THE NEW ART OF PROBLEM SOLVING New Art of Problem Solvi… · The Art of Problem Solving (AoPS) isn’t a project, but rather, a system comprised of a series of habits, artifacts,

THE NEW ART OFPROBLEM SOLVING

A Guide to Your Decision Sciences Journey

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Table of Contents3 Introduction

3 Businesses Must Learn to Capitalize on Change

4 Where to Focus: The River of Reasonable Return

5 What it Means for Today’s Solution Options

6 New Paradigms are Needed

7 Mu Sigma's Art of Problem Solving

8 Plan for Outcomes and Transformation in Harmony

12 Map the Interactions Across the Problem Universe

13 Encode Problems to Promote the Best Design

14

Harmonize the Creation and Consumption of Analytics

16

Pursuing a New Art of Problem Solving – While Doing Your Day Job

17

Charting the Course

17

The Right Governance

19

The Right Culture

22

Summary and More Information

2020 Mu Sigma Proprietary Information


IntroductionMu Sigma has a singular purpose: To instill a fundamentally new approach to decision making in large enterprises,

one designed to not just match capitalize on it. We call

it a New Art of Problem Solving, and it’s our pleasure to describe the concept in this paper.

, which is designed with

Businesses Must Learn to Capitalize on ChangeBig data tops many lists of business priorities, as the growing volume, velocity, variety, veracity and volatility of data

continue to fuel anxiety in business leaders. But that anxiety is misplaced. It’s not these clichéd Vs that you should

worry about. After all, big data is a symptom not a cause. The root cause of our anxiety is something that’s been

around forever: Change. And change continues to accelerate, creating more complexity and more data for our

businesses in shorter cycles.

Change isn’t new. We only have to look to nature for guidance. If Earth’s 4.5 billion year existence were compressed to

one 24-hour period, modern humans would be only one second old.


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network that generates 22 billion minutes of online interaction each day. All industries are young. All business models

will continue to evolve rapidly.

learn to thrive in changing environments, organizations designed to capitalize on change will be more successful than

those who attempt to simply manage change.

understanding of the problem universe in which we operate. Then we’ll discuss how the solution space must

change as well.

Where to Focus: The River of Reasonable Return

We have a situation that is unique in the short history of humankind: An explosion of signals, more networks for

carrying those signals instantaneously to anywhere in the world, and a proliferation of market economies that reward

people for responding quickly to those signals. Decision supply chains now trump physical supply chains in terms of

importance, and data is now the new oil.

With so many signals and noise, the next question is, “Where

does one focus?” After all, not all problems are created equal,

and the problem space isn’t shifting at a leisurely pace. In this

climate, your organization must track two dimensions: The

frequency with which problems arise, and the impact of solving

them. And based on the resulting plots, identify where it makes

sense to allocate resources to develop solutions.

As we chart our results, three zones of opportunity become

visible (see Figure 1).

Figure 1: Categories of business problems as measured by Frequency and Impact

Barren Desert of Low Return

Fertile Lands of Disproportionate

Return



Most businesses want to focus on the upper right section of the graph, where the combination of payback and

time-consuming problems. Management consultants will ply their heuristic, judgment-based approaches in the part of

this zone where business impact is highest, while independent software vendors (ISVs) promote algorithmic solutions

in the part where frequency is highest.

But most problems occupy the middle of the graph, in what we call the “River of Reasonable Return.” And that’s also

between removing one or two massive boulders versus dredging millions of smaller pebbles. Exploding the big rocks

growing in terms of frequency. They’re also interconnected. In fact, we might envision some connections between just

those four questions above.

That’s why the River of Reasonable Return represents the richest opportunity – because of its problem density and the

myriad interactions between those problems. The impact of solving any one of these problems in isolation is too small to

– the greater the impact you’ll experience, especially when compared with trying to solve the big shiny problems.

What it Means for Today’s Solution Options

Swimming in the current of these muddy, interconnected problems, many organizations face some challenges. For

one, the data and analytics domain is still nascent – new platforms emerge seemingly weekly, and analytical processes

can be immature or fragmented across industries. And don’t forget the lingering lack of analytical talent.

When it comes to external resources, although traditional solution providers have an important role to play in

exposed in a climate of fast change. For instance, we know that consulting and its inherent project-to-project

approach, doesn’t scale. We know that corporate IT, under pressure to just “keep the lights on,” is not known for its

problem domain.

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integrated blend of math and business insights, tightly wound within software. Not “man + machine” but truly “man

machine.” Such a combination is a requirement for implementing our Art of Problem Solving System and something

we take great pride in at Mu Sigma.

New Paradigms are Needed

The scarcity of resources and inadequacy of solution options is just the start. A bigger problem is that, despite the fast

pace of change, many organizations rely on safe analytical paradigms.

of replicating it elsewhere. All too often this knowledge collects dust in KM repositories, loses relevance, and then

depreciates in value faster than that of a new automobile.

ideas across your company and your competitors.

The modes of thinking mentioned above might help you keep up with change, but they won’t help you capitalize

on that change. As such, they represent the opposite of what’s needed to thrive in today’s fast pace of change and

muddy, interconnected problems. Enterprises should instead follow these three principles:

1. Learning over Knowing. Rather than compete on knowledge that constantly breaks down, better

companies will compete on an ability to learn, where learning is the rate of change of that knowledge. The

best companies will compete on their speed and adaptability of learning.

2. Extreme Experimentation. Rather than rely on experts or expertize, rely on experiments. We are like

monkeys throwing darts at a moving dartboard. Our hubