Members of the Analytics Leadership Consortium (ALC) come together every quarter to share and discuss some of their most pressing topics in analytics. Our highlighted topics from discussions this spring include Remote Work, Job Family Modernization, Managing Complexity, Scaling, and Ethics.
Two years into the pandemic, teams are grappling with how to implement hybrid or permanent remote work policies. Companies are being intentional about how employees interact and defining when and why people should come to the office – as many leaders think coming back full-time is off the table. The capabilities necessary for a manager are also changing to include being able to lead, build connections, and have difficult conversations virtually. For many, these skills may be new, but they are necessary because management plays an important role in growing and retaining talent.
Job Family Modernization
Some companies are finding that standardizing job families both internally and in accordance with market norms helps to both set expectations for the position and establish a single source of truth for describing and categorizing job capabilities. One poignant example is the role of a data scientist. For some, this role implies a PhD and an intimate knowledge of the mathematics behind various models that they deploy, while for others, a data scientist means someone who applies state-of-the-art tools such as neuro nets. Job family modernization facilitates a company’s ability to attract and retain people with the right skills, set expectations, and maintain appropriate salary bands.
Many leaders mentioned two fronts that can both be generously described as managing complexity: reducing unnecessary complexity and planning with uncertainty. In reducing unnecessary complexity, one topic includes addressing the trend of using the most advanced tools available for all problems instead of recognizing when simpler tools can produce a cheaper, faster, and useful business solution. Another topic includes managing long-term commitments through automation, panning, and self-serve analytics to minimize the resources required to answer recurring questions or questions that have already been answered. Planning with uncertainty, on the other hand, is more focused on business continuity planning (BCP) in relation to complex or uncertain world events such as future waves of COVID or the war in Ukraine.
Scaling is not a new challenge, but three areas that data and analytics teams mentioned include: balancing support for different business lines as a center of excellence (CoE), managing the reach and mindset of analytics across the organization, and data.
Balancing support for different business lines as an analytics CoE means not solely servicing those with the largest business impact but also taking an appropriate amount of time for smaller, less visible business lines. Managing the reach and mindset of analytics is a combination of process engineering, culture building, and education that helps leaders to think more like analytical competitors. Mainly, this enables them to proactively identify ways that analytics and new technological developments can drive value at a company level. Lastly, data governance, data quality, and data pipelines were all mentioned as priorities for many analytics leaders.
In developing and deploying models, many analytics leaders are wondering if they are asking the right ethical questions that go beyond basic transparency, explainability, or regulatory requirements. This includes evaluating the ethics of vendor AI models and others that they did not build. Some companies are looking for different perspectives, such as experts at different companies or those with backgrounds in ethics or philosophy, while others are asking: Even though you can model it, should you?