Imagine going into a QBR with a customer you’ve been losing money on. You want to raise your rates. It’s going to be the kind of tough conversation that you dread, not the least of which is because you could end up losing a customer. But you’re prepared to do that, because you’re losing money on them.
What if you’re actually not losing money on them? What if you arrived at that conclusion because the data in your PSA doesn’t paint an accurate picture of them as a client? Duplicate tickets make you think you're working harder for them than you actually are. Now you’re having a difficult conversation for no reason and might churn a profitable client.
Alternately, what if you don’t trust your data? Your team is pretty sure they’re not a good client, and the numbers you looked at confirmed this. But you don’t have time to conduct regular data hygiene, and this client has a lot of projects, tickets and history with you. Their file in your PSA is a mess and you know it. Because you don’t trust the data, you avoid the hard conversation, and ultimately keep them on for a few more years, only to find out they were a money-losing client all along.
Either way, the quality of your data had an impact on your decision making, and not for the better.
When it comes time to making quality data-driven decisions, there’s garbage in, garbage out (GIGO) and there’s quality in, quality out (QIQO).
It’s really that simple. Good data means you’re making the best possible decisions, armed with facts rather than assumptions and guesses.
Where Clean Data Comes From
The 1-10-100 Rule makes it clear that there’s three ways to get clean data. One is up front, by ensuring that the data goes into your PSA correctly in the first place. There’s a lot of ways to approach this, but understanding the sources of dirty data is huge, and so is building a culture around data. In fact, a recent Harvard Business Review study showed that having a data-driven culture is essential to extracting value from data, because even frontline workers will be empowered to make data-driven decisions.
The second way to get clean data is to clean it up routinely. After all, some data goes in accurate and up-to-date, and only becomes dirty after the fact. Gradient’s good for that. Our data hygiene software helps flag dirty data, and allows you to remediate dirty records quickly and easily. Plus, you get to hang out with Phil the Flamingo. He’ll help you tackle the Data Monster.
The third way to get clean data is to make a bad decision. Then you find out the hard way that you made a bad decision, investigate to find the sources of the error, and only then realize you had bad data. This approach is effective; you’ll definitely figure out you had bad data. Gradient MSP does not endorse this approach.
Gradient's Approach to Clean Data
We do endorse a combination of effective controls on data going into your PSA, with regular cleaning using Gradient’s free data hygiene solution.
This is all you need to know about clean data. Don’t overcomplicate it. Build data hygiene into your business right up front. Routinely sanitize your PSA data with Gradient. Make better decisions, with confidence.
