Six Essential Elements of Data Fluency
What is data fluency and how do we get it?
#1. Learn to identify and articulate outcome-centric goals.
Virtually all organization today use data, to some degree, in analyzing performance or charting the course ahead to either make money or save money. But this doesn’t mean they do so effectively. Only when data collection, queries, and analysis are aligned with outcome-centric goals does data really begin to add value. Without outcome-oriented parameters, analysts are sent on so-called ‘Fishing expeditions’ – where they are tasked with extracting value from the data sources they’ve identified, but are not given specific goals or hypotheses necessary to do so. Thus, an essential element of data fluency is learning to root data analytics in clear-cut business outcomes and goals.
#2. Familiarize team members with new data methodologies and approaches
One of the fundamentals of data fluency is recognizing that discrete or role-specific data use is not enough. For example, your marketing team members might be extremely comfortable with certain data collection and analysis methodologies, like surveys, focus groups, or social media learning. But in a data fluent organization team members should at least be conversant in a wide array of data methodologies, even those not necessarily suited to their division or role. Forward-thinking organizations will train teams to be conversant in at least 10-15 different methodologies, says Research Rockstar. Even when it comes to analyses they’ll never perform themselves, make sure team members understand enough about relevant methodologies to know what they are, when they might be appropriate, and what kind of insights they may provide.
#3. Foster a company-wide understanding of reliability
Another fundamental elements of data fluency is a basic understanding of data reliability. A data fluent team is one whose members can identify, without relying on experts, data that is reliable – that is, sufficiently complete and error-free to be convincing for its purpose and context. Having a basic understanding of data reliability is essential to data fluency for one simple reason: People don’t use data unless they trust it. Teaching members how to identify and evaluate for reliable data is a huge step toward them making data a part of their daily workflow.
#4. Learn to avoid ‘Report Proliferation’
The team at Juice Analytics warn against what they call “report proliferation” – where there’s simply just too many reports and detailed breakouts to be useful. In a world of ‘too long, didn’t read,’ data-dumps with too much detail simply turn into information pages that no one will take the time to absorb. At the same time, data reports that are too narrow, condensed, or poorly extrapolated are just as meaningless. In either case, ‘new reports spawn, but the old ones don’t go away,‘ creating an overwhelming amount of information that has the opposite effect of what was intended, getting your team further away from deriving actionable insights from the data they use.
#5. Banish data silos
A data fluent organization encourages integrated data sources and conventions across divisions and roles. In doing so, they rid themselves of problematic siloed data – that is, data that is not integrated and has its own norms, conventions, and terminology. This is where a written, company-wide data strategy can come in handy. A well-articulated data strategy provides a clear set of data management guidelines for everyone to follow, regardless of where they sit within the organization.
#6. Discourage data elitism
We’ve talked a lot about data democratization and how removing barriers of use and understanding throughout your organization can help you become more data-driven as a whole, but this bears repeating. As Zach Gemignani from Juice Analytics explains, “distancing analysis from the people who use it results in data products that are disconnected from the decision-making process.”