Selecting Winning Projects

Man does not simply exist but always decides what his existence will be, what he will become the next moment. By the same token, every human being has the freedom to change at any instant.

—Viktor Frankl

Before we go further in our discussion, let's take a moment to define winning projects. Winning projects are those analytics projects that are widely understood by business executives, sponsors, subject matter experts, project review members, portfolio managers, finance professionals, and the analytics team. Winning projects are prioritized in the analytics backlog and chosen as areas of strategic and tactical importance to the success and improved operations of the company. The most important and selected analytics projects are then measured and compared to a wide range of other projects that the company needs to execute, and the final selection process is undertaken. The resulting projects have been carefully scoped...

Analytics self determination

In most business situations, people are directed to the projects that they will be undertaking. Of course, there are decisions to be made and leeway in how the project(s) will be accomplished and how progress will be made, but for the most part the projects that are to be undertaken are dictated; this is true for large and small projects. People are told to build a factory, or they are charged with upgrading the hardware and software of an application system, like an Enterprise Resource Planning (ERP) environment, or perhaps they are tasked with improving the user interface or data visualization for an application.

The point that I want to make is that not everyone has the option of selecting the projects that they or their team will undertake. Having a choice of the work that we and our teams will engage in is a fulfilling and energizing element of running an advanced analytics and artificial intelligence team. We should not take it for granted that...

Communicating the value of analytics

Let's take a look at the top three reasons why we find ourselves in this environment and discuss the best methods for ensuring that we, as analytics professionals, can make this work to our maximum benefit. Let's discuss the ways in which we can ensure that the executives, managers, and subject matter experts we work with understand the value of analytics.

Relative value of analytics

In my view, the majority of functional executives and managers are not aware of, or conversant in, the salient factors relating to the tradeoffs or relative value to be delivered between the possible or potential analytical projects that are being considered. This is an education issue and an issue related to the current time we live in.

Also, it is related to the level of knowledge and interest management and managers have in analytics in general. Most executives and many managers look at analytics projects as technology projects and, for all the reasons we have previously discussed in this book, they do not delve into the details of making an informed decision of which projects deliver the most immediate, and greater overall, value to the company.

Managers consider and evaluate options, such as hiring a third shift at a factory, and compare it to upgrading the SAP ERP system, or they compare the use of funds with the possibility of building an...

The value of analytics, made easy

Part of the issue lies with the leadership of the analytics team. Recently, I had a conversation with a group of subject matter experts, a project sponsor, and the analytics team members. The analytics team presented initial results from a test that was underway, they explained that we had seen positive and negative operating results from the test. I asked the team as a whole to highlight and extract the effects of the test into the following elemental components – naturally occurring business results; results from known, yet unrelated, operational changes; and results from external events (i.e. competitive activity, natural disasters, man-made or market changes) and from the tests that we designed and inserted into the operational mix. The people who fought the hardest against the request or directive to take the results down to the next level were the data scientists. The analytics team argued that there were no compelling reasons to tease...

Enabling understanding

We, as analytics professionals, often know what needs to be done, but the analytics team either does not want to do the work, because they have plenty of other work to do, or they think that the results are obvious and the further decomposition is not needed to understand the deeper dynamics and the driving forces underlying the high-level results. In the majority of cases, the data scientists rationalize their reasons for not wanting to undertake the further decomposition work because they can already see and understand the dynamics without the further effort, but the business users – the sponsor, who has to explain the project, value, and the need for change to the executives, and the subject matter experts who have to support the sponsor and also implement the required changes – do not understand the core dynamics at a level where they can change the operations in a meaningful way that maximizes value realization for the business as a whole...

Enterprise-class project selection process

The communication dynamic outlined in the previous section – the need to ensure that the functional team understands and is on board – is true and extends to the project selection process as well. Analytics leaders and professionals need to be decomposing projects down to a level where analytics projects can be compared on a like-for-like basis with competing projects and priorities. This is a track of work that we as analytics professionals need to improve on and hone our skills in.

