Getting Started with Data Literacy: Two Tips for Success
Independent consultant and popular TDWI conference speaker David Langer explains the two steps you can follow to get your data literacy program off on the right foot.
- By Upside Staff
- June 29, 2023
David Langer has worked in the tech industry for over 25 years. Starting as a software engineer and coder, he moved into analytics, starting with traditional data warehousing and BI. Over time, he moved into more advanced analytics, including machine learning and cluster analysis. Today he works as an independent consultant and educator, working with clients to plan, organize, and execute their data literacy programs, and building and deploying production machine learning models for clients. He spoke recently with “Speaking of Data” host Andrew Miller about how to successfully launch a data literacy program.
After transitioning to analytics work directly with business decision makers, business leaders, and executives, he learned that data literacy matters more than anything else. “For most organizations, the fundamentals are where the problems are. Decisions are not being made using data. They're being made using experience and gut instinct alone, and it’s just not enough.” He admits that as persuasive as he could be, he repeatedly wasn’t successful in changing the course of the organization because of a lack of data literacy. As a result, he developed these two tips.
Tip #1: Getting started requires executive commitment
How should an enterprise get started? Langer says he “came to the inescapable conclusion that data literacy must start with leaders. Data literacy isn't just for the rank-and-file.” As a litmus test when he starts talking to organizations, he asks about their leader's commitment to data literacy. “I ask them, ‘Is your organization willing to send your leaders to training -- managers, executives, the C-suite, all of them?’ If not, which is often the case, that probably tells you everything that you need to know, because data literacy is very much a cultural transformation. If your leaders aren't all in, then there's almost no point in getting started, to be frank. If employees see their managers not exhibiting a data literacy mindset and data literacy behaviors, they will revert to business as usual.”
Langer admits to receiving pushback; executives wonder if data literacy is needed because newer technology such as no-code/low-code or generative AI already make it easier to gain insights.
“Generative AI is all the rage these days. Those models have long-term promise, but right now, 80 to 90% of advanced analytics initiatives fail. Machine learning initiatives fail, and generative AI is just another form of machine learning and advanced analytics.” Likewise, low-code/no-code solutions -- which, he hastens to add, aren’t new -- are powerful and useful tools, but they are no replacement for having data literacy concepts permeated throughout the organization.
“There's no substitute for data literacy because it allows the organization to exhibit this mindset each and every day independent of the tool. It's a way of thinking. It's a way of behaving and acting. There's no substitute for a data-literate marketing manager using Excel and making data-driven decisions.”
Tip #2: Choose the right tool from the start
Other pushback comes from executives asking why he doesn’t talk more about Python or R. Suggesting Excel as a good beginner’s tool seems like blasphemy, according to Langer. “If you think about data literacy, you think about tooling because you must have some tooling to work with the data. What tools should an enterprise use?” The simple answer is that a tool should have wide-scale adoption, “so you might as well start by focusing on a tool that people already know and are comfortable with, rather than saying, ‘Oh, you want to be data literate, you're going to have to learn how to code in Python.’ That's just not going to happen.”
Langer says an enterprise can stack the odds in its favor by concentrating on Excel first and foremost, but scale isn’t the only point in Excel’s favor. “It’s an exceedingly powerful analytical tool. With the right techniques, some professionals might find they don't need anything else to do all the analyses that could help them become truly data-driven.”
Among its power, Langer says, is that “you can actually build predictive models in Microsoft Excel, and these predictive models are of the exact same quality. They come up with the same exact results as R or Python. Most people don't know that.” From there workers can take their knowledge of Excel and easily move into R; Langer says it’s a “pretty easy” transition.
ChatGPT and the Future of Data Literacy
Imagine asking ChatGPT to build a PivotTable and point out what’s interesting about the results. Langer sees that as a possibility, but for now, organizations need to address concerns about loading proprietary data into the AI engine. Enterprises should “make sure you're implementing all your data privacy and data security guidelines before you start using AI. We'll get there eventually -- a sandbox environment where you can safely use your proprietary data with ChatGPT. We're not quite there yet, but it'll happen.”
[Editor’s notes: Quotations have been edited for clarity and length.
You can listen to the entire conversation, including a discussion of best practices for data analytics, here.
Langer is a hands-on consultant, trainer, and speaker with a mission to make data analysis skills as commonplace as Excel skills. His company, Dave on Data, was founded to achieve this mission by crafting and delivering the best analytics training for all professionals regardless of role.
He will be leading a session about applied analytics at the TDWI Virtual Summit:Solving Business Problems with Analytics (July 12-13, 2023) and a discussion about getting started with machine learning at TDWI’s San Diego Executive Summit for Analytics (August 6-11, 2023). He also teaches a class on getting started with Python and a session focusing on predictive analytics using Excel as part of TDWI’s online course curriculum.]