Recently on Twitter, Daragh O Brien and I discussed his proposed concept. “After Big Data,” Daragh tweeted, “we will inevitably begin to see the rise of MOData as organizations seek to grab larger chunks of data and digest it. What is MOData? It’s MO’Data, as in MOre Data. Or Morbidly Obese Data. Only good data quality and data governance will determine which.”
Daragh asked if MO’Data will be the Big Data Killer. I said only if MO’Data doesn’t include MO’BusinessInsight, MO’DataQuality, and MO’DataPrivacy (i.e., more business insight, more data quality, and more data privacy).
“But MO’Data is about more than just More Data,” Daragh replied. “It’s about avoiding Morbidly Obese Data that clogs data insight and data quality, etc.”
I responded that More Data becomes Morbidly Obese Data only if we don’t exercise better data management practices.
Agreeing with that point, Daragh replied, “Bring on MOData and the Pilates of Data Quality and Data Governance.”
To slightly paraphrase lines from one of my favorite movies — Airplane! — the Cloud is getting thicker and the Data is getting laaaaarrrrrger. Surely I know well that growing data volumes is a serious issue — but don’t call me Shirley.
Whether you choose to measure it in terabytes, petabytes, exabytes, HoardaBytes, or how much reality bites, the truth is we were consuming way more than our recommended daily allowance of data long before the data management industry took a tip from McDonald’s and put the word “big” in front of its signature sandwich. (Oh great . . . now I’m actually hungry for a Big Mac.)
But nowadays with silos replicating data, as well as new data, and new types of data, being created and stored on a daily basis, our data is resembling the size of Bob Parr in retirement, making it seem like not even Mr. Incredible in his prime possessed the super strength needed to manage all of our data. Those were references to the movie The Incredibles, where Mr. Incredible was a superhero who, after retiring into civilian life under the alias of Bob Parr, elicits the observation from this superhero costume tailor: “My God, you’ve gotten fat.” Yes, I admit not even Helen Parr (aka Elastigirl) could stretch that far for a big data joke.
A Healthier Approach to Big Data
Although Daragh’s concerns about morbidly obese data are valid, no superpowers (or other miracle exceptions) are needed to manage all of our data. In fact, it’s precisely when we are so busy trying to manage all of our data that we hoard countless bytes of data without evaluating data usage, gathering data requirements, or planning for data archival. It’s like we are trying to lose weight by eating more and exercising less, i.e., consuming more data and exercising less data quality and data governance. As Daragh said, only good data quality and data governance will determine whether we get more data or morbidly obese data.
Losing weight requires a healthy approach to both diet and exercise. A healthy approach to diet includes carefully choosing the food you consume and carefully controlling your portion size. A healthy approach to exercise includes a commitment to exercise on a regular basis at a sufficient intensity level without going overboard by spending several hours a day, every day, at the gym.
Swimming is a great form of exercise, but swimming in big data without having a clear business objective before you jump into the pool is like telling your boss that you didn’t get any work done because you decided to spend all day working out at the gym.
Carefully choosing the data you consume and carefully controlling your data portion size is becoming increasingly important since big data is forcing us to revisit information overload. However, the main reason that traditional data management practices often become overwhelmed by big data is because traditional data management practices are not always the right approach.
We need to acknowledge that some big data use cases differ considerably from traditional ones. Data modeling is still important and data quality still matters, but how much data modeling and data quality is needed before big data can be effectively used for business purposes will vary. In order to move the big data discussion forward, we have to stop fiercely defending our traditional perspectives about structure and quality. We also have to stop fiercely defending our traditional perspectives about analytics, since there will be some big data use cases where depth and detailed analysis may not be necessary to provide business insight.
Better than Big or More
Jim Ericson explained that your data is big enough. Rich Murnane explained that bigger isn’t better, better is better. Although big data may indeed be followed by more data that doesn’t necessarily mean we require more data management in order to prevent more data from becoming morbidly obese data. I think that we just need to exercise better data management.