Whether you’re headed to ATD TechKnowledge 2019 to learn more about xAPI and analytics, VR and simulations, or AI and chat bots, new investments in learning technologies should always be made with the backing of a comprehensive data strategy.
Consider these pro tips to get your data transformation off the ground:
Recognize that you are planning for today, tomorrow, and the unknown.
Designing a data strategy is a long-term investment that will have far-reaching, organization-wide impact. You must think about things in both the current and the future state, and view your learning ecosystem and goals in multiple dimensions. Be sure to consider what you have now, what you’ve already made plans to invest in, what gaps you know exist, what gaps you haven’t uncovered, and what resources you’ll have access to as you move forward.
Take your team on a roadshow.
Once you have the thumbs-up to undertake a major initiative, teams can get sucked into a “me, me, me” trap. They start planning internally based on the problems or challenges they already know. Instead, you should start your transformation by listening to others. Take your team on a roadshow to other departments and ask what kinds of information they would like to know from L&D. Allow them to share what your team has been doing well, where there are gaps, and what insights are most important to them. This will not only increase collaboration and buy-in for your work, but also provide you with real data points to develop your strategy.
Connect with IT ASAP.
Nobody wants to get invited to the conversation after all the decisions have been made and the fun stuff has been designed. Chances are, your IT team can provide significant and unique considerations, such as security requirements, provisioning systems, permission structures, and deployment needs. Let them know what you want to accomplish, not what you’ve already done, so they can get on board early. This way IT can help you craft a better solution and prevent technical roadblocks later.
Evaluate with an impact-first approach.
The beginning stages of a data transformation initiative are critical. People want to see impact fast, usually with minimal financial and technical investment. Obviously, this is a challenging balance. The key is to pursue a technically efficient option rather than solely a monetarily inexpensive one. Evaluate and prioritize project factors based on business impact first, then consider technical difficulty. For example, in the case of xAPI data integrations, you might identify six priority objectives, with four of those objectives requiring two of the same data sources. In this instance, you should select the objective that has high impact but a low technical lift in the long run, resulting in the most repeatable and reusable data integrations. By going this route, impact is felt quickly, and the completed work will support the next iteration of the data transformation.
It’s been said different ways, but I’ll say it again: Don’t try to boil the ocean! Yes, the possibilities and potential for your learning analytics initiatives are endless. This makes it hard to know where to start, so you have to get specific. Focus on a discrete problem with specific objectives. Start small to achieve those early wins. Remember, you’re not only making an investment in technology infrastructure that will have a great impact on your department and organization as a whole, but you also are building new muscle for your team in data design and learning analytics.
For many people this will be the first time they’ve been able to quantitatively answer their big “why” questions with data. It’s going to be a moment of growth that will stretch your team’s capabilities. Providing measurable wins along the way will scaffold this learning and set you up for successful rollout and adoption.
Use the data you have to identify the data you need.
No matter what your learning ecosystem looks like today, there is some amount of data that already exists. Figure out how to access that data, whether that’s via spreadsheets, notes, or dashboards. Work with your team to define:
- why more data is needed
- why big questions currently go quantifiably unanswered
- why some information is siloed or inaccessible
- what you want to demonstrate and measure
- how you plan to use this information in the future.
Think of this as a “5 Whys” approach to data gap identification. Keep asking why, and you’ll start to uncover real pain points.
Remember that not everyone cares about “data”—but everyone cares about outcomes.
No matter how much we in L&D care about data, metrics, and evidence, it’s all just numbers on a page and graphs on a dashboard to others. You must contextualize your project for your audience; you have to keep the big picture in mind at all times. In other words, stakeholders want to know how this is going to help them. How is a data transformation going to support their team? How is this change going to help the company retain talent? Make sure that any time you share data and results, you connect it back to the outcomes that matter most—empowering your people.
Share your progress early and often.
Keeping people engaged and invested in your data transformation is an ongoing endeavor, and you will have to continually nurture interest! Think about how you will present your progress from the very beginning. This could be as involved as a weekly in-person meeting with a progress report presentation, or as simple as a Monday morning email sharing a challenge, a win, or a lesson learned. The reason for this is twofold: First, transparency keeps the project top-of-mind for stakeholders, and second, sharing progress provides a built-in opportunity for team reflection.
Get feedback as quickly and as frequently as possible.
The best way to get new perspectives is to hold in-person user testing sessions that request feedback directly from team members and stakeholders. Create a defined, replicable process for these sessions and have a plan for how to capture reactions. You’ll want to ask the same set of questions with each group at each iteration of the project, and make time to listen and observe how people interact with the data, tools, or dashboards you’re implementing in your data transformation. The more frequent and tighter you can make these iteration loops, the faster you’ll identify issues and be able to correct them.
Make data visual and accessible to empower your team to make it actionable.
While it’s easy to get wrapped up in the process of implementing your data transformation, stay focused on your ultimate goal. This isn’t about streamlining, getting rid of silos, or improving business process—those will be the byproducts of your success. It’s about making data useful and actionable. Plan early who should have access to the data, how they will access the data, and how you will make it easy for them to understand and work with the data to glean the insights they need to take action. By making data something that every person on your team is able to bring into their day-to-day work, you’ll change the “Why are we doing this?” question from a reaction to a proactive strategy. What’s more, decision making will truly become data-driven.
With these pro tips in your toolkit, you’ll be well on your way to creating a data strategy that will transform your team into an analytics leader at your organization. Remember to focus on the outcomes, keep the people in mind, stay flexible, and enjoy the experience!