What is data literacy?
Data literacy isn’t all that different from literacy in any other sense. A data literate individual understands what has been communicated in the data they are reading, and they know how to communicate that data in the context of their own work. This definition does not just apply to database engineers and data analysts.
An MIT study on Data Driven Decision Making showed that firms who adopt a policy of data informed decisions have output and productivity that is 5-6% higher than what would have been expected given their other investments and information technology usage. One of the leading data platforms, Qlik, did a study on data literacy that dove even deeper into how data literacy improved data-driven decision making. They found that data literate enterprises experience an increase in enterprise value by 3-5% following their literacy efforts.
Who needs to be data literate at your organization?
Anyone in your organization who is reading reports or relies on data to inform their decisions ought to be data literate. For most organizations, this includes almost every person on staff. While the extent of data literacy required may vary, there is value in providing appropriate data literacy training for all those who interact with data. For example, when talking about digital analytics this includes those on your teams generating content or making decisions about marketing, web or mobile app design, and social media to name a few. With that definition in mind, how confident are you that your team members are data literate?
What is poor data literacy costing you?
Poor data literacy can cost you time and money across the organization.
If teams producing data insights have poor data literacy, they will generate inconsistent reports. When reports are not in sync because of different interpretations of how to read or communicate the data, leadership can lose confidence. I have seen key stakeholders shy away from data driven decisions because of these inconsistencies. When different teams tell different stories with their data, decision makers can fall victim to confirmation bias and lean toward the data that most closely aligns with their existing assumptions. When this happens, the time and effort invested in analysis is lost. If no one trusts the data, they will just continue to make the same decisions they would have without it.
What does this look like in an organization?
Many organizations find themselves stuck in a cycle of regular extensive meetings with both staff and leadership discussing, explaining, and auditing data. Does this sound familiar to you? Ten to twenty people in a conference room trying to determine what actually happened with their customers last quarter – or possibly the quarter before last. Since teams are reading the data in different ways, the meeting may end with a smaller group charged with auditing the reports and determining where the objective truth actually lies. This is a common pattern across organizations of all sizes. What was scheduled as a one-hour meeting can consume ten to twenty hours. Additionally, the cycle of explanation and auditing has a direct impact on the number of full-time employees required to produce data informed decisions.
When there is a data literacy gap between analysts and decision makers, it becomes more difficult for decision makers to ask new questions and inform future data collection and analysis. As an example, most organizations collect web data, but it is rarely understood or leveraged well. Web analysis that is clear and readily understood by decision makers and stakeholders will naturally spark questions that can lead to new insights. If you have a data literacy gap in any part of the team responsible for analyzing data or leveraging data in decision making, you will be missing out on an opportunity to ask targeted questions that can focus your digital efforts for greatest impact. Without targeted analysis, your digital efforts are a hit or miss game, and it is difficult to say which efforts will lead to a real ROI. Potential improvements may pile up in your backlog with no clear guidance as to which change has a real opportunity to improve your customer experience.
How can you improve your organization’s data literacy?
1. Unify the way data is collected and organized.
Having a cross-organizational team responsible for creating and documenting a shared understanding of how data is gathered and key metrics are defined within your organization can ensure alignment across teams. When confidence in the data is restored, teams can focus more on insights and exploration and pay less attention to methodology or whose numbers are more accurate.
Not only does this help surface duplicative work that can be eliminated, but it also reduces the need for auditing. Decision makers see a single story and their confidence in the value of the data improves.
With shared definitions across all teams reports can include a unified glossary, reducing the amount of time spent in meetings explaining exactly what is captured by each metric.
2. Make sure everyone in the organization is speaking the same language.
Once analysts and decision makers are aligned on how the data is used and read across the organization, everyone will start speaking the same language. This will have a significant impact across all levels of the organization, from senior leaders to individual contributors. I have seen clients consolidate and repurpose roles when the amount of time spent explaining and auditing data goes down, freeing people up to focus on more impactful efforts.
3. Foster collaboration and insights.
Finally, when both analysts and leadership are data literate, they will have better conversations that lead to insights beyond standard reporting of your key performance indicators. Further analysis can inform the next iteration of your data strategy as you discover additional questions that your current data collection strategy cannot answer. Sometimes, this will force you to correct the way you gather and report on your data. As new questions arise, data literacy will have a positive impact on both your data strategy and your data quality.
There is no shortcut to becoming a data literate enterprise. Collaboration and continued education are the pillars that support your data literacy effort. The value of this investment is clear.
Data literacy is important for everyone who works with or uses your data.
Data literacy saves time, reduces overlap, and can increase your overall enterprise value.
Targeted questions stemming from data literacy can lead to improved data quality and can help iterate on your data strategy.
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