Facing Data Analytics Disillusionment
The Harvard Business Review article Why Marketing Analytics Hasn’t Lived Up to Its Promise (by Carl F. Mela and Christine Moorman, May 30, 2018) is a “trough of disillusionment” article. Mela and Moorman report the seemingly contradictory findings that (a) marketing budgets are poised to significantly increase spending in coming years on analytics while (b) actual evidence of analytics’ impact on company wide performance has actually been minimal.
This may be typical of what happens when the capabilities of technology (in this case, data management and analytical software) outstrip traditional but often sluggish corporate management processes.
The authors of the HBR article focus on two factors they say will help overcome barriers to realizing the benefits of data analytics: (1) the data and (2) the people who analyze the data. They then discuss what they call the “data analyst challenge” and recommend that management focus on the following factors:
- Clearly define the business problem.
- Understand how algorithms and data map to business problems.
- Communicate insights, not facts.
- Develop an instinct for mapping the variation in the data to the business questions.
- Identify the best tool for the problem.
- Span skill boundaries.
Examining the paragraphs associated with each of point reveals the authors’ belief in the need for balancing (a) deep technical skills, (b) understanding how the company’s markets operate, and (c) the ability to model and relate all relevant data to well defined marketing challenges.
None of these recommendations should come as a surprise. In my own research and consulting related to data program management I have found that corporate management, especially in industries and institutions that developed in pre-Internet days, often view the challenges of “big data” and “data analytics” from an overly technical perspective. Both new and traditional vendors have responded with a plethora of analytical, master data management, and data governance tools.
It's not unusual for the following to happen in the world of data analytics:
- Someone with an understanding (real or imagined) of both management and technology has a vision of what better data can do for the organization.
- Realizing that vision requires an integrated approach to strategy and management that crosses traditional organizational and disciplinary boundaries.
- Resistance and delay brought about by (2) provides an "in" for both traditional and new vendors to focus on selling technology-based tools as the (seeming) solution.
- Money is made and spent in step (3) but results are disappointing since changes to business processes and management structures take longer than buying and implementing new technology. Such disappointment generates reports like the aforementioned "Why Marketing Analytics Hasn't Lived Up to Its Promise."
- By the time (4) is reached, (1), (2), and (3) are still rolling on like a giant aircraft carrier that requires miles to turn around. This is how we get the seemingly contradictory finding mentioned by Mela and Moorman in the first paragraph above.
We appear to have moved way beyond what happened when computer automation was first introduced to corporations. Back then, making things “more, better, and/or faster” didn’t necessarily require wrenching changes to organizational governance.
Nowadays, when (often incompatible) data can freely flow across organizational and disciplinary boundaries, the need for unified data governance aligned with business strategy is critical. Making that happen, however, requires more than just new software and the hiring of technically qualified data scientists.
Copyright © 2018 by Dennis D. McDonald. Dennis is an independent consultant who provides short- and long-term support to private and public sector clients. His services include help with content and business development, market research, and project management. He loves writing, proposal development, and data of all kinds. Contact him via email@example.com.