As an executive, I constantly receive pitches from companies urging me to take advantage of the latest technology. The pitches are largely the same: I must implement or deploy the technology now or risk losing any advantage over my more forward-thinking competitors, who are lining up with their checkbooks open. Of course, the technology they’re urging me to deploy often comes with a steep price point and implementation and learning curves that only prolong the process.
These pitches are almost always filled with "buzzcronyms": ROI and TCO for the marketing folks, and ACID, JSON, MDM and YARN for those more technically minded (my favorite is the electronic interface for enterprise integration and optimization — you know, EIEIO).
Given that artificial intelligence and machine learning (AI and ML, for those who can’t get enough of acronyms) are among the hottest topics these days, it should come as no surprise that a significant percentage of marketing outreach involves these technologies. It’s on that note that I’m going to play devil’s advocate, and it may surprise you: While I’m the CEO of a company that offers ML-based data discovery, I’ll tell you there are several caveats you should consider before deciding whether any ML-based solution is for you.
Do I have any data? Do I have enough data?
Yes, this sounds like an obvious question, but machine learning works best when you have significant amounts of data, with no signs of slowing down its accumulation. Deploying ML against a relatively modest set of data is almost like trying to start a charcoal fire with a blowtorch: Yes, you can do it, but why? There may be more cost-effective ways of analyzing your data. (Defining a “relatively modest set” is a matter of perspective: Several years ago, “relatively modest” meant several hundred gigabytes. Today, it may mean dozens of terabytes or even several petabytes.)
What shape is my data in?
If your data is coming from a variety of sources, or if it hasn’t been cleaned and standardized into a consistent set, you won’t get value from any analytical exercise. Or, if you want to use another acronym, GIGO — garbage in, garbage out.
Have I thought about the total costs involved?
Lots of data means lots of storage. While the per-gigabyte cost of storage is sharply lower than it used to be, you can still run up a sizable tab. That’s before you’ve even considered computational costs: How much data do you analyze? How often? Who does it? In many cases, a data scientist may be needed to coordinate efforts. A cost analysis of the options is required to help make an informed decision.
Where is my data stored?
If you have multiple locations, you may need to find where and in what form your data resides. Even if you only have a centrally located enterprise data warehouse, information may be stored in multiple databases on that site. But, if you have a firm handle on all your data, if it’s all in one place and if you’ve continually carried out the due diligence to ensure that you have control of it, machine learning may not yet be needed.
Meanwhile, it’s back to my inbox ... and look, there’s another ad! At least you can easily figure out which pitches make sense to you and why.