Often analytics leaders and professionals do not want to create the materials or information for review by a project review board or by a portfolio management function because the projects may be rejected, or more information might be requested or the project scope may be changed, all resulting in more work for the analytics team. The bottom line is that this activity and the materials to support it are required by most organizations and must...

Understanding and communicating the value of projects

The second factor to call out is that the finance organization or the portfolio management function in the organization does not know how to calculate the relative value of the possible projects in the analytics portfolio very well or with the needed level of accuracy. I am not being harsh or demeaning to the finance or portfolio management teams with this remark. The analytics projects being discussed and undertaken are not well defined, or at least not as well defined as they could be. Analytics projects have a great deal of flex and flow in their definition.

It is paradoxical that the analytics team is not very good at defining the details of the project delivery, cost, timelines, and relative value of analytics projects. Given that the primary purpose of analytics projects is the measurement of performance or the prediction of future results, it would be expected that the analytics team would be better at this aspect of...

Delegation of decision making

The final factor that I see on a daily basis in the project selection process for analytics projects is the delegation of decisions down to lower levels of the organization; executive management delegates the decision-making authority for analytics projects to the senior managers, who in turn delegate to the functional managers, who, in the majority of cases, default to what the head of analytics wants to do, or recommends, to the functional managers.

In one of my previous roles, I spoke with a senior vice president about how to prioritize the company's investment in analytical applications. I suggested that we organize a cross-functional group of executives and senior managers to review the lists of projects and assist in making fair and equitable decisions regarding where the analytics team would focus their efforts. The person thought about it for a couple weeks and when I came back to revisit the discussion, the suggestion was to delegate...

Technical or organizational factors

For the remainder of this chapter, we will examine and discuss the factors that play into selecting winning projects. Our discussion will center on two areas of important factors – technical and organizational. We will address the technical factors first and then move on to the organizational factors.

Data – does it exist, and can you use it?

As we have discussed, we are data scientists and analytics professionals, not alchemists.

If there is no data that directly or even indirectly describes the operational aspect of the company or behavior of the entities – people, processes, products, machines, etc. – that we are to examine and analyze at least once, then the advanced analytics team cannot assist in the improvement of that process, operation, or area.

All analytics professionals experience this situation regularly: the discovery of the lack of data is often accompanied by a sense of surprise, and possibly...

Guidance to end users

When I refer to end users, this is an umbrella term that refers to all people who utilize the analytical models and applications that the analytics team builds and deploys. I am including executives, senior managers, functional managers, subject matter experts, and casual users in this category.

End users tend to overthink how an analytical application might be able to help them and their operational teams. Executives and senior managers will self-edit and cull ideas that could be very useful and valuable in how the analytics team thinks about and approaches solving an analytical challenge.

My guidance to end users is this: if you have an idea for how data and analytics can help you and your team, call me or e-mail me immediately. Don't design the solution, don't agonize about the profusion of data or the lack of data, don't think about the budget implications or how long it might take to build the analytical application – just call...

Where is the value in a project?

As I noted at the end of the Introduction section of this book, I am endlessly curious about all things – all people, anthropology, psychology, processes, products, history, science, economics, and more. One of the problems with being intrigued by a wide range of topics is that you may allow the analytics team to chase down projects and areas just because they are fun to learn about; this is not a good idea.

Of course, there will be topics and subjects that are tangentially related to the business problems and challenges that you and your colleagues are facing, and running down a few wild ideas never hurt anyone, but if it is a regular occurrence, the productivity of the team, and your credibility, will suffer.

It is always good to place context around analytics and analytics projects in the early stages of a relationship with an executive and business sponsor. They have pet projects and personal interests too. Recently, I was in a meeting...

Operational considerations

In the next three sections, we will discuss best practices to follow in your day-to-day actions in gaining consensus across the organization and ensuring that you and your team have the confidence of the executives, functional managers, and subject matter experts.

Selling a project – vision, value, or both?

