Regarding Big Data One Manager's "Resistance" Is Another Manager's "Caution"
Matt Turck has some interesting things to say about “big data” in “Is Big Data Still a Thing? (The 2016 Big Data Landscape).” Focusing on the technology, he seems to be saying that expectations for big data’s game changing disruption haven’t been met but that what we are seeing is maturation of the field brought about partly by steady improvements in applying Artificial Intelligence (AI) techniques to help manage and make sense of big data.
After all, Turck suggests, big data is characterized by situations involving vast quantities of rapidly expanding data volumes; we puny humans can’t be expected to lasso meaning from all this without some help from AI-based minions.
A lot of this makes sense when looked at from an “early adopter/late adopter” perspective. Data intensive “digital native” companies (e.g., Google, Amazon, etc.) have been in the forefront because they’re already data-centric. Less “resistance” means faster adoption by them, right?
While I have to defer to Turck on the basis of his understanding of the technological landscape, a lot of this discussion has a very familiar ring to it.
Turck’s is a technology-centric view that’s focused on an industry that emphasizes development and selling of products and services. Viewed from the customer perspective, however, variations in adoption rates naturally exist among the majority of companies that are not “digital natives” but are still highly dependent on a range of legacy systems that serve as the sources for both day to day business transaction processing as well as potential big data/AI-based processing. Add in sensor based and unstructured data and you have a situation where traditional data management thinking may prove inadequate.
When we look at these legacy-dependent companies it’s tempting to view them as being “resistant” to new technology and innovation. I think that is too simplistic a view. Sure, people might resist change because they want to control uncertainty and risk. But when something comes along where (a) hard benefits are difficult to quantify and (b) that may require nontrivial changes to current systems and business processes to generate these uncertain benefits, isn’t it logical to expect some resistance that’s not restricted to the “Luddites”?
Perhaps, one manager’s “resistance” is another manager’s “caution.” Given the wide variations that currently exist in most organizations in understanding the ins and outs of managing current data governance and analytics practices, it’s not surprising that bringing in potentially “disruptive” technologies will be even more of a challenge.
As with many new and promising technologies, the more we can do to simplify/streamline/standardize the adoption process, the faster we can move past initial resistance to serious benefits delivery. One approach is a working prototype or system that can be delivered rapidly via an Agile type management framework, coupled with management’s serious acknowledgement that there’s more to data management than just adding another layer to the stack of systems that need to be integrated.
I suspect that a lot of what people do now as “resistance” is really management asking three very basic questions about big data and sophisticated analytical techniques:
- What’s in it for me?
- How much time will it take?
- How much will it cost?
- I’d like to thank Harlan Harris of Data Community DC for talking with me about the topics discussed in this article!
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Copyright (c) 2016 by Dennis D. McDonald. Dennis’ consulting interests include project, program, and data management; market assessment, digital strategy, and program planning; change management; and technology adoption. Currently he’s focusing on big data project planning & management. He can be reached by phone at 703-402-7382 and his web site is located at www.ddmcd.com.