Optimizing Data-to-Learning-to-Action: The Modern Approach to Continuous Performance Improvement for Businesses

By Steven Flinn

Apply a powerful new approach and method that ensures continuous performance improvement for your business. You will learn how to determine and value the people, process, and technology-based solutions that will optimize your organization’s data-to-learning-to-action processes.

This book describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today’s business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector.

You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks. And, importantly, you will learn techniques for quantifying the value that can be attained by successfully addressing the bottlenecks, providing the credible support needed to make the right level of investments at the right place and at just the right time.

In today’s dynamic environment, with its never-ending stream of new, disruptive technologies that executives must consider (e.g., cloud computing, Internet of Things, AI/machine learning, business intelligence, enterprise social, etc., along with the associated big data generated), author Steven Flinn provides the comprehensive approach that is needed for making effective decisions about these technologies, underpinned by credibly quantified value.

What You’ll Learn

• Understand data-to-learning-to-action processes and their fundamental elements
  
• Discover the highest leverage data-to-learning-to-action processes in your organization
  
• Identify the key decisions that are associated with a data-to-learning-to-action process
  
• Know why it’s NOT all about data, but it IS all about decisions and learning
  
• Determine the value upside of enhanced learning that can improve decisions
  
• Work backwards from the decisions to determine the value constraints in data-to-learning-to-action processes
  
• Evaluate people, process, and technology-based solution options to address the constraints
  
• Quantify the expected value of each of the solution options and prioritize accordingly
  
• Implement, measure, and continuously improve by addressing the next constraints on value
  

Who This Book Is For

Business executives and managers seeking the next level of organizational performance, knowledge workers who want to maximize their impact, technology managers and practitioners who require a more effective means to prioritize technology options and deployments, technology providers who need a way to credibly quantify the value of their offerings, and consultants who are ready to build practices around the next big business performance paradigm

 

• Language: English
• Publisher: Apress
• Release date: Apr 6, 2018
• ISBN: 9781484235317

Book preview

© Steven Flinn 2018

Steven FlinnOptimizing Data-to-Learning-to-Actionhttps://doi.org/10.1007/978-1-4842-3531-7_1

1. Case for Action

Steven Flinn¹ 

(1)

Brenham, TX, Brenham, Texas, USA

Or more precisely, this chapter is all about the case for data-to-­learning-to-action! We touched on Why Now in the introduction—here, we will take a much deeper dive into that subject. A fundamental maxim of change management is that organizations will not take a new direction or take on a new approach unless there is genuine dissatisfaction within the organization with the status quo situation. This chapter provides plenty of reasons why there should be cause for concern about the current state and should help serve to get the optimizing data-to-learning-to-action approach off the ground in your organization.

The following are motivators for moving beyond the current state of business as usual that we will explore in detail this chapter:

A careful examination of the long-term trends of the economic results for firms, particularly in the United States, over the past several decades reveals a not-so-pretty picture, and the picture appears to generally be worsening.

The dizzying advances in information technology, while ultimately promising a tremendous upside, also create an intensifying level of complexity and confusion, too often leading to either organizational paralysis or wasteful spending.

Along with the baseline complexity and confusion that the rapid advances in technology are leaving in their wake, the level of confusion is amplified by the advocacy of players who promote their own agendas with respect to particular technologies or technology-based roles, further serving to inhibit clear thinking about business value.

The historical toolkit of management techniques aimed at improving business performance remains valuable, but fails to effectively address key performance-improvement issues that are relevant for organizations operating in today’s environment.

The Economic Imperative

Deloitte’s Center for the Edge has conducted a remarkable study over the past seven years or so that brings into sharp focus the economic issues that businesses are facing in the contemporary economic and technological environment. This study includes a periodically published set of metrics and accompanying commentary that Deloitte calls The Shift Index.¹

The Shift Index illustrates that, on the one hand, some US economic-performance indicators, such as productivity growth and overall GDP growth, have been increasing, and these positive indicators seemingly provide comfort with respect to overall economic performance. On the other hand, a deeper look reveals troubling systemic issues related to business performance—issues that the optimizing data-to-learning-to-action approach is geared to help address.

Perhaps most sobering is the long-term trend of the return on assets (ROA) for US firms since the mid-1960s, which is depicted by Figure 1-1, along with the associated linear trend line. The steady deterioration of ROA over the multiple decades is both obvious by inspection and alarming. Return on assets is defined in accounting terms as net income divided by assets. Basically, it can be considered a measurement of how effectively a company’s assets are being leveraged for economic benefit. The assets may be hard assets, such as plants and equipment, but also less tangible items, such as software and even cash holdings. In many ways, this decline in return on assets is particularly surprising because with the increasing proportion of services in the economic mix at the expense of hard assets, it would be expected that the ROA metric would benefit. Hard assets should be proportionally shrinking, and their value-add should therefore be increasing as a result of the ever-growing levels of services that are not part of the asset-based denominator but that do contribute to the net income in the numerator.

