Trends

Data Questions: Using Intelligent Tools When Everything Changes

Heather Bussing Heather Bussing

Three coworkers discuss data in conference room

In the current business climate, organizations can use their intelligent tools to formulate better data questions, adjust their perspective and address new challenges in creative ways.

What happens to the way we understand and use data when the course of business abruptly changes? Instead of only looking for answers in the data, we must also ask better data questions.

Many of our machine learning and AI-based tools depend on data that was more relevant before COVID-19. In March 2020, many of us changed where we work, how we work and the circumstances under which we work, which had the effect of making much of that data obsolete.

Big changes in our world have compelled us to think about how we can use our intelligent tools under new conditions. For example, if your people analytics tools rely partly on keycard data to understand where and how employees interact — but now your people are remote — what other information might you need to understand how employee interactions have changed?

If your organization laid people off, you might wonder whether you still need to pay attention to your employee flight risk predictor (you do). Staff reductions mean that the success of the business rests on the shoulders of fewer people. Retention must remain a priority, and data analytics is more essential to effective retention initiatives than ever.

Data can teach us a lot in these times, but open and frequent communication will be needed to transform data insights into meaningful developments.

The pandemic has taught us a lot about how context can affect performance. For example, when the NBA's workplace changed, so did the game. ESPN noted how players in a bubble at Disney World started scoring more than usual — for the first time in playoff history, two opposing players (Donovan Mitchell and Jamal Murray) scored 50 or more points in the same game. Was it less travel and more rest? Less noise? The fact that all the games took place in the same arena?

Measuring and tracking so many aspects of work to understand how they are evolving gives us an exciting chance to develop more useful data questions for the current business climate. But first, it's important to take into account what has changed and what is changing so we can use data and intelligent tools more effectively.

Here are some questions organizations should consider to help guide these investigations:

1. What has changed with ...

  • The place we work and the traffic our workplace sees?
  • The work we need to do for our clients now and down the line?
  • Our workforce?
  • Our business priorities?

2. How are these changes reflected in our data and tools?

  • How has the data changed since spring 2020?
  • What KPIs are affected by the changes?
  • Which tools and insights should we be using more? Which should we be using less?
  • Are there new trends emerging?
  • What seems weird, confusing or like it doesn't make sense?

3. What's missing? What do we need to know more about?

  • What assumptions are we making based on how things have worked in the past? Are those assumptions still valid?
  • What do we need to understand to make plans and decisions from here?
  • What are the contingencies and possible scenarios?
  • What data and information would help us understand our situation more clearly?

4. How do we manage the unknowns and contingencies?

  • What is most likely to happen, and why do we believe that?
  • What could change that assessment?
  • What data might help us address the unknowns, even if it's not what we would normally use?

5. What do we want to try next?

  • Are we actively working toward a specific outcome or waiting for certain conditions to settle first?
  • How long do we want to try this? Under what conditions might we reconsider and change course?
  • What data might help us determine the best approach to completing our key objectives?
  • When will we know that we have accomplished those objectives?

AI and machine learning are great at processing vast amounts of data and finding patterns, trends and matches — and they are also great at noticing changes. It may be some time before these tools can provide us with the answers to certain pressing data questions, but we should be proactive around using them to formulate better questions, adjust our perspective and address new challenges in creative ways. As Isaac Asimov explained it, "The most exciting phrase to hear in science, the one that heralds the most discoveries, is not 'Eureka!' (I found it!) but 'That's funny...'"

Consider surveying the members of your organization to verify your data-backed findings against their lived experiences as well. Data can teach us a lot in these times, but open and frequent communication will be needed to transform data insights into meaningful developments.

Learn more about how ADP® DataCloud can help you better manage, utilize and get the most out of your data.