As we have discussed, executives have a number of options to choose from when deciding where to spend the money of the company. Analytics projects, the analytics team, and the support for the overall analytics effort needs to be funded and supported.

What is the best path and method for selling the value of an analytics process and all the projects that are included in the total program to the executive level? My suggestion is that you sell the entire process and program and lay out the various major projects/phases that will be executed over time.

Executives are looking for impact from investments. It is best to start with the most expansive impact and headline possible. If you are proposing to transform a corporate function, like the supply chain, start with the broadest view that you can apply analytics to. The good thing for us is that you can apply analytics to all aspects of the supply chain, just as you can to...

Don't make all the decisions

Analytics professionals tend to be an independent lot. We are smart, driven, and can be insular and have a tendency to want to make decisions on our own and move forward quickly. We can see consensus building and group decision making as a hindrance to the speed of change and progress. Sounds like some of my past performance reviews, especially when I have worked at large companies like IBM and others.

In most cases, you and your analytics team members will know the right answers before any other people in the organization. You should: you and your team have been given the data, the resources, and the approval to examine operations, pricing, manufacturing, and more with the express purpose of improving these functional areas.

The challenge is that you and your team do not own the daily operations of those functional areas. Knowing what needs to be done is a prerequisite to action, but it does not predestine action. You and your team can know...

Do the subject matter experts know what "good" looks like?

For some of you, I'm sure that your reaction to the heading for this section is, "They are subject matter experts, and they should know what good looks like!" Well, perhaps not.

Subject matter experts are experts in what should be happening on a daily basis. They know how the plant should run, when the supply chain should deliver something, or when the process will complete. They are not experts in how much improvement can be wrung out of a process by using data and analytics.

There are too many stories to recount where the analytics team has undertaken a project and presented the results to the functional managers and subject matter experts only to have them sit in a meeting and ask, "Is that a good result?" I often say, or want to say, "We are presenting it, so, yes, it is a very good result." Most of the time, I do not say that or something like it, but if the team has...

The project mix – small and large

People have short attention spans. No surprise there, but what does that mean for you? How will you counteract the tendency of people to lose focus? How will you keep stakeholders and sponsors engaged with your team and in the projects you are undertaking?

The project mix has a great deal to do with stakeholder engagement, team satisfaction, and workload consistency. My experience shows that a senior data scientist can work on two major projects and multiple smaller projects, around two or three, all simultaneously.

When I first tried this idea with an analytics team, it was unanimous. The staff all came to me individually and explained that there was too much work and that it could not be done. I suggested to each team member that they try the new system. Also, I suggested that they keep an eye on work-life balance and to not increase their hours to try to accomplish the work more quickly.

The team found that the gating factors...

Opportunity and responsibility

We are living in a fantastic time to be responsible and accountable for an advanced analytics and artificial intelligence team.

We can pick and choose the programs, processes, and projects that we want to undertake. We can have influence across all the functional units of an organization on a global basis. We can work with front line employees across all functions of the entire company. We can engage with all levels of management, including executive management of the firm. We have an open invitation to go anywhere in the company and engage in a process or processes to improve operations incrementally or exponentially. This can be a heady position to be in, for sure.

Of course, as analytics professionals and leaders, we must live in the real world and drive improvements in operations and functions of the company. Our teams must be looking for practical improvements and methods and ways to implement and drive real and measurable change.

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Summary

We have a duty to teach our colleagues what can and cannot be accomplished with data and analytics. Analytics professionals and leaders need to be at the forefront of the discussion of what is a realistic project and expectations of that project and the teams that we have on staff.

We must explain and place context around our proposed programs, processes, and projects to ensure that our colleagues in the functional business units know what is expected of them – from involving their staff, to the spending of their budgets, to the timing of delivery of interim and final results.

Data is a critical element of all analytic efforts. Analytics professionals, in conjunction with the internal and external legal experts, must be the leading voices explaining and examining the internal and external data sources we will be using, integrating, and leveraging to develop insights into customers, patients, partners, operations, and more.

Analytics, legal, and business...