Sustain and Grow Analytics


Congratulations! You have built an analytics team and created a concept of operations for successfully executing your work statement. You've seen how organizations can get started with advanced analytics and learned how to make your analytics capabilities understood by explaining analytics tools using a business case. But you are not done yet - you still need to sustain and grow your function and ensure to embed analytics into all areas of the organization and its business processes.

As a recent McKinsey[1] article illustrates, many companies are struggling when they try to scale analytics and embed data-driven decision making into every layer of their organization. Based on a survey with more than 1,000 companies, the authors identified nine critical areas for scaling analytics.

Aligning strategy

  • Obtain a strong, unified commitment from all levels of management
  • Increase analytics investments, with a focus on the last mile

Building the right foundations

  • Develop a clear big data analytics strategy with strong data governance
  • Use sophisticated data science methodologies
  • Possess deep analytics expertise enabled by a tailored talent strategy
  • Create cross-functional, collaborative agile teams

Conquering the last mile

  • Prioritize top decision-making processes
  • Establish clear decision-making rights and accountability
  • Empower the front lines to make analytics-driven decisions

Although you already put many of these success criteria in place to get your analytics organization to its current state, sustaining success requires ongoing focus. With changing business environments, adapting organizational priorities, and rotating leadership teams, analytics organizations need to continuously apply these principles to stay successful.

From a maintenance and growth point of view, we are going to describe three areas in more detail.

Talent management strategy – attracting and retaining your talent

Analytics leaders: Once you have attracted the right talent to build your team, you need a talent management strategy to retain and expand your analytics talent. A good data scientist is a scarce resource, this strategy needs to address several areas, some of which require tailoring for analytics:

  • Empowerment – letting your talent “loose” to do what they do best
  • Meaningful work – ensuring that your employees understand the significance of their work
  • Personal development – investing in your employees’ hard and soft skills
  • Community building – ensuring that your employees don’t feel isolated
  • External engagements – expanding your employees’ horizon and network
  • Recognition program – showing that their contributions are valued (often the most overseen aspect, but among the most important) 

The last mile – data-driven business culture

As pointed out by Bisson et al. in the McKinsey article, the biggest challenge is turning business intelligence into outcomes. This is what the authors call the last mile. It is where the value of analytics is ultimately captured. As Jack Levis from UPS once said in a public presentation, “Data without insight is entertainment. Insight without action is trivia.”

Here are some areas that you will need to address on an ongoing basis.

  • Data-driven business culture – investing in data literacy and analytics maturity of your environment. This includes thinking critically about the quality of data sources and where there may be unexplored data collection opportunities.
  • Consultative approach – engaging your analytics consumers and champions every step of the way.
  • Prioritization of top decision-making processes – identifying and focusing on the biggest “needle movers.” A variety of metrics could be used to prioritize projects, including return on investment, time to payback, or even important business priorities.
  • Clear decision-making rights and accountability – empowering employees to make analytics-driven decisions.
  • Support processes – ensuring that analytics-based insights can be put into action by decision makers and evaluated for impact.
  • Change management – carrying out changes associated with the implementation of analytics, which might include upstream or downstream practice changes.

[1] Peter Bisson, Bryce Hall, Brian McCarthy, and Khaled Rifai. Breaking away: The secrets to scaling analytics.

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