Everybody talks about data-driven decision making, but here’s the thing. Decisions are about how you’re going to approach a future event, while data comes from the past. The past is not a perfect predictor of the future. This means that how you interpret data is important. Every decision, ultimately, will be made by utilizing a combination of data, qualitative knowledge, your technical expertise and your past experiences.
Understanding the role that data plays in decision making allows you use the data at your disposal more effectively. Your decisions will be sharper, more likely to deliver the results you seek. Moreover, your decision-making will be replicable, something you need in order to scale.
Data-driven decision making is one of the key competitive advantages that large enterprises have, specifically because of how much they are able to invest in gathering and interpreting critical data points.
Data helps remove biases from decision making - and we all have biases. Removing those biases as much as possible gives you more confidence that you’re making the right decision. If you formulate a particular hypothesis, use your data to test it.
Data also can take you from the reactive space – where the data indicates that a decision needs to be made, to a proactive one where patterns and trends in data are extrapolated to the future. Yes, years like 2020 make forecasting more difficult but those years are the exceptions. Even in an exceptional year, the long-range trend towards SMBs being increasingly reliant on their technology providers still held. The day-to-day may have been disrupted, but the MSP business being incredibly resilient and in high demand did not.
When you know the data well, and understand the context that surrounds it, you're in a position to move proactively, rather than simply reacting to the numbers.
We use our experience, expertise and qualitative knowledge to help us do two things. One is to help us make sense of the data. This means knowing what data to look for, and having the capacity to interpret the data we have. Fortune 500 companies use AI-driven approaches and have specialized data scientists. The rest of us work with dashboards, spreadsheets and reports from our various software tools. Either way, the biggest differentiator should always be your ability to interpret the data, not the data itself.
As you scale, you’ll delegate decision making to people who have different knowledge and experiences than you do. That’s good, of course, especially where those skill sets are complementary to yours. To leverage the value of delegation, you at least want to be confident that the data your team uses is of high quality. Things like training on how to interpret data, and picking the right people to make the right decisions, that’s your responsibility. That the data your team is using to make decisions is clean and reliable should not be an additional burden for you to worry about.
This is where data hygiene comes into play. Whether you’re doing it, or an AI is, or whatever, interpreting the data requires good, clean data. Whatever dirty data is in the system is going to obfuscate your view of whatever it is you’re trying to analyze. Those data scientists the Fortune 500 companies use? They spend 80% of their time cleaning data.
That’s how important it is to have clean data. Without clean data, the best of the best will still struggle to interpret the data correctly, and make better decisions accordingly.
So how much dirty data do you have in your PSA?
How confident are you that you’re making decisions with the best data possible?
Make decisions with confidence.
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