Minding the Machines. Jeremy Adamson
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organization, still in its analytical infancy, is unable to take advantage of these powerful technologies, the cost of which is allocated to the analytics team. Organizations need to learn to walk before they can run, and the burden of these data-centric initiatives, executed far too early, has weakened many emerging analytics teams who have had to justify their inheritance after the fact.
These issues are the result of a lack of strategic focus. Well-intentioned projects initiated from a mature data team, supported by an executive, and imposed on future analytics teams leads invariably to technical debt and a handicapped team. Analytical excellence needs to be the focus and the function to be optimized and not confused with the activities that enable it. Early holistic strategy development that considers the interactions between these different teams is essential.
Summary
These common issues all share similar root causes across the pillars of strategy, process, and people. Without exception, every failed attempt to build out the analytical function could have been prevented with some forethought, forbearance, and expert advice. Every externality in the equation supports the practice—there are new approaches and new technologies and success stories of analytics teams adding value, optimizing processes, automating, and increasing the bottom line to the organizations in which they work. To be successful, however, standing up these analytics teams needs to be a thoughtful and measured approach that leverages best practices and integrates with the organization while having a mind to the future.
Organizations and leaders need to follow three guiding principles to successfully build and lead advanced analytics and AI teams:
Start Early The best time to have begun your analytics strategy was 10 years ago, but the next best time is today. Discuss with your colleagues in other industries, perform benchmarking exercises, and engage consultants where appropriate.
Go Slow As the team is chopping their way through the jungle of legacy processes, vestigial data pipelines, and change management, ensure that they are taking the time to pave the way behind them by documenting, securing, understanding, automating, and training others in what they are doing. Moving between projects in a frenetic commotion gives the impression of positive activity at the expense of long-term sustainability. Considering the total project life cycle of each project ensures that future people and processes are not entangled in patchy solutions that had only a short-term view.
Commit Fully Assume that the analytics function will not cover its own costs for the first two to three years and evaluate projects over a longer horizon. Integrate analytics into all functions and encourage a cultural change. Furtive deployments of analytics, starting with low-impact functional groups, leads to low-impact results.
It is my sincerest hope that this book helps you in your journey toward achieving analytics excellence in your organization. This practice can do great good in the world—it just needs to be allowed to succeed through planning and organizing and through minding the machines.
References
1 Ammanath, B., Jarvis, D., & Hupfer, S. (2020). Thriving in the era of pervasive AI. In Deloitte's State of AI in the Enterprise, 3rd Edition. Retrieved from http://deloitte.com/us/en/insights/focus/cognitive-technologies/state-of-ai-and-intelligent-automation-in-business-survey.html
2 Davenport, T. H., & Patil, D. J. (2012, Oct.). Data scientist: The sexiest job of the 21st century. Harvard Business Review. Retrieved from https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
3 PwC. (2017). Sizing the prize: What's the real value of AI for your business and how can you capitalise. Retrieved from http://pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf
4 Russell, B. (1902). Study of mathematics. Cambridge University Press.
5 Taylor, F. W. (1911). The principles of scientific management. Harper Bros.
6 US Bureau of Labor Statistics. (2020). Computer and information research scientists: Job outlook. Retrieved from http://bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm#tab-6
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