3 Ways to Make Data Literacy a Two-Way Street
I count myself as extremely lucky to have worked in many highly data-literate environments in my career to date. What is data literacy? For me, data literacy’s definition is that the consumers of data products (from static reports to interactive tools to comprehensive analyses) have enough understanding of those products that they can use them to drive action and make appropriate decisions. For this to work, those data products must be intuitive and easy to use.
I believe that data literacy is a two way street; producers of data products and the consumers of those products must meet in the middle if you want to provide value and drive impact. For example:
- Your stakeholders need to trust you, but to build that trust you need to demonstrate an understanding of what their goals are.
- Your stakeholders should make use of the data products you provide them, but you are on the hook to ensure that they land well to drive better decisions in the organization.
- Your stakeholders should self-serve where appropriate, but you must provide the tools and the enablement to make this as easy as possible.
Lasting data literacy requires this persistent, bidirectional approach. Here are three elements to consider:
- Start with a data literacy assessment. If overall data literacy levels are low, even the most comprehensive data products are likely to offer little value. Data leaders need to step up and assess where employees stand on data, then educate them on why it matters. Think of yourself as positioning and equipping your stakeholders to make the most of your data outputs, not merely delivering them.
You also need to hire well. That means people who are not only highly data-literate but also able to quickly assess the environment they are working in, know how to frame problems and deliver insights in a way that increases acceptance, and can listen to feedback in terms of what is not working. If they lack pieces of this overall package, you should be helping them develop the data literacy skills they need.
- Accelerate toward creating impact. Closing data literacy gaps means making your data products as easy to consume as they are actionable and relevant. Our core purpose as data leaders is to drive impact within the business and ensure that, fundamentally, decisions are being made in a way that is informed by data. If we do this well, we create a greater confidence in those decisions because they are based on evidence.
Some good news here is that the tools available to us are becoming more accessible every day. Off-the-shelf commercial data tools have vastly improved in recent years. Low and no-code solutions (including Tines) exist for almost every application. Barriers to empowering non-technical, non-analytical colleagues to do basic analytics and self-serve have come down considerably. If you want to know your sales trends and which customers are performing, for example, there is no longer a need to write code or understand statistics to create useful data analyses to help stakeholders interpret data. In essence, technology has removed a lot of the excuses to driving data literacy forward into tangible action.
- Arrive at a culture of more confident decision-making. As a former colleague of mine often says, decision making is a skill. One signal that this skill is growing within your organization is when you experience more pull for data products. For this to work sustainably, you need to build a good foundation of understanding and trust in those products. Again, it’s a bilateral process
How will you know you have been successful? Data literacy is a continuous journey - a moving target - but there are clear signs. You know you are on the right track when members of your teams start being asked to jump into more and more engagements because colleagues trust and value their perspective.
All that said, we as data leaders should be the initiators of the dialogue, defining what is data literacy and then taking steps to assess it, create impact, and build a culture around it. There is no simpler way to put it: if we want our colleagues and customers to feel engaged and positive about using data - to improve organizational decision-making - we need to take the first step.