Ensuring Engagement with Business Professionals

Everyone has a plan until they get punched in the face.

—Mike Tyson

We have discussed engaging with and convincing executives that there is value in data and analytics. Now, let's turn our attention to the managerial levels of the organization below the C-Suite and above the front-line workers.

We are now interested in exploring and understanding all levels of management from senior management to front-line managers. In most organizations, these are the levels where work can be immediately stopped, delayed indefinitely, or approved and moved forward.

In this chapter, we will examine the most common causes of analytics not being developed or adopted. We will look at the variety of areas in which you will need to be armed with knowledge and expertise, to convince your functional stakeholders that these roadblocks need to be overcome. This will help them realize the potential and value of analytics...

Overcoming roadblocks to analytics adoption

Tom Davenport and his co-authors have offered the following observation: "More than a decade after the concept of big data became part of the lexicon, only a minority of companies have become insight-driven organizations..." [1]

In using Davenport's quote about the passing of a decade, I want to ensure that we understand that analytics and analytical approaches have been around for a significant period of time—certainly longer than the decade we have been talking about "big data" for. To be clear, we are not talking about big data and we have not been talking about big data anywhere in this book.

Davenport's quote is employed here to illustrate that data and analytics have been in our orbit for far longer than a decade. Data and analytics have been with us for centuries, and only in the past decade has there been widespread recognition and interest in serious and active utilization of data and...

Organizational culture

Currently, we are experiencing an intense wave of interest in the possible positive and negative impacts of advanced analytics and AI. We have seen waves similar to this in the past, but previous episodes have not been as widespread, nor have achievable and practical results been as possible to so many organizations. We certainly did not have as many success stories to convince people that analytics can make a difference in their lives, work, school, government, or communities.

Interest is required, but interest is not enough to drive action. We need to enter discussions with stakeholders and sponsors under the assumption that the advanced analytics systems being developed will be implemented and will change the way that operations are executed.

It seems odd for me to write this and probably strange for you to read it. You would never enter into a major technology project or process re-engineering effort thinking that the end users may or may not use...

Data or algorithms – the knee of the curve or the inflection point

We have reached, and passed, an inflection point in our engagement with data and analytics.

We have heard the arguments that algorithms and math are the seat or source of competitive advantage. We have been told that if you are smart, innovative, and own your own algorithm(s) or approach, then you have mastery over all your competitors and the road to success is nearly assured.

Also, we have heard, sometimes from the same experts who asserted the previous point, that the pathway to all success in analytics is through the ownership, control, management, and proactive use of data—large amounts of typically fast-moving data.

Why are we discussing data and algorithms now, as part of a chapter on organizational engagement? The topic is salient and relevant at this juncture in our discussion. I have had to refute the argument from functional managers numerous times, and I expect that you will as...

A managerial mindset

I completed my undergraduate degree in Computer Science (it was called Data Processing back then) in 1983. When people asked me what I was studying, I replied, "programming and computers." The most common reply to that was, "There is a future in that?" My son received his degree in Computer Science in 2018 and my daughter will receive her undergraduate degree in Information Management in 2022. A common joke that we share is, "Those computers; there's a future in working with them?"

It seems a silly observation, but think of it: there are still a significant number of executives and managers who graduated from their undergraduate programs in the 1980s and 1990s. Many of those people have a cursory understanding of computers and programming, and even less of an understanding of data and analytics.

Generally, people do not enthusiastically embrace what they do not understand. You won't find many, if any, of that generation...

The skills gap

Another real and present reason for the lack of progress in developing and leveraging data and analytics is the talent gap. The talent gap has been discussed widely in academic circles, within data science teams, and in the press that is oriented toward the science and engineering communities. In the popular press, the conversation is typically discussed in terms of the lack of students in STEM (Science, Technology, Engineering, and Math). There is definitely an issue with the lack of qualified students in this broader area, but when you narrow the focus, the problem for advanced analytics teams becomes even more acute.

Hiring managers cannot find and hire enough talented people to build, manage, and maintain a rich, growing, and evolving analytical ecosystem across all of the industries, geographies, and companies that want to build out their analytics capabilities.

Tom Davenport and his co-authors also explored organizational gating factors in the adoption...

Linear and non-linear thinking

Developing analytical applications is a creative endeavor.

The process is characterized by fits and starts, dead ends, sparks of brilliance, and moments of eureka. Who would not want that in their daily jobs? It turns out, lots of people would rather never have to do any of that in their daily role. They are linear thinkers. Linear thinkers are everywhere. You may be one of them. I am not. No matter—we can all work together.

Linear thinkers tend to see things in black and white terms. They want concrete and defined dates. They see the rules as being immutable. No gray areas, or very few gray areas for them. Again, no worries, we can all get along, if we are willing to listen to each other and collaborate.

