Going from Data to Ta-da! (Part 1 of 2)

Complementing Intuition with Data

Qualitative Metrics {Photo by Luke Chesser on Unsplash}

Relying on Intuition Alone Might Kill

Yes, there is substantially more clickbait headers used in this post as this is an important issue :). To illustrate my point, can you imagine driving without any of the gauges and indicators on your dashboard? For the fitness enthusiasts, imagine not having metrics and feedback from your smart watches. It’s not exactly desirable, and in the case of driving, downright dangerous.

Unfortunately, this is also happening with many businesses and organisations. We don’t make use of data to make informed decisions. In many cases, we don’t even have the data nor understand what metrics we need to inform us of the changes we need to make. On the other hand, with well-developed and tracked metrics, it guides the organisation to adjust and move purposefully` towards it’s goals. Furthermore, it also makes the organisation more efficient and empowers staff as there’s objectivity, thresholds and logic that managers and staff can now work and communicate clearly on.

Identifying the Reasons for Resistance (to being data-driven)

We often think about the lack of data or the tools necessary to generate the data we need. While it’s a possible reason for resistance, it’s also the least of our worry if that’s the only problem. I think there are generally 4 key challenges (arranged in increasing order of difficulty to deal with) that organisations face in adopting a data-drive approach to decision-making:

1. Lack of Data/Tool
   Amongst all the challenges, the lack of data and technology is probably much simpler to work through compared to people-related issues. Such symptoms include the lack of data, poor quality of data, and lack of tools to collect or automate the metrics needed.
2. Lack of knowledge and skills to develop good metrics
   It’s both a science and an art to define, track and consume good metrics, and hence it’s difficult to get it right. As an example, my organisation used to identify and keep track of the number of reports produced, and use that as a measure for performance and implement new initiatives. There were quite a few problems with using such a metric. It wasn’t comparative (neither against time nor competition) when we started, so we couldn’t really tell if we were headed in the right direction. It was also a vanity metric, much like tracking website page views which is far inferior to measuring new users or even repeat users visiting your website. More importantly, it wasn’t aligned and reviewed in conjunction to goals or strategies. This led to the blind pursuit of generating more reports that did not value-add to the outcomes. It was entirely possible that content was broken down into smaller chunks to generate more reports — an absolute increase in busywork!
3. Lack of buy-in
   This could be a lack of buy-in from either management or the team to develop metrics and use them. The underlying reasons could differ. For example, your team could be wary of the “latest fad that management is pushing for” and are not sure of the effort needed to start this new initiative just to see it abandoned later. Leaders on the other hand could be worried about exposing their teams’ weaknesses and shouldering “new found” accountabilities for real outcomes.
4. Unclear goals and strategy
   I feel that this is the least talked about concept and hence the most challenging to get right. Without clear goals and strategy, any metric that is being used is meaningless and can used to paint both positive and negative pictures at the same time. With the driving example again, going at 180 km/hour is excellent if one is in a race, but an absolute catastrophe waiting to happen if the goal was just to get to work. Setting the right goals and strategy is a whole other topic, but the point here is really to have clear goals and strategies before developing metrics to better inform decisions. These goals and strategies should also be present when we review metrics to avoid missing the forest for the trees (i.e. discussing the metrics and forgetting the larger intent behind what the numbers are supposed to inform us of).

With these challenges in mind, I want to continue deeper in the next post on developing good metrics which hopefully provides an initial reference to address issue #2, and some suggestions and overall approach to lower the organisation’s resistance to being more data-driven in making decisions.

Until the next post!

Edit (19 Sep 2022): Part 2 has been completed!