But, in fact, ROA has continued to decline in spite of the help it should be getting from this services-to-asset mix advantage. ROA is a fundamental—perhaps the fundamental—way to judge the overall economic performance of businesses, and therefore this deterioration needs to be taken very seriously, notwithstanding the gloss of seemingly benign economic news embodied by other, less fundamental, metrics.²

So, what is the root cause of this deterioration of ROA? Most fundamentally, the only way aggregate ROA can continue to decline is because decisions with respect to investments in, and operations of, assets are relatively poorer than they were historically. It’s as simple (and complex!) as that, since we know financial performance is highly correlated with decision effectiveness.³ In fact, particularly puzzling is that this decline in the economic performance of businesses is occurring in the face of a concurrent explosive growth and popularity of business schools, management-related publications, and the overall field of management consulting! How can that possibly be? The inescapable answer is that business decision making, in aggregate, must somehow be worse than it was historically, despite the concurrent growth in management-related education and advice.

../images/455323_1_En_1_Chapter/455323_1_En_1_Fig1_HTML.jpg

Figure 1-1.

US firms’ return on assets 1965–2015. Based on Deloitte/Compustat data.

Perhaps some insight into the paradox lies within a seemingly completely different puzzle in the field of medical diagnostics. Over the past few years there has been significant controversy about cancer screenings, particularly for prostate and breast cancers.⁴ On the one hand, it has traditionally seemed sensible to encourage such screenings, even though the screens are not perfectly reliable. That is, they are prone to some degree of false positives and false negatives. Nevertheless, the screens seem to at least provide useful clues that can then be followed up on, and a fundamental maxim of decision science is that information, even if it is not perfect, cannot be worse than not having the information at all. Or can it? When the all-type mortality of those screened was compared to that of those who were not screened, it was found that those people who had been screened on average had worse outcomes than those who had not been screened!⁵ How could that be? How could more information possibly be worse than less information? The only way that could be the case is if the information was somehow systematically misused. That is, while the screening information should in theory enable better decision making, it was, in fact, causing worse decision making than if no screening tests had been conducted. Specifically, over-treatment was apparently occurring, and treatments always carry their own risks, even if the risks are comparatively small.

In other words, although with sophisticated decision making the screening information would enable better treatment decisions to be made, resulting in comparatively better all-mortality outcomes, applying unsophisticated or downright poor decision making transformed what should have been something with a positive value into something with a negative value. Unfortunately, rather than tackle the root cause of the problem, which is clearly the decision-making process occurring downstream of the screening, various medical organizations essentially threw up their hands and recommended that in most circumstances the screenings simply not be performed at all!

The lesson from this unfortunate situation is that even positive advances, if they come along with complexity and uncertainties, can lead to value destruction rather than value creation if the associated decision-making processes are not up to the task. From the perspective of the overall economy, decisions are currently being made in an environment that is characterized by greater complexity, more rapid change, and more new uncertainties than ever before. And that makes for very challenging decision making, necessitating more thoughtful approaches if the advances are to lead to business value creation rather than value destruction.

Decomposing the aggregate returns on assets of Figure 1-1, it is noteworthy that even for companies in the top quartile, ROA is at least modestly falling, which implies that even these top-performing firms’ decisioning processes need work. And as illustrated in Figure 1-2, for the bottom quartile of companies, sub-par decision making results in ROA levels that are prone to being negative and that plunge dramatically during times of overall economic stress.

../images/455323_1_En_1_Chapter/455323_1_En_1_Fig2_HTML.jpg

Figure 1-2.

Bottom quartile of US firms’ return on assets 1965–2015. Based on Deloitte/Compustat data.

Chronically low returns on assets result in more than just ugly-looking accounting charts. In times of stress, it is the path to dramatic changes in competitive fortunes, perhaps even leading to business extinction events. This is well illustrated by Figure 1-3, which charts the Topple Rate, a metric of changing ranks among companies with greater than $100 million in annual revenue.⁶ Bucking the long-term trend, there has recently been a pause in the increasing rate of rank churn. The long-term trend of the topple rate suggests that we may merely be in a period of calm before the storm in that regard, however. For example, as we saw in Figure 1-2, ROA has continued to decline during this same recent period. It seems a reasonable conjecture that we are therefore set up for another spike in toppling as soon as the relatively benign economic period of the last few years inevitably comes to an end.

../images/455323_1_En_1_Chapter/455323_1_En_1_Fig3_HTML.jpg

Figure 1-3.

