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Rise and Lies of the Chief Data Officer

Siemens Dreamer Siemens Dreamer
Siemens Dreamer

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A new C-level role has been introduced in the executive halls in recent years - that of the Chief Data Officer (CDO). The rise of the CDO is spreading fast, especially among the ranks of large US companies. It’s helpful to understand the catalysts of the emerging position as well as the expectations and the risks.

 

For decades the Chief Information Officer (CIO) was concerned with putting the necessary systems for data collection and storage in place. However, the rate at which data volume is increasing and becoming unmanageable, and the speed at which it is being generated, requires more dedicated attention.

 

This burgeoning data phenomenon instigated a rise in big data technologies that traditional IT departments are not equipped for. The Big Data technology landscape has not yet matured sufficiently and, as a result, it is cluttered with many options and specialized tools that each address different use cases. Remaining current with the rapid pace of innovation requires continual monitoring. Some of the new technologies such as Hadoop and MapReduce have made previously tedious analytics of large data sets much more feasible. This type of data analysis requires more than the technical background a Data Scientist may have; it requires a statistical skill set, as well.  A tongue-in-cheek definition Josh Wills tweeted is representative: “Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.”

 

The Chief Data Officer steps into this world of high data volumes and velocities to ensure the business derives value from all the collected data. Many businesses have an enormous amount of fragmented data spread over disparate silos. In such cases, data can become inaccessible, such as Dark Data, and unrelatable data. The CDO must be able to bridge the technical barriers to access and contextualize the data. If able to overcome this initial barrier, the next hurdle of veracity awaits the CDO: veracity (i.e., truthfulness) of the data, which becomes problematic when the same entity is called by different names depending on the source of the data. For example, one source may call it Coke but another may call it Coca Cola. Up to this point not much value has been derived for the business except perhaps starting to construct a single version of the truth. These are all stepping stones to the most important value of the CDO.

 

A Chief Data Officer’s most significant contribution is in the area of analytics. By employing concepts of data mining and machine learning, a business can discover valuable insights. These fact-based insights can be used to drive decisions that were previously based on speculation and reasoning. However, this is a very precarious position to be in, as Mark Twain said, “There are three kinds of lies: lies, damned lies, and statistics”.  A CDO will be promoting lies or inaccurate outcomes if they are not well equipped and trained. To ensure the quality of the results, they must ensure the quality of the data, which is why data governance must be the first thing on a CDO’s agenda. This will quickly transform any data lies into a single truth that can be distributed across an entire organization.