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Hoping to be helpful in data and analytics.

Some of my most enjoyable work experiences over the past 35 years occurred when I was involved in data and analytics or related work. My experience was primarily as a manager and generalist attempting to connect data and analytics activity to business value.

As I continue to read and strive to keep up, I often see examples where my experience could be helpful. However, I also noticed that talking about this at parties can make people want to stick a stir stick in their eye. I hope that writing will help me to organize and clarify my thoughts, stay involved in conversations with people who care and relieve those who don’t.

My perspective was developed reflecting on experience advocating, leading, and managing attempts to be more data and analytics-driven. There were some big successes but more than a few disappointments. Many times the cost was more than expected, and the potential envisioned was not realized. One of the disappointing aspects of this was seeing professionals in data, and analytics related disciplines (statistics, data modeling, master data management, governance, behavioral science) frustrated because the potential benefit of their expertise was absent. At the same time, executive sponsors and business subject matter experts become disillusioned when they are not getting a return on sizeable investments.

Often the reason for frustration on both sides is the inability to identify the problem let alone a solution. The basic assertion I make is that an essential part of the answer is identifying the critical components in the data-driven decision making value chain and address the weak links iteratively. Although this sounds simple, it can be difficult, especially in large organizations.

As new sources and volumes of data become available, and with the rapid improvement in tools, techniques, and computer power to analyze that data, the importance of an approach to data and analytics that guides us to solutions that benefit individuals and society increases. In other words, we need to be less driven by technology and technologists, and more connected to needs of socoiety.

Hiring a Manager of Data Science or Chief Data Officer

I hope this article is helpful recruiters or managers of “Data Scientists”. It could also be applicable to organizations considering a Chief Data Officer role to be responsible for Data and Analytics Strategy.

Would you rather hire:
A) Someone who has an in-depth working knowledge of “Data Science” and related technology.
or
B) Someone who knows their way around data and analytics well enough to identify and implement opportunities.

Person A is technically oriented – A has an in-depth working knowledge of Data Science and applicable technology. Skills could include statistics, machine learning, AI, working knowledge of BI tools, the ability to capture, slice and dice data. “A” may also need to be a data engineer if that support is not available. This combination of skills could include a high level of education, perhaps even a Ph.D.

Person B is business oriented – B knows how data and analytics connect to business value. This person can identify opportunities, gain support, manage relationships, guide statisticians and technicians, and deliver value. B is aware of the technical and organizational support a Data Scientist will need to be successful and how to get it. B measures success based on the value provided instead of the amount technology used, the sophistication of techniques, or the complexity of the solution as assessed by peers.

You will do well if you can find someone with either A’s technical skills OR B’s business knowledge and soft skills. The combination will be rare and much more difficult to attract. No harm in asking, I guess, but casually copying all those requirements into a job posting, then mistakenly thinking you have hired the combination will probably lead to failure and blaming the innocent.

Choosing the technically oriented person can work, but someone has to address B’s responsibilities for finding opportunities and eventually realizing business value, either directly or as a coach and mentor to A. Even then, A’s primary interest and highest value to the organization may be in deepening the technical skills they worked hard to acquire and are in demand. Data Scientist may get reported as a sexy job, but surveys show many are discouraged and spending significant time looking for other jobs.

3DM6 framework and principles help to organize thinking that coordinate data and analytics activity across the organization.
Contact Ed Unrau, [email protected], for an overview.