US company topple rate 1965–2015. Based on Deloitte/Compustat data.⁷

Other metrics associated with the Shift Index are broadly consistent with the unfortunate trends of return on assets and topple rates. Take, for example, the financial metric that ultimately matters most to the owners of enterprises—return on shareholder value. While rates have been maintained by the top quartile of performers, for the bottom quartile, shareholder value is being destroyed, and seemingly at an increasingly faster rate.

Ongoing inadequate returns on assets and the destruction of shareholder value can only occur when enterprise decision processes are suboptimal or, in the worst cases, just plain consistently faulty since, ultimately, a company’s value is just the sum of the decisions it makes and executes.⁸ And, of course, decisions tend to be suboptimal or faulty because learning is suboptimal or faulty. Which again leads us to the inevitable conclusion that optimizing data-to-learning-to-action is the way, and really the only sustainable way, to get out of the rut of sub-standard business performance.

Disruptive Technologies

As is the case in nature, disruptive forces and the resulting stress are the drivers of extinction events in the business world. Economic recessions are classic stressors that serve to clear out the corporate old, the young, and the infirmed, and that reality is reflected in some of the abrupt spikes and deep valleys depicted in the business-performance charts we just reviewed. But sometimes even forces that would seem on balance to be good things can be stressors as well, because they can mean significant change is required. And change, even when the change seems destined to result in a positive outcome, is most definitely an organizational stressor.

The rapid advances in technology we are currently experiencing represent just such outwardly attractive opportunities, but also represent potentially fatal stressors for organizations. The list of disruptive technology and related advances that enterprises now need to successfully navigate is formidable and just keeps growing. It can seem like a big, buzzing confusion, as illustrated by Figure 1-4.

../images/455323_1_En_1_Chapter/455323_1_En_1_Fig4_HTML.jpg

Figure 1-4.

IT-driven opportunity and confusion

When there is a buzzing confusion, inevitably decisions are going to be suboptimal. Fear, uncertainty, and herd mentality all play their part in sabotaging logical thinking. When whimsical decisions about information technology, data, and analytics are continuing favorite topics of Dilbert cartoons, you know there is a real problem!

The following is a brief look at just a few of the potentially disruptive technologies and related trends that are profoundly affecting today’s organizations, for better or worse.

Cloud Computing

The inevitable transition to cloud computing models brings with it significant efficiency benefits. Fixed costs are converted to variable costs. Upgrading to new features is a much more graceful process. There is less friction in accessing and sharing information. New capabilities can be delivered to users much more quickly.

Cloud computing has already disrupted entire sectors of the economy. Information technology companies themselves have been particularly affected. The model has enabled new entrants to quickly gain traction in the marketplace at the expense of traditional on-premises vendors. And incumbent software companies have had to meet this competitive threat by undertaking the onerous task of transforming their product lines from on-premises to cloud-first models, while navigating a revenue-model shift from up-front licensing fees to a subscription model. So, cloud computing–based decisions are increasingly fundamental to competitive-positioning decisions in some sectors of the economy—and getting it wrong can be fatal.

But cloud computing is also at the core of most decisions with respect to the internal information technology of businesses in general. These decisions are a fundamental part of any CIO’s job these days. Moving to the cloud brings with it all those tremendous advantages for the organization just described: upgrading to new features is much easier, and new capabilities can be delivered to users much more quickly—and can be more easily accessed by users without the IT organization’s involvement (which, of course, brings with it both positive and negative aspects).

Although these are significant advantages, they can be organizational stressors as well. It must be carefully thought through how new cloud-delivered capabilities are applied in practice. If they are applied haphazardly or without thorough thought of how they will integrate with the current technological or process environments and how they will integrate across cloud platforms, as well as how the associated changes will affect user communities, they can surely lead to negative rather than positive value. Cloud computing is the amplifier of the advantages that other technologies promise, but also the amplifier of the buzzing confusion that accompanies all the new technology options.

Internet of Things

The internet of things (IoT) denotes an intensely networked world in which every device is connected: clocks, refrigerators, vehicles, industrial sensors, medical implants, and so forth, in addition to the already ubiquitous smartphones. And it is a world awash in all the data that emanates from all these devices.

The IoT phenomenon affects every company that delivers things into the marketplace. The things must be increasingly intelligent and connected. In some cases, they may be self-propelled and embodied as a robotic apparatus. Difficult decisions must be made between the legacy things and these new IoT-based products. As in the case of IT vendors with respect to cloud computing, getting this transition right is a life-or-death decision process for product companies.

And for businesses that are the consumers of the products, determining when to convert from legacy equipment to smart, connected devices and equipment is difficult. There are not only timing considerations, but also integration considerations and, of course, an overlay of security and privacy issues to be considered as well. Simply being connected to the internet is only half the story. For example, how are the new devices integrated into an organization’s overall processes and IT infrastructure? Cross-vendor compatibility is always an issue. Is there a special-purpose application programming interface (API) for the new device, or does it obey an industry standard API? And how long can the standard be expected to