I found the McKinsey’s article from October 2, 2020, on “How to be great at people analytics” useful, because it was complementary to a plethora of cases we studied throughout our class in Leading with Peoples’ Analytics (LPA) at HBS.
Although the cases showcased pros and cons of having dedicated teams perform people analytics at large companies such as McKinsey, Google, and Teach for America, they did not address complexities of launching and growing such teams at well established institutions. And the task is by far not trivial!
According to McKinsey’s article “ [there were] different ways that firms have approached building their people analytics functions. Team size, composition, and organization vary widely, and priorities for capability development and maturation differ significantly” (McKinsey, 2020). The authors further described immense challenges in how corporate environments embraced this relatively new function: “Most companies still face critical obstacles in the early stages of building their people analytics capabilities, preventing real progress.” (McKinsey, 2020).
Frankly, my own experience from three Fortune 500 companies corroborates these findings, because my corporate heavyweights lacked sufficient talent and innovative spirit to capitalize on this new function, or simply did not even have it (e.g., the Public Service Enterprise Group and Shell).
As my LPA class wrestled through the cases, which helped form understanding of how companies used people’s analytics to drive business performance, promotion decisions, and employee engagement, I could not stop thinking about the process it took them to get to a clear dataset and R-code. McKinsey, however, helped me think about this in terms of “a stairway to impact,” which often requires arduous effort of cross-functional alignment and sponsorship of senior leadership teams.
The main takeaway from the framework outlined in the article was that a buildup of people analytics team at a company was an iterative process, which often necessitated taking steps back, before progressing further up the “stairway to impact.” (McKinsey, 2020).
Out of 12 companies interviewed by McKinsey for the article, none achieved the holly grail of step 5 i.e., “reliable predictions.” Some companies were still in the process of dealing with poor data quality, from which it was impossible to draw any reliable and actionable conclusions.
In other cases, company’s technology used for data storage and processing had to change, which highlighted the need for agile and adaptive talent on LPA teams. However, whatever the stage of a company in building its LPA function, the most successful companies had one thing in common i.e., rather than relying on generalist data science skillsets, the companies leveraged subspecialities including “natural-language processing, network analytics, and quantitative psychometrics,” and linked their work to strategic corporate objectives. They also embraced culture of “trust, empowerment, and ownership”(McKinsey, 2020).
So, why is this article so relevant to my learning? I see LPA as one of my most important tools in how I plan manage hundreds of people at Amazon Logistics in the next couple of years post-MBA. As I grow my responsibility to thousands of employees and a complex operation that goes beyond one delivery station, I believe that no human being will be capable to fully optimize a workforce without relying on data-driven approaches (such as machine learning, and natural language processing to perform deep network analytics). Hence, understanding how to best build LPA function at a large e-commerce conglomerate or a smaller startup, will make me deliver impact on people and business that I seek.