Working with linear thinkers—and you will have to, because they are the majority of people who are successful in business today—is not as hard as it sounds. You simply need to spend much more time setting expectations...

Do you really need a budget?

Budgets are many things to many people. For most people, they are a way to keep score. Who has a bigger budget? How can you take money from someone else's budget? However, what if you didn't need a budget?

In an advanced analytics and AI team, you really do not need a discretionary budget. Of course, you need money to pay for salaries, incentive compensation, desks, office space, travel and entertainment, and setting up your team with the appropriate technologies, but after you have the team hired, the infrastructure built, and the software installed, do you need a big or any additional budget to be effective and to deliver results? Trust me, you don't.

I have had multiple roles where budgets were tight. Actually, I am having a hard time recalling where budgets were freewheeling and we were let free to spend whatever we wanted. The point is that budgets are always controlled and restricted.

Since the advanced analytics and AI...

Not big data but lots of small data

I am certain that my views in this area are informed and shaped by my early consulting experiences. I was fortunate enough to work with experienced professionals that knew how to acquire, integrate, and analyze a wide range of related but disparate datasets. They never worried about bringing together a couple of databases to create new information and analytics. They saw this activity as quite natural. As a consequence of spending so much time with them and seeing the incredibly powerful and insightful results, I too came to believe that integrating data together was an incredibly valuable activity that provided tools and insights that other people just did not see or even think of.

In the beginning, we would integrate datasets that were obviously related. We would integrate shipments information from the client company systems with the consumption data from grocery stores. Consumption data or scanner data from Nielsen and Information Resources...

Introductory projects

It is unclear in certain circumstances if the sponsoring stakeholders or subject matter experts are fully committed to the project that you are chartered to undertake.

Of course, they will say that they want to engage, and they will assign people to support the efforts of the advanced analytics and AI team, but they may not even know the implications of engaging in an analytics project. It is almost certainly the case that they do not know the full implications of engaging in the project and the results that will be shown.

I strongly suggest that the first project you do with a new functional area, a new manager, or an executive that you do not have an existing relationship with should be a short project.

Not short as in small in ambition or scope—you can definitely take on a significant issue or challenge for the company or cause—but you should break down the initial stage of the project into a piece of work that can be completed and...

Value realization

I have seen a wide range of reactions from organizations as their own advanced analytics teams have developed and delivered new findings and insights that illustrate the path to positive change in their operations.

A few years ago, I was talking with the manager of an advanced analytics team at one of the leading manufacturers of athletic clothing. The situation that was described was not surprising to me. The analytics team worked on projects for all functions of the company, but most of the projects that the advanced analytics team undertook were focused on marketing and sales. If the project findings supported what the sales and marketing leader believed and had planned to do, the results were embraced and used in building the case or executing the plan. If the analytical findings indicated that there was a better way to execute the plan, then the results were ignored, and the plan was executed as the sales and marketing leader intended. This happens on a...

Summary

Middle managers get bad-mouthed on a regular basis. I am sure that as you read this chapter, some of you may have felt that I was taking unfair aim at some of our middle management compatriots. I meant no harm or disrespect.

This chapter was all about understanding the roadblocks that you will encounter. I have encountered all of these issues and challenges multiple times in various organizations. I have included them because I am nearly 100% certain that you will encounter them as well.

I have described and discussed the issues as clearly as possible in hopes that you will think through them ahead of time and consider your team, the organization you are working for or consulting with, and the best method possible for explaining the challenge, the solution(s), and the benefits of moving past the current roadblock to your collaborators.

These issues can only be overcome in collaboration with the functional sponsors and stakeholders that you and your team are engaged...

Chapter 6 footnotes

1. Analytics and AI-driven enterprises thrive in the Age of With. The culture catalyst, July 25, 2019, Thomas H. Davenport, Jim Guszcza, Tim Smith, and Ben Stiller, https://www2.deloitte.com/us/en/insights/topics/analytics/insight-driven-organization.html
2. Ibid
3. Augmented Intelligence: The Business Power of Human–Machine Collaboration 1st Edition, November 1, 2019, Judith Hurwitz, Henry Morris, Candace Sidner, Daniel Kirsch, https://www.amazon.com/dp/0367184893/
4. University of Iowa Hospitals and Clinics Transforms the O.R. Accessible, predictive analytics results in 74% reduction in surgical infections. https://www.tibco.com/customers/university-iowa-hospitals-and-clinics
5. What Separates Analytical Leaders from Everyone Else. https://www.thelowdownblog.com/2020/02/what-separates-analytical-leaders-from.html
6. What Separates Analytical Leaders From Laggards?, February 3, 2020, Thomas H. Davenport, Nitin Mittal, and Irfan Saif,...