Data Literacy: the ability to read, understand, creation, and communicate data as information, much like literacy is the ability to derive information from the written word.
In order to make data discovery and analysis easy for all, we have to democratize data and make it accessible for all.
The ideology of empowering everyone, regardless of their skill set, to gather and analyze data on their own sounds great in practice, and we hope to get there one day, but what good is accessible data for all to readily use if everyone cannot actually digest, understand, and then effectively talk about it? For us to turn citizen data scientists and self-serving data analysis into a more prevalent reality, we first have to solve the root of the problem: a lack of data literacy.
Do you know what your data is showing you? Are you asking the right questions? Can you analyze the data visualizations and pull out the right insights? Do you fully understand what trends are showing and how you should act on them?
According to Gartner, “By 2020, 50 percent of organizations will lack sufficient AI and data literacy skills to achieve business value.” If only a handful of people in your organization can understand what’s being illustrated and highlighted in data then it doesn’t matter how advanced your data teams are, how incredible you analytics are, or how pretty your visualizations are, immense business value is lost because it’s unable to be relayed up and across your enterprise.
Why Data Literacy is Crucial in Business Intelligence
Data literacy is the ability to interpret the data being presented—to understand what the data means, what trends it reveals, how to talk about it, and know what course of action to take. Making data literacy a top priority for your organization is crucial for implementing a successful BI strategy. Today’s modern business must champion data as a second language, they must get everyone on the same page. For today’s modern business, data isn’t an asset that’s “nice to have.” Reliance on data to drive all business decisions is a must. It’s a core function and team members must be able to communicate and understand basic analytics at the very least. Poor data literacy will be an inhibitor to growth over competitors who have already made it a core priority.
Rapid Growth of Data Collection and Analysis
Companies and enterprises are collecting more data than ever before, leading to a rapid adoption of advanced models and new business intelligence (BI) platforms in an effort to use it and act on it. We’ve entered into a data-driven era like no other; it’s rapidly growing, and people can’t afford to be left behind.
Bridging the Skills Gap
Not everyone is equipped with the skills to analyze data to make better decisions. There are programs to educate and train data scientists that are slowly making their way into schools, but we still have a way to go before supply catches up with demand which leaves organizations seeking to fill the gap in other ways. Many people and teams across an organization are now in positions where they interact with data on a regular basis. In order to bridge the skills gap and empower everyone to become their own self-serving analyst, we have to provide the ability to understand their data. We have to meet them where they’re at. A more proactive approach to bridging the skills gap is investing in new technology like NLG that simplifies the complexities of immense data sets and analytics for the average business user. NLG democratizes data, meaning it works across the entire organization. Not every team or department is a technical team, but they still have technical needs. If your BI tools don’t serve each department well, then it’s less likely it will be adopted and utilized. It must bridge the gap between regular people with minimal data expertise and highly-skilled data scientists.
More Powerful Data Storytelling
As the data revolution progresses and the need to improve data literacy consequently rises, using automated tooling that enables your company to scale data expertise will be essential.
Achieving Company-Wide Data Literacy
How do you make data more accessible while simultaneously making it easier to understand? To make company-wide data literacy a reality, you can utilize natural language generation (NLG) to generate automated written analytics directly inside your data visualization tools. This give you real-time data insights in a way that we’re innately created to understand – the written word. NLG allows you to simply read and digest using the power of data storytelling.
Let’s first take a look at four key stages of truly understanding your data:
Investing in easy-to-use BI data visualization tools like Tableau, MicroStrategy, Qlik, TIBCO Spotfire, and Power BI is a good first step in making data accessible, but to make your investments impactful throughout your enterprise you also have to invest in making those visualizations easy-to-understand. The end goal is for everyone in your organization to have the confidence to analyze data and make their own decisions. NLG can accomplish this in a fast, cost-effective, and scalable way, while freeing up the manual reporting and coaching your data teams are constantly tasked with. Here’s a detailed look at each of the four stages and how NLG can lead the charge in achieving company-wide data literacy.
Familiarity of Data
We become familiar with our datasets by learning what the metrics are, what they mean, how they’re calculated, and possibly how we collected them or what group they represent.
Often, non-data experts are reluctant to log in and engage with a dashboard because of this initial step. They’re overwhelmed by the sheer amount of data they need to process when they’re likely searching for just a few key insights. It’s intimidating, so why try? NLG helps solve this problem by giving users readable insights, so they don’t have to stress about trying to pick out points or misinterpreting the visualization. The heavy lifting is done for them. The written analysis is presented to them in a way they’ll understand and relate to.
Contextual Understanding of Data
It’s important to keep in mind that everyone has a different perspective, job function or responsibility, requiring different insights than their peers. How does the data relate to your position and strategic goals of the company? Different colleagues have different roles within different departments. NLG is able to customize the narrative for each team or individual’s perspective. It can relay personalized analytics to each person in the frame that makes the most sense for them through role-based insights.
Interpretation of Data
In this next phase of data understanding, it’s time to do something about the data. It’s critical to be able to decipher why things are playing out in a certain way and what course of action to take. Knowing what is happening with your data is a good first step in data literacy. However, NLG can take it to the next level by showing you why things are happening, calling out key drivers affecting a certain metric, diagnose any outlying data points, and outline actionable next steps—telling people what they need to do about what they are seeing.
Questioning of Data
The final stage of achieving data literacy is when teams have the confidence to answer their own questions, becoming a self-service analyst (or commonly referred to as a citizen data scientist). Once organizations harness the power of NLG, their entire workforces have the ability to become data literate data explorers. This is the ultimate way to make advanced analytic adoption seamless and approachable: utilize NLG to coach them through what they’re seeing then coach them on how to start doing their own drill-down analysis.
Data Literacy Must be a Priority
Gartner predicts “by 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms.” In order to make better decisions that drive success, we now have to become data literate to make those same business decisions where we used to just have to be business literate. If we want to empower everyone, from marketing and sales to procurement and finance, to use data and make their own smarter decisions, company-wide data literacy must be a priority.
Achieving or even simply improving data literacy can transform an organization from retroactive, rear-view thinking to becoming a leading, innovative, and intelligent enterprise.
Are you ready to become a champion for data literacy in your organization? Request a demo to see Wordsmith in action!