be the analysis of data, in particular survey data , to determine the reliability of the survey instrument used to the collect data. Cronbach’s Alpha is often applied to perform this task. Cronbach’s Alpha determines whether survey items (questions/statements) that belong to the same factor are really behaving in a similar way, i.e. showing the same characteristic as other items in that factor. Testing reliability of a survey instrument is a prerequisite for further analysis using the dataset in question.

Data Preparation Before Data Analysis

Often enough, data is not ready for analysis. This can be due to a data collection format that is not in sync with subsequent analysis tools. This can also be due to a distribution that makes it harder to analyse the data. Hence, reorganising , standardising or transforming  (to normal distribution) the dataset might be necessary.

Data Analytics with Descriptive Statistics

Descriptive Statistics includes a set of tools that is used to quantitatively describe a set of data. It usually indicates central tendency, variability, minimum, maximum as well as distribution and deviation from this distribution ( kurtosis ,  skewness ). Descriptive statistics might also highlight potential outliers for further inspection and action.

Data Analytics with Predictive Statistics

In contrast to descriptive statistics characterising a certain given set of data, inferential statistics uses a subset of the population, a sample, to draw conclusions regarding the population. The inherent risk depends on the required confidence level, confidence interval and the sample size at hand as well as the variation in the data set. Hence, the test result indicates this risk.

Data Analytics with Factor Analysis

Factor Analysis helps determine clusters in datasets, i.e. it finds empirical variables that show a similar variability. These variables may therefore construct the same factor. A factor is a dependent, unobserved variable that includes multiple observed variables in the same cluster. Under certain circumstances, this can lead to a reduction of observed variables and hence the increase of sample size in the remaining unobserved variables (factors). So, both outcomes improve the power of subsequent statistical analysis of the data.

Factor analysis can use different approaches to pursue a multitude of objectives. Exploratory factor analysis  (EFA) is used to identify complex interrelationships among items and determine clusters/factors whilst there is no predetermination of factors.  Confirmatory factor analysis  (CFA) is used to test the hypothesis that the items are associated with specific factors. In this case, factors are predetermined before the analysis.

Data Analytics For Problem Solving

Data analytics can be helpful in problem solving by establishing the significance of the relationship between problems (Y) and potential root causes (X). As a result, a large variety of tools is available. The selection of tools for a given data analytics task depends on the overall objective, the source and types of data. Discrete data, such as counts or attributes require different tools than continuous data, such as measurements. Whilst continuous data are transformable into discrete data for decision making, this process is irreversible.

Depending on the data in X and Y, regression analysis or hypothesis testing will be used to answer the question whether there is a relationship between problem and alleged root cause. These tools do not take away the decision, they rather tell the risk for a certain conclusion. The decision is still to be made by the process owner ( example ).

Analytics was never intended to replace intuition, but to supplement it instead. Stuart Farrand, Data Scientist at Pivotal Insight

Applications for data analytics are evident in all private and public organisations without limits. For example, already some decades ago, companies like Motorola and General Electric discovered the power of data analytics and made this the core of their Six Sigma movement. As a result, these organisations made sure, that problem solving is based on data and applied data analytics wherever appropriate. Nowadays, data analytics or data science is vital part of problem solving and most Lean Six Sigma projects. So, Six Sigma Black Belts are usually well-versed in this kind of data analysis and make good candidates for a Data Scientist career track.

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Data at Work: 3 Real-World Problems Solved by Data Science

By Patrick Smith

At first glance, data science seems to be just another business buzzword — something abstract and ill-defined. While data can, in fact, be both of these things, it’s anything but a buzzword. Data science and its applications have been steadily changing the way we do business and live our day-to-day lives — and considering that 90% of all of the world’s data has been created in the past few years, there’s a lot of growth ahead of this exciting field.

While traditional statistics and data analysis have always focused on using data to explain and predict, data science takes this further . It uses data to learn — constructing algorithms and programs that collect from various sources and apply hybrids of mathematical and computer science methods to derive deeper actionable insights. Whereas traditional analysis uses structured data sets, data science dares to ask further questions, looking at unstructured “big data” derived from millions of sources and nontraditional mediums such as text, video, and images. This allows companies to make better decisions based on its customer data.

So how is this all manifesting in the market? Here, we look at three real-world examples of how data science drives business innovation across various industries and solves complex problems.

AirBnB uses data science and advanced analytics to help renters set their prices.

The vacation broker Airbnb has always been a business informed by data. From understanding the demographics of renters to predicting availability and prices, Airbnb is a prime example of how the tech industry is leveraging data science. In fact, the company even has  an entire section of its blog dedicated to the groundbreaking work its data team is doing. The team understands the importance of data quality, data mining, and data analytics.

Faced with a large amount of data from customers, hosts, locations, and demand for rentals, Airbnb went about using data science to create a dynamic pricing system called Aerosolve, which has since been released as an open-source resource.

Using a machine learning algorithm, Aerosolve’s predictive model takes the optimal price for a rental based on its location, time of year, and a variety of other attributes. For Airbnb hosts, it revolutionized how rental owners can best set their prices in the market and maximize returns. And that’s not all — Airbnb’s data scientists have also recently launched Airflow , an open source workflow management platform for building data pipelines to ingest data easily.

There’s no shortage of need for these solutions, and for the foreseeable future, we’ll be seeing explosive growth in data science solutions for technology companies like Airbnb

Data science revolutionizes sports analytics.

After the 2003 book Moneyball (and corresponding 2011 film) became successful, sports teams have realized that their data is more powerful than they had ever imagined. Over the past few years, the Strategic Innovations Group at the consulting firm Booz Allen Hamilton has been doing just that — working to transform the way teams utilize data.

Using data science and machine learning tactics, Booz Allen’s team developed an application for MLB coaches to predict any pitcher’s throw with up to 75% accuracy, changing the way that teams prepare for a game. Looking at all pitchers who had thrown more than 1,000 pitches, the team developed a model that considers current at-bat statistics, in-game situations, and generic pitching measures to predict the next pitch.

Now, before a game starts, a coach can analyze an opposing team’s lineup and run predictive models to anticipate how to structure his plays to add capability for his team and change how the game itself is played.

Nonprofits solve the most pressing social issues with data.

Founded in 2014, San Francisco-based Bayes Impact is a group of experienced data scientists assisting nonprofits in tackling some of the world’s heaviest data challenges. Since it’s founding, Bayes has helped the U.S. Department of Health make better matches between organ donors and those who need transplants, worked with the Michael J. Fox Foundation to develop better data science methods for Parkinson’s research, and created methods to help detect fraud in microfinance. Bayes is also developing a model to help the City of San Francisco harness data science to optimize essential services like emergency response rates. Through organizations like Bayes, data science has the power to make a significant social impact in our data-driven world.

So, what does all of this mean for the job market? With the ever-increasing need for data-driven solutions across every industry, the demand for data scientists has outpaced supply. According to a recent study by McKinsey , “By 2018, the United States will face a shortage of up to 190,000 data scientists with advanced training in statistics and machine learning as well as 1.5 million managers and analysts with enough proficiency in statistics to use big data effectively.”

It’s no wonder, then, that data scientists are one of the few non-managerial positions included by Glassdoor in the top 25 highest-paying jobs in America . Plus, in their annual list of the 25 Best Jobs in America , Glassdoor rated data scientists as No. 1 one due to the high median base salary, a number of openings, and career opportunity.

Two things are certain: There is a serious need for data scientists in today’s job market, and no shortage of life-changing problems that data wranglers can solve.

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Precision Problem Solving: Topological Data Analysis Driving Advances in Medicine and Biology

Mathematician Chad Giusti spoke with MAA FOCUS, the news magazine of the Mathematical Association of America.

Chad Giusti is an assistant professor of mathematics at Oregon State University. He works in pure and applied topology, with applications principally in neuroscience and complex systems. His work has appeared in journals such as PNAS and Crelle’s and has been supported by the NSF, AFOSR, and AFRL. Here, we learn about the fascinating work Chad has done in applying the tools of topological data analysis to problems in medicine and biology.

1. You are an expert in topological data analysis (TDA), a field that many people in our community are unfamiliar with. How would you describe TDA to someone who just finished the calculus sequence? How would you describe TDA to someone who has taken a standard introductory course in topology?

The usual quip is that topological data analysis characterizes complex systems or data in terms of qualitative notions of “shape.” I think this is at the same time too vague and too specific.

Calculus students are adept at describing shape in qualitative ways. A common exercise is to read off various information about a polynomial by looking at its graph or the graph of its derivative. By counting extrema and roots, examining behavior “at the ends,” and so on, we can determine things like the minimum possible degree, sign of the leading coefficient, and so on. While these are, in principle, numeric answers, they aren’t exact measurements—they’re bounds and ranges of possible values. Even if I only provide a scattering of points on the graph of the polynomial, it’s not much harder to provide the same data about the underlying polynomial.

