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TDWI Upside - Where Data Means Business

Why Analysts Must Move Beyond Data Literacy and Focus on Business

To get the most from your analytics, stop focusing on business-user data literacy and instead consider BI tools that increase business literacy in analytics.

If "data-driven" was the buzzword of the last decade, "data literacy" is making a play for the title in the 2020s. Everyone wants to be data-driven -- to make decisions based on relevant information instead of relying on intuition. Companies that are turning entire industries on their heads are data-driven, which (accurately) implies a mastery of data, so data literacy is important.

For Further Reading:

5 Ways to Make Better Business Decisions with CX

Five Characteristics of a Data-Driven Company

The Secret to Organization Data Science Success: Data Literacy

However, "data literacy" is often used in the business intelligence (BI) space in the context of users' alleged lack of mastery over data. It's used in appeals for users to learn how to read, understand, work with, and analyze more data so they can finally put their BI tools to work.

What if the problem isn't users' lack of data literacy but rather a need for analysts and developers to be more business literate in the tools they provide?

Arguably, shifting the "literacy" burden to the data analysts who create BI and analytics content rather than to business users who make the decisions makes more sense. There's a case to be made that users' data literacy is more than sufficient — they know which performance numbers are important, and if they could easily access that data and apply it to their daily jobs, they would.

The problem is that all too often, users don't apply data to business decisions because they either can't get their hands on the data they need or receive it without the context necessary to interpret it. Analysts may be too busy to respond to their requests for new reports and analysis in a timely manner. Analysts without business training may simply deliver the minimal amount of data without the foresight to dig deeper or provide the context to changes in trends.

Answers in Context

Users are looking not only for data but also for context around that data, which generates insights. That's where the real value is -- it's not just the raw data but the context that tells a story and creates "aha moments" that give users an opportunity to address issues and improve performance. The information also has to be timely and relevant or business users won't derive any value from it.

Think about the process that exists at many enterprises. A user needs an answer to an urgent business question -- for a simple example, the user wants to understand why there was a drop in sales on a particular date. The user has the raw data but needs the context around it to understand it and make decisions accordingly. A request for analysis can often take weeks or months, which isn't timely enough.

To flesh out that scenario and illustrate the dilemma, think of a business user monitoring sandal sales in Europe who notices a sharp drop in sales in France that is outside the historical norm for that time of year. Without context, the cause for the drop is unknown, and decision-making would be based on intuition and guesswork.

A data analyst who receives a request to explain the drop might overlay internal data from other company divisions and publicly available information, such as current and historical precipitation and temperatures. The analysis might suggest that unusually wet and chilly weather patterns depressed sandal sales and caused an increase in the sale of boots -- that's context that can drive data-driven decisions.

It takes too long to get this contextual information to business users at most companies. What users need is context built right into their BI tool. It could be the ability to overlay other data that provides context (for example, weather information, travel data, or competitor intelligence). The ability to collaborate with colleagues who can contribute context could also be incredibly valuable for telling the full story around data anomalies.

Data-driven businesspeople need to know more than just "sales rose 11 percent in the second quarter." They need to understand why it happened, which requires drilling down into the numbers to get the full story. This analytical approach fosters a broader data culture that allows business users to make more strategic decisions overall.

Business Literacy and Context

To get to that state, data analysts who conduct analysis and build BI and analytics content such as dashboards and reports will need to deliver data that is timely and relevant and also provide context around it in a format that is easy for the business user to consume. That will require not only data literacy and tool expertise, but also an in-depth understanding of how business users interpret and apply data on the ground level.

BI technology can be the bridge between data analysts and business users. Analysts understand the datasets that are relevant to their industry. What too many don't know is how to communicate the story the data tells in a way that business users can understand and apply, which is a business literacy problem. A BI tool that surfaces relevant insight and enables collaboration among business users can be the solution.

Company leaders who are looking to ensure that frontline decisions are driven by data should consider thinking beyond "data literacy." The problem isn't that business users lack data literacy; it's that the dashboards and reports they have to make data-driven decisions are too often created by people who lack business literacy. Solve that problem and the data-driven decision-making issue takes care of itself.

About the Author

Glen Rabie is the CEO and co-founder of Yellowfin, a global analytics and BI software vendor. Prior to founding Yellowfin, Glen worked for National Australia Bank in multiple roles, including senior business consultant and global manager. It was here that he learned the value of enterprise data and developed his passion for data analysis. Glen enjoys the challenge of bringing new products to market and competing with the world’s best. Most of all, he is proud of the team he’s built -- their passion and seeing the difference that the Yellowfin team has made to the analytics industry. You can reach the author on Twitter or LinkedIn.

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