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Big data continues to be a force for change. It plays a part in the ongoing drama of corporate innovation -- in some measure, giving birth to the chief data officer role. But consensus on that role is far from set.
The 2018 Big Data Executive Survey of decision-makers at more than 50 blue-chip firms found 63.4% of respondents had a chief data officer (CDO). That is a big uptick since survey participants were asked the same question in 2012, when only 12% had a CDO. But this year's survey, which was undertaken by business management consulting firm NewVantage Partners, disclosed that the background for a successful CDO varies from organization to organization, according to Randy Bean, CEO and founder of NewVantage, based in Boston.
For many, the CDO is likely to be an external change agent. For almost as many, the CDO may be a long-trusted company hand. The best CDO background could be that of a data scientist, line executive or, for that matter, a technology executive, according to Bean.
In a Q&A, Bean delved into the chief data role as he was preparing to lead a session on the topic at the annual MIT Chief Data Officer and Information Quality Symposium in Cambridge, Mass. A takeaway: Whatever it may be called, the chief data officer role is central to many attempts to gain business advantage from key emerging technologies.
Do we have a consensus on the chief data officer role? What have been the drivers?
Randy Bean: One principal driver in the emergence of the chief data officer role has been the growth of data.
For about a decade now, we have been into what has been characterized as the era of big data. Data continues to proliferate. But enterprises typically haven't been organized around managing data as a business asset.
Additionally, there has been a greater threat posed to traditional incumbent organizations from agile data-driven competitors -- the Amazons, the Googles, the Facebooks.
Organizations need to come to terms with how they think about data and, from an organization perspective, to try to come up with an organizational structure and decide who would be a point person for data-related initiatives. That could be the chief data officer.
Another driver for the chief data officer role, you've noted, was the financial crisis of 2008.
Bean: Yes, the failures of the financial markets in 2008-2009, to a significant degree, were a data issue. Organizations couldn't trace the lineage of the various financial products and services they offered. Out of that came an acute level of regulatory pressure to understand data in the context of systemic risk.
Banks were under pressure to identify a single person to regulators to address questions about data's lineage and quality. As a result, banks took the lead in naming chief data officers. Now, we are into a third or fourth generation in some of these large banks in terms of how they view the mandate of that role.
Isn't that type of regulatory driver somewhat spurred by the General Data Protection Regulation (GDPR), which recently went into effect? Also, for factors defining the CDO role, NewVantage Partners' survey highlights concerns organizations have about being surpassed by younger, data-driven upstarts. What is going on there?
Bean: GDPR is just the latest of many previous manifestations of this. There have been the Dodd-Frank regulations, the various Basel reporting requirements and all the additional regulatory requirements that go along with classifying banks as 'too large to fail.'
That is a defensive driver, as opposed to the offensive and innovation drivers that are behind the chief data officer role. On the offensive side, the chief data officer is about how your organization can be more data-driven, how you can change its culture and innovate. Still, as our recent survey finds, there is defensive aspect, even there. Increasingly, organizations perceive threat coming from all kinds of agile, data-driven competitors.
Randy BeanCEO and founder, NewVantage
You have written that big data and AI are on a continuum. That may be worthwhile to emphasize, as so much attention turns to artificial intelligence these days.
Bean: A key point is that big data has really empowered artificial intelligence.
AI has been around for decades. One of the reasons why it hasn't gained traction is, in its aspects as a learning mechanism, it requires large volumes of data. In the past, data was only available in subsets or samples or in very limited quantities, and the corresponding learning on the part of the AI was slow and constrained.
Now, with the massive proliferation of data and new sources -- in addition to transactional information, you also now have sensor data, locational data, pictures, images and so on -- that has led to the breakthrough in AI in recent years. Big data provides the data that is needed to train the AI learning algorithms.
So, it is pretty safe to say there is no meaningful artificial intelligence without good data -- without an ample supply of big data.
And it seems to some of us, on this continuum, you still need human judgment.
Bean: I am a huge believer in the human element. Data can help provide a foundation for informed decision-making, but ultimately it's the combination of human experience, human judgment and the data. If you don't have good data, that can hamper your ability to come to the right conclusion. Just having the data doesn't lead you to the answer.
One thing I'd say is, just because there are massive amounts of data, it hasn't made individuals or companies any wiser in and of itself. It's just one element that can be useful in decision-making, but you definitely need human judgment in that equation, as well.