For students, I would say that topology, particularly algebraic topology, provides a set of mathematical tools for a similarly qualitative characterization of more complex shapes: surfaces and higher dimensional analogues called manifolds, and more abstract structures like graphs. We most commonly formalize “qualitative” as meaning “up to continuous deformation” – stretching or compressing, without cutting or gluing. A circle remains a circle, topologically, even if we stretch it into a wiggly mess as we might do with a rope, so long as we don’t cut it open into a long strand or glue distant points together. This flexibility reduces the specificity of what we can say about systems, but it makes these descriptors more applicable in the presence of noise or incomplete data, both of which are particularly pernicious in biological and medical applications.

Students in an undergraduate topology course might not recognize much of what we do in TDA immediately. However, many will have seen the fundamental group of a topological space, or the topological classification of smooth surfaces, which are cousins of the kind of measurements and classifications we employ when studying “shape” in applications. However, data is rarely given to us in the form of a topological space—we must build approximations of our spaces from things like finite collections of points sampled on (or noisily near) a surface we want to study.

Currently, the most common tool used in TDA is called persistent homology, which characterizes how qualitative features of a shape evolve as some parameter changes. The parameter can be a measure of size (“how big are the features”), time (“when do the features appear”), or something more esoteric and domain dependent. Persistent homology gives us a collection of vector spaces associated to the space, much like the fundamental group gives us a group. By comparing these vector spaces across different data sets—results of some experiment under different conditions, for example—we can use the similarity or differences between the evolution of features to reason about how the underlying systems compare. Differences in shape can point to differences in organization in a complex system. For example, neural activity that encodes the head direction of a mouse is well-described by a circle, but that which describes the head direction of a bat generally requires a shape that can encode three dimensions of motion. (In fact, experimentally it appears to be a torus, not a sphere!)

Image of a MAA FOCUS magazine article.

Image of Chad Giusti's MAA FOCUS magazine article.

2. You apply TDA to current problems and systems that arise in biology and medicine. Can you elaborate more on those applications and what got you interested in pursuing them?

When I think about my applied work, I usually place it in the field of theoretical neuroscience, in the context of developing a theory of how neural populations encode information and perform computations. It turns out that many of the models that neuroscientists have developed to describe these phenomena “look” topological in the sense that it’s easy (for an applied topologist like me) to imagine formalizing them using language from TDA.

In fact, this is how I first got started in the area. As a graduate student, I worked in pure algebraic and geometric topology studying spaces of knots, though my projects always had a computational bent. One year on the job market, I had two offers: one to go to Belgium and work on this very theoretical type of mathematics, and another to go to Lincoln, Nebraska and try to apply topology to the study of neural codes. The PIs on that project, Vladimir Itskov and Carina Curto, showed me some pictures of place fields, which diagram how individual neurons in the hippocampus respond to an animal’s location in its environment.

These look a great deal like the topological notion of a “cover” of a space, which is one of our fundamental tools for studying shape. Their notion, which turned out to be an excellent one, was that we should be able to use tools from TDA to study this structure in neural activity, providing a platform for mathematically formalizing some of these informal models. The idea of developing an entirely new way of studying how the brain works—and doing it using all of the abstract math I’d fallen in love with in graduate school—was a very compelling offer.

I think it’s important to note that, as compelling as the offer was, pursuing this route was a risky decision. Novel applications of mathematics, particularly areas of math that aren’t well established for applications, very often don’t gain traction or take many years to do so, and a postdoc project that doesn’t go anywhere usually doesn’t lead to further employment. I had the privilege to be able to take that risk in large part because I had a strong economic and personal support system, including skills that would allow me to seek alternative employment if the project didn’t work out. It would behoove us to provide more support to early career academics so it’s easier to take these big risks.

Lastly, I should note that my own narrow conception of my work is not exactly accurate: I’ve done or supervised projects in human neuroscience/neurology, physics of granular media, plant/pollinator networks, collective behavior of swarms, and elsewhere. I’m currently working with researchers on problems in climate science and cancer genetics. I suppose the point is that it doesn’t take a lot of persuasion to get me interested in a good problem.

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Heavy Machinery Meets AI

  • Vijay Govindarajan
  • Venkat Venkatraman

solving problems through data analytics

Until recently most incumbent industrial companies didn’t use highly advanced software in their products. But now the sector’s leaders have begun applying generative AI and machine learning to all kinds of data—including text, 3D images, video, and sound—to create complex, innovative designs and solve customer problems with unprecedented speed.

Success involves much more than installing computers in products, however. It requires fusion strategies, which join what manufacturers do best—creating physical products—with what digital firms do best: mining giant data sets for critical insights. There are four kinds of fusion strategies: Fusion products, like smart glass, are designed from scratch to collect and leverage information on product use in real time. Fusion services, like Rolls-Royce’s service for increasing the fuel efficiency of aircraft, deliver immediate customized recommendations from AI. Fusion systems, like Honeywell’s for building management, integrate machines from multiple suppliers in ways that enhance them all. And fusion solutions, such as Deere’s for increasing yields for farmers, combine products, services, and systems with partner companies’ innovations in ways that greatly improve customers’ performance.

Combining digital and analog machines will upend industrial companies.

Idea in Brief

The problem.

Until recently most incumbent industrial companies didn’t use the most advanced software in their products. But competitors that can extract complex designs, insights, and trends using generative AI have emerged to challenge them.

The Solution

Industrial companies must develop strategies that fuse what they do best—creating physical products—with what digital companies do best: using data and AI to parse enormous, interconnected data sets and develop innovative insights.

The Changes Required

Companies will have to reimagine analog products and services as digitally enabled offerings, learn to create new value from data generated by the combination of physical and digital assets, and partner with other companies to create ecosystems with an unwavering focus on helping customers solve problems.

For more than 187 years, Deere & Company has simplified farmwork. From the advent of the first self-scouring plow, in 1837, to the launch of its first fully self-driving tractor, in 2022, the company has built advanced industrial technology. The See & Spray is an excellent contemporary example. The automated weed killer features a self-propelled, 120-foot carbon-fiber boom lined with 36 cameras capable of scanning 2,100 square feet per second. Powered by 10 onboard vision-processing units handling almost four gigabytes of data per second, the system uses AI and deep learning to distinguish crops from weeds. Once a weed is identified, a command is sent to spray and kill it. The machine moves through a field at 12 miles per hour without stopping. Manual labor would be more expensive, more time-consuming, and less reliable than the See & Spray. By fusing computer hardware and software with industrial machinery, it has helped farmers decrease their use of herbicide by more than two-thirds and exponentially increase productivity.

  • Vijay Govindarajan is the Coxe Distinguished Professor at Dartmouth College’s Tuck School of Business, an executive fellow at Harvard Business School, and faculty partner at the Silicon Valley incubator Mach 49. He is a New York Times and Wall Street Journal bestselling author. His latest book is Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future . His Harvard Business Review articles “ Engineering Reverse Innovations ” and “ Stop the Innovation Wars ” won McKinsey Awards for best article published in HBR. His HBR articles “ How GE Is Disrupting Itself ” and “ The CEO’s Role in Business Model Reinvention ” are HBR all-time top-50 bestsellers. Follow him on LinkedIn . vgovindarajan
  • Venkat Venkatraman is the David J. McGrath Professor at Boston University’s Questrom School of Business, where he is a member of both the information systems and strategy and innovation departments. His current research focuses on how companies develop winning digital strategies. His latest book is Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future.  Follow him on LinkedIn . NVenkatraman

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Electrical Engineering and Systems Science > Image and Video Processing

Title: robustness and exploration of variational and machine learning approaches to inverse problems: an overview.

Abstract: This paper attempts to provide an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning. A special focus lies on point estimators and their robustness against adversarial perturbations. In this context results of numerical experiments for a one-dimensional toy problem are provided, showing the robustness of different approaches and empirically verifying theoretical guarantees. Another focus of this review is the exploration of the subspace of data consistent solutions through explicit guidance to satisfy specific semantic or textural properties.

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solving problems through data analytics

ANALYSIS: Can AI Solve ESG’s Data Problem?

By Abigail Gampher Takacs

Abigail Gampher Takacs

Artificial intelligence disclosures to the SEC are on the rise . The references to AI are many and varied; some companies use it to cut costs, while others discuss AI’s risks.

A closer look at SEC filings in the context of ESG shows how AI could affect ESG data collection and disclosure. This is important because stakeholders are increasingly requesting that companies publish ESG data—including everything from information on their workforce demographics to their carbon footprint.

However, the disclosure of accurate and comparable ESG data has hit a major roadblock : Getting ESG data ready for public disclosure requires companies ...

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