Managing the New Analytical Ecosystem

Don't worry so much, you'll make yourself sad

—Danny Elfman

You have a team that is building analytical applications; the analytics staff is engaged with subject matter experts, sponsors, and executives. The ideas are flowing back and forth between the functional team and the analytics team, and the analytics projects are aligning with the corporate strategy. The feedback is that the analytical applications are being adopted and the need to drive change through data and analytics is being recognized and acted upon.

The investments that you and the organization have made in people, technology, processes, and transformation are recognized as being intelligent moves, and promotion for you is starting to be discussed. Looks like it is time for a vacation and a celebratory dinner with your family. Well, maybe it is time for that dinner no matter what part of the story and journey has come to pass.

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Stakeholder engagement – your primary purpose

Your primary role and that of your analytics team is to engage with stakeholders, including executives, sponsors, and subject matter experts, to understand their challenges and problems; translate those challenges into analytical applications and models; build those applications and models; prove that the new data-driven approach delivers superior operational results on a reliable and repeatable basis when compared to the previous approach; convince the executives, sponsors, and subject matter experts to adopt the innovations and changes; document and communicate success to sponsors and executives; and then to do it again and again, on repeat. This is the stakeholder engagement process. The stakeholder engagement process is illustrated below in Figure 9.1.

The stakeholder engagement cycle/process is at the core of why the executives gave the approval to fund you and your team. You need to know and understand that the execution...

Bias – accounting for it and minimizing it

We briefly discussed bias in Chapter 6, Ensuring Engagement with Business Professionals, but bias is a significant issue that we must face when building and managing an active and engaged advanced analytics and AI ecosystem.

Most people think of bias and they immediately talk about the data that is used to train systems. That is one very important part of bias. This is selection bias. We select data that we use to train our systems. Given that many aspects of our world are dominated by limited groups of people, we further institutionalize bias when selecting data from historical or current operational systems. Let's examine a few examples to bring the point to life.

Most C-level executives and board members are men, and more specifically, white men. When we select and use data about this group of people, we are including bias toward and related to white men toward the later stages of their careers. We bias toward men...

Ethics

I am sure that you have noticed by now that I am interested in and a proponent of acting ethically in all things.

Ethics is mentioned in six of the eleven major sections of this book. We have taken the time to discuss the importance of ethics and transparency and focus on the objectives and purposes for why we are using data and building analytic models and applications.

Now, take some time to consider how you will make ethics a topic of discussion with your analytics leadership team, the analytics team itself, and the functional/operational teams. It is worth the time to ensure that each group and all individuals are aware of your position on using data and analytics for ethical, honest, and transparent purposes.

Another element of the corporate environment that is critical to your success is understanding the mindset of your peers and the executives running the company. Let's talk about how deep the understanding of data and analytics is among the executive...

Summary

This chapter has been about discussing and explaining what you will need to do on a daily basis to ensure that you, your analytics team, and your functional team colleagues are successful.

We started the chapter by outlining how the analytical and production processes come together and work in a symbiotic way. These two processes support and engage each other to ensure that we have built, and are operating, an ongoing self-improving process to move the company forward in achieving our goals and objectives.

We are seeking to have a continuous loop of data flowing from operations to analytics and a smooth return flow of data and results to operational systems and to decision makers to support and extend the capabilities of the functional teams and the leaders who manage those teams.

We moved from detailed operational considerations to more conceptual topics.

We explored why you and your analytics team need to be cognizant of how to understand and mitigate bias...