Chapter 5. Takeaways for Your Journey
J.P. Morgan’s 2021 Business Leaders Outlook found that 61% of midsize business leaders named economic uncertainty their top challenge in 2021.1 To stay agile in such an uncertain business environment, all organizations must put continuous efforts into their data strategies. This chapter closes with practices that any can adopt or evolve to improve their analytics capabilities and, in turn, their agility.
Analytics Center of Excellence
According to Gartner, centers of excellence (COEs) exist to “concentrate existing expertise and resources in a discipline or capability to attain and sustain world-class performance and value.”2 In Chapter 3, you saw that lines between technical and nontechnical users tend to blur in converged analytics. A shared analytics COE can help promote this shared ownership across business units. The COE shifts the mindset of analytics architecture from one-off implementation handled by the experts to continuously evolving integration, involving everyone in the organization.
Data Literacy Initiatives
A closely related goal of the COE is to develop analytics learning and growth opportunities to attract and retain talent. Establishing formalized analytics training programs and communities of practice for all roles and levels serves to institutionalize data literacy in an organization. With this shared knowledge basis, cross-functional teams are better able to use data together. McKinsey Quarterly illustrates examples of organizations who have benefited from establishing an “analytics academy” to help educate and prepare individuals across the workforce for the AI-infused analytics.3
A Culture of Experimentation
As organizations advance their analytics maturity, they tend to shift from slow-moving, monolithic uses of data to decentralized, rapidly evolving ones. Leaders must be comfortable with releasing minimally viable data and analytics into production and iterating over time given resources and needs. At the same time, a faster learning rate means a faster failure rate, so the organization should be comfortable with these advantages and disadvantages of business experimentation. Everyone in the organization should feel empowered to speak freely and make their case through data and experimentation.
Conclusion
In an unprecedented period of uncertainty, now is the time for analytics to shine in leading with business agility, giving leaders across the organization data-informed tools and products to support their decision making. Siloed, backward-looking data analysis by the few for the many can no longer suffice as an analytics strategy. Analytics maturity today means democratized access and use of real-time, AI-infused analytics. By assessing your organization’s current maturity in Chapter 4 and adopting the practices listed in this chapter, you will be on your way to promoting business agility through a converged analytics architecture.
1 “2021 Business Leaders Outlook.” J.P. Morgan, January 6, 2021.
2 “What Makes a Marketing Center of Excellence?” Gartner, August 24, 2016.
3 Solly Brown et al. “The Analytics Academy: Bridging the Gap between Human and Artificial Intelligence.” McKinsey Quarterly, McKinsey & Company, September 25, 2019.