Introduction

This book is intended for senior managers and executives who are contemplating or have made the commitment to hire and manage a team of individuals with the stated purpose of designing, building, and implementing applications and systems based on advanced analytics and artificial intelligence. If you believe that this objective can be accomplished in a year or less and can be achieved cheaply, do not buy this book. You should stop reading, cancel your current plans, and save your money.

Making a commitment to drive your organization to a higher level of effectiveness and efficiency is what you are doing. Just as if you were deciding to build a state-of-the-art factory or if you and your team were entering into a completely new market or geographic region of the world – if you do not have a multiyear view, then you should seriously reconsider undertaking this journey.

In this introductory section, we will walk through the process of succeeding in the analytics...

Becoming data and analytically driven

One of the mindset changes as well as the organizational process changes that is required to be successful in this journey is that by becoming a data and analytically driven organization, you at some point realize that the organizational change you seek is never "done." This process is evergreen and ever changing.

Along with the "macro" process of organizational and mindset change, there is a "micro" process of evolving and changing in response to the needs, wants, and desires of customers, patients, the market, the environment, suppliers, investors, stakeholders, and competitors. From the perspective of the middle of the processes as described, these processes at the execution level are usually described, organized, and discussed as projects. The larger overall process is typically made up of projects that focus on a specific objective or goals, but the overall process is dynamic and ever changing. If you, your...

An analytical mindset

For those readers who are analytics professionals, let's draw an analogy. Advanced analytics models are trained on data. The data represents the world or the subject area at the time that the data was collected. Once the model is trained and that model is accurately "predicting" the characteristics of the subject area as represented by the training data, the model is "locked." By locking the model, we end the training phase and we move the model into production.

The model ingests and examines data in the operational world and predicts the information that we are interested in. But we all know that the world changes and so does the data that is the byproduct of those activities. The models must be updated or retrained using current data to ensure that the models are generating predictions based on data that is as close to the current state of the world as possible. We "unlock" the model and train it again using new data....

Building an analytics team and an environment for collaboration

You will need to hire a leader. You will need to empower and fund that leader to hire and lead a team. I refer to this team as the Advanced Analytics and Artificial Intelligence Center of Excellence (AA&AI COE). The COE leader and team will need to learn the broader organization and to build a network of collaborators, sponsors, and allies. This new network, which we will refer to as the Community of Practice, or COP, will span the entire organization and global operations. If your organization numbers in the thousands, the AA&AI COE will be less than 50 staff members within the first two years and the COP will be a few hundred in number.

The most recent COP that I built took approximately a year to reach critical mass and was made up of between 400 and 500 staff members spanning the globe, including every operational department in the company. I stopped counting after the first 14 months, but, in the first...

Collaborators in the analytics journey

Let's define the taxonomy and naming of the collaborators that we will discuss and describe in the book and work with on our analytics journey. I will be referring to executives as sponsors, because they typically set the direction and control funding and staffing for their organization.

Also, I will be referring to managers as stakeholders, as they typically own the headcount that is needed to collaborate with the AA&AI COE staff. And finally, I will be referring to the staff members of the operational departments as subject matter experts. The AA&AI COE leadership, data scientists, data engineers, data visualization experts, and others cannot be successful without the full-throated support of sponsors, stakeholders, and subject matter experts. A transparent and trust-based relationship between all parties is crucial to our joint success.

I have explained in keynote speeches, fireside chats, articles, white papers, meetings...

Selecting successful projects

At this point in the process, the analytics leadership and the AA&AI COE team can begin to discuss and select an operational area or areas to partner with to begin to evaluate options for analysis and possible operational improvement. The AA&AI COE will discuss the potential areas for improvement with the operational or line-of-business executives and teams. The discussion and selection of areas for examination and improvement through data and analytics sounds like it should be straightforward and encounter little to no contention in the process, but even in the most apolitical organizations, this is not the case.

Organizational dynamics

Executives and senior managers will span the range of behaviors, from exuberant and public support to actively attempting to block projects and progress.

Building the AA&AI function and capability in an organization will be seen by some as an opportunity to grab more budget or funding and to create a larger organization or empire. You, as the executive sponsoring or building this new capability, need to be aware of this dynamic and ready to evaluate the motivations and abilities of these managers. It is entirely possible that these existing managers may understand data and analytics and have the experience, expertise, and knowledge of how to be successful in the macro and micro processes described earlier in this introduction. If that is the case and you believe in them, then the organization has a head start in the process, but if they do not have the requisite experience and knowledge, and they think that the success that they have had in other, possibly...

Competitive advantage or simply staying competitive

First, you should consider outsourcing certain projects to competent, capable, and proven third parties. The projects to be considered are those that others in your industry have completed and are now considered tables stakes to be competitive at the new level of efficiency and effectiveness that the industry operates at or that the market, customers, suppliers, and patients demand. Projects like inventory management, supply chain efficiency, or designing servicing maps for optimal territory coverage by a sales team – these are projects that have been successfully executed across numerous industries with well-known and published success stories.

Find an experienced third party with a long track record of success and outsource the project. Of course, you need competent team members to manage the project, but you do not need analytical professionals to manage this relationship and process. To be clear, you cannot wash your...

The core collaboration/innovation cycle

Once sponsors have committed their teams to the process of improvement through data and analytics, and candidate areas for improvement have been selected, then the AA&AI COE team and the functional team (that is, the sponsors, stakeholders, and subject matter experts) can begin to analyze the operational area, the processes, the data, the organizational resistance to change, and the feasibility that the required data exists today and in a historical form. The AA&AI COE team can explain to the functional team the analyses that will be experimented with and undertaken and the analytical pipeline and models that will be developed.

The functional team will need to understand the process changes, the new mode of operating, and the resulting changes to their personnel needs and daily functions.

After a common understanding of the area to be analyzed and improved through data and analytics has been developed, the functional team and...

Focusing on self-renewing processes, not projects – an example

Perhaps a concrete example can ground our discussion to make it clearer and more understandable where we stand in the overall process and the pitfalls that face the combined analytical and functional teams.

Let's use the example of retail store site selection. Before the widespread use of data and analytics, organizations had site selection teams that performed research on the proposed region, markets, and neighborhoods; visited prospective locations; scouted sites; and spent time talking with local business people, local governing bodies, landlords, real estate agents, brokers, and others. They compiled briefing books on the various options and made presentations to management regarding their process and the locations that the site selection team felt were the best bets. This process could take weeks to months to possibly years.

Why is this traditional process not optimal and why does this type of...

Summary

I hope that after reading this introduction, your interest has been sparked and that you are intrigued about how to execute and drive the analytics process forward in your profession and in your organization. The process will not be quick, it will not be easy, and in some parts of the process, you will not enjoy it, but it won't be dull, it won't be boring, and you will always be learning. If you are a lifelong learner, and the chances are good that if you have read this far you are, a career in analytics is one of the most fulfilling careers you can choose.

People continually ask me something along the lines of, "Analytics is such a hot and current field, how did you find yourself in it so many years ago?" My response is typically something like, "I am a lifelong learner. I live to learn new things. Not just things about data and analytics, but I do love learning about those topics too. I love to learn about everything – ethics, physics...