STATEMENT: In order to make data discovery and analysis easy for all, we have to democratize data and make it accessible for all.
More than likely, you’ve heard something along the lines of that before. Great in theory, but what good is accessible data for all to readily use if everyone can’t actually understand it?
‘Easy-to-use’ doesn’t always translate into ‘easy-to-understand’.
So how do you go about making data more accessible to use, while simultaneously making it more accessible to understand? It starts with data literacy.
Data literacy is an increasingly popular term floating around within business intelligence—and rightfully so. In order to keep pace with the rapid growth of data and increase in BI tools, it’s critical to make data literacy a top priority for your organization. To make data literacy a reality, we’ve created a natural language generation (NLG) platform that generates automated written analytics directly inside your data visualization tools.
When data and insights are presented within your company, how can NLG help employees become data literate? Here are four key stages of data understanding and how NLG can lead the charge in achieving company-wide data literacy bliss.
1. Familiarity of Data
First things first, what are we looking at? In this initial phase of data understanding we get familiar with our datasets by learning the metrics, how they are calculated, and possibly how we collected them or what group they represent.
One major benefit of NLG is that it creates analytics presented in natural language, also referred to as “written analytics,” that are unambiguous. Even data novices can read the language that NLG produces to automatically explain a dashboard. Oftentimes, non-data experts are too reluctant to even log in and engage with a dashboard because of this initial step. NLG can help solve this problem by providing insights that are in an easy-to-understand format, giving you a jumpstart on understanding your data. Request a demo and see how NLG works within Phase 1: Familiarity of Data »
2. Contextual Understanding of Data
Now that we understand what we’re looking at, it’s time to move onto the next phase and get a contextual understanding of the data. When we approach data, we need to keep in mind that everyone has a different perspective and thus requires different insights. How does the data relate to your position and strategic goals of the company?
We understand different business colleagues have different roles within different departments, so we configured our NLG platform with the ability to customize the narrative for each team or person’s perspective. NLG can teach each person in the frame that makes the most sense for them, delivering role-based insights that are relevant to org structures, job functions, and individual goals. Request a demo and see how NLG works within Phase 2: Contextual Understanding of Data »
Natural language generation (NLG) empowers you to be data literate without having to be a data expert.
3. Interpretation of Data
You’re comfortable with the data and have a strong grasp of what it means to you. So, now what? In this next phase of data understanding, it’s time to do something about it. One should 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 couple of steps. However, NLG can take it to the next level by showing you why things are happening, calling out key drivers affecting a certain metric. From there, NLG can provide insights that are prescriptive, telling people what they need to do about what they are seeing. Request a demo and see how NLG works within Phase 3: Interpretation of Data »
4. Confidence to Question Data
The final stage of achieving data literacy bliss is when people have the confidence to answer their own questions, thus becoming a self-service analyst (or commonly referred to as a citizen data scientists).
Once people experience the power of NLG over time, we teach them to be data literate and data explorers. This is the ultimate way to make advanced analytics easy and approachable: coach them through NLG to understand what they’re seeing, then coach them on how to start doing their own drill-down and analysis. Request a demo and see how NLG works within Phase 4: Confidence to Question Data »
In order to make good decisions and be successful, it used to be the case that we all needed to be business literate, but now we have to become data literate to make those same business decisions. If we want to empower everyone—from marketing and sales, to procurement and finance—to use data and make their own decisions, we can’t afford to leave anyone behind when it comes to data literacy.