Sometimes, the biggest lessons come from the biggest fails. I’ve been privy to a number of data failures, so here’s my list of top organizational failures made when utilizing data.
1. Measure the wrong activity.
In the book Meltdown: Inside the Soviet Economy, the author describes failed incentives in Soviet-era Russia. The government chose meters drilled as the metric for petroleum drilling productivity. This metric may have worked fine, except that drilling the first 100 meters was markedly easier than the remaining drilling necessary to strike oil. Clever companies responded by drilling a lot of 100-meter holes, and never striking oil.
Incentives drive results. As a general rule, what an organization measures is what it will get.
Organizational metrics tend to steer employees to understand the organization’s priorities. But, if they are focused on the wrong activities, they can have drastic consequences.
Being able to find the correct activity to incentivize could take some time. If you are using data correctly you will be able to pinpoint where your issues are, and create a realistic incentive plan. Take time to find and strike at the roots. Not the branches.
2. Overcomplicating the metrics.
Healthcare organizations often fail in this way. We once asked an organization to explain to us how productivity was measured for their providers. After a 15 minute explanation, they decided to give us more time with a different team member to better understand it. We spent an hour with him and left more confused than we had started, despite our understanding of many other organizations’ productivity metrics. Shockingly (sarcasm intended), when I asked a few providers if they understood their productivity metrics, they responded with rolled eyes and a sharp “not at all”.
Unfortunately, this problem is often compounded because the most aggressive users of the data always want more. More information, more complexity, more to internalize. And, who do you suppose an informatics team listens to most? The most aggressive users of the data.
As a rule of thumb, every team member should be able to communicate and understand your high-level metrics. If the least common denominator team member cannot explain your metrics, it’s time to simplify.
3. Data in a black box.
In another organization we worked with, I was very impressed by the reports administration had created. The reports were intuitive, focused, and well-designed. The information they contained would have been very valuable to mid-level managers in the organization. Excitedly, I asked a few mid-level managers to show me how they used the reporting, on their computers. After fumbling around the company intranet for an uncomfortably long minute, each admitted that they either didn’t have access to or didn’t know how to access the reports.
At SyncTimes, transparency is an important factor that helps us drive results. We do our best to give every member of the team access to data, helping them know how we are achieving our goals, especially as it pertains to customer success. Openness facilitates trust and ensures that people have access to the information they need to be successful. The more accessible the data is to everyone, the more potential there is for people to spot insights from.
Obviously, other data fails to exist. Not explaining the “why” for tracking certain data points, sharing incorrect data, or slanting the data to tell a story rather than the complete truth. However, these are the top data fails I see all too often.
What data fails do you see organizations fall prey to?
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