- Sean Cogan
Data automation does not mean you lose control of your data
Frequently, data engineers tell us they are concerned about how implementing data automation means they lose control of their governance, and ultimately, are concerned about the risk to their careers.
Let’s correct the record: data automation will not result in a loss in data governance or risk data engineers’ or data scientists’ careers.
In fact, not only does data automation allow data teams to retain governance over their data, but it allows data scientists and data engineers enhance their careers. Before we explore why, let’s discuss the importance of data automation.
If you are a small- to mid-sized company, leaving money on the table could break your business. While the occasional inaccurate forecast may not harm a large organization, if you have a smaller team with a smaller user base, a couple of missteps could literally ruin you. If you don’t automate your data, you run the risk of missing these critical revenue opportunities. Because, without understanding your data, you might be investing money into channels that aren’t producing revenue.
With data automation, you can better identify revenue-generating channels, which will help inform future strategy and how to reallocate budgets, big or small. In my last blog post, I talked about a data aggregator who said they were “only” off by 150 impressions. Well, 150 impressions in a major segment for a relatively small company could lead to a lot of lost revenue.
Additionally, as I mentioned, reducing trust in the data could be an existential threat to an organization’s data journey. Because if people believe the data is inaccurate, every department will question its veracity. And if that happens, they will ignore the data and return to guessing, even though they just spent a whole ton of time and money trying to avoid guesswork in the first place!
Let me illustrate. One prospect is an education company with about $12 million in revenue. They need to understand how to maximize their budget and continue to scale, or they will plateau. They are investing in 40+ different marketing segments, of which they only understand the top five, because the data from the other different channels is in different formats. Their team is trying to clean and validate the data manually, but they are learning that this process is expensive and time-consuming. And, as the data gets more complex and they add additional channels, they run the risk of increased errors while needing to hire even more staff.
Naturally they want to optimize revenue. But to get there, they need to analyze ROI across different data sources, and are left with one of two choices to get there: one, hire a team of data scientists and engineers to normalize and validate all their data on an ongoing basis; or two (the option we recommend) embrace data automation to scale revenue while reducing their expenses.
The main concern they have, however, is losing governance over their data, which brings me back to the point of this post.
The truth is, with data automation, the team will still have access to, and have authority over, the data. They just won’t spend their time pouring over endless spreadsheets to arrive at that single truth. Moreover, data automation implemented as part of an ETL (extract, transform, load) strategy will allow data scientists and data engineers to focus on more interesting, and higher-priority work while reducing their manual tasks. Meanwhile, they will still have full control over the data governance.
So, if you are a data scientist or a data engineer, you should know that data automation does not mean you lose control over the data. It just means reducing the risk of errors while increasing efficiency and reducing overhead. Additionally, data automation will allow data teams to engage in more productive and interesting work. That’s why we encourage you to embrace data automation for your enterprise, no matter your size.
Be Data Strong!
Read more from our blog HERE
Learn more about ETL here: ETL: The Ultimate Guide.
Learn why a publicly-traded media conglomerate said, “We had previously invested over eight months of engineering to overcome a data challenge that took Switchboard one month to solve.” Email me to learn more: firstname.lastname@example.org.