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What are data insights?

April 27, 2020
· 4 min read

This article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. Find out more about DataRobot MLOps here.

Data insights are knowledge that a company gains from analyzing sets of information pertaining to a given topic or situation. Analysis of this information provides insights that help businesses make informed decisions and reduces the risk that comes with trial-and-error testing methods. 

In the digital world we live in, there are copious amounts of data at our fingertips. But though anyone can access raw data, the ability to extract valuable and actionable information from the numbers is what will determine whether you can generate a competitive advantage for your business.

What is the difference between data and insights?

Many people think of data and insights as synonymous, but there are subtle, yet important distinctions between these two terms. Data are information, generally sets of numbers or text. Insights are the knowledge gained through analyzing data, generating conclusions from the data that can benefit your business. Data are the input. Insights are the output.

Input and output

What is an example of an insight?

Insights can vary greatly between industries. Your ability to derive actionable data insights is dependent on a variety of factors. Some of these might include the quantity of data available to you, the parameters of a given dataset, and the specific problem that you are trying to solve. What’s important is establishing which metrics provide valuable information for your particular industry, and leveraging that information to give you an edge in the market.

Data insights might include:

  • Knowing the optimum number of team members to schedule at your store by looking at previous trends in shopping traffic.
  • Noticing that certain weekends of the year are in higher demand for your product or service, allowing you to raise prices during these times.
  • Monitoring your company’s online advertising performance data to determine which campaigns, audiences, and ads are effective and which are not.

What makes a data insight actionable?

Actionable data insights share many distinguishing characteristics. Generally speaking, insights are basic observations that may not provide a direction or course of action. Actionable insights include an action you will take due to the insight you gained. This provides a blueprint for creating greater value for consumers, as well as increased efficiency for your business. Below is a list of attributes to consider when deciding whether or not your derived insight is actionable.

What makes a data insight actionable?


Many insights can be extracted from a single dataset. However, this gained information is useless if the individual insights are not relevant to the problem you are trying to solve. Selecting measures that are relevant to your business situation is crucial in generating effective actionable insights.


For data analysis to mean anything, there must first be some level of baseline information to understand how the status quo stacks up to your current data. When looking for actionable insights, it is important to know the historical context of the metrics you are viewing. What range of values would be considered “normal” for a given metric? At what point would a piece of data be concerning? Putting your data into context will help you answer these questions and provide greater value.


Complete and detailed insights have a higher likelihood of being actionable. Truly actionable insights are specific and well thought through. In addition, greater specificity will hold more weight when attempting to convince managers, stakeholders, and colleagues that the insight you’ve gathered can genuinely make a difference. An insight must be specific enough to not only tell you what has occurred, but also why it occurred.


Taking specificity a step further, clarity helps an insight stand out among the rest. Generating highly detailed, specific information will go a long way in establishing an actionable insight. However, the ability to communicate this insight in a manner that is easy to understand is vital to get others on the same page. Proper messaging and data visualization will help provide clarity for those you are presenting to, lessening skepticism surrounding your actionable idea.


Even if an insight abides by all of the previously mentioned characteristics, it must still pass the alignment test. Does your insight align with your company goals? For instance, insights that revolve around your Key Performance Indicators (KPIs) will inherently match your business strategy, but relevant metrics aren’t limited to KPIs alone. Generating insights that fit your company and brand will help those around you relate and feel passionate about what information is being extracted from the data.

How do you find data insights?

Data insights can be found by defining the problem you’re looking to solve, measuring and analyzing the metrics that are relevant to this problem, making improvements based on your findings, and then monitoring the effects that these improvements have on your KPIs.

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About the author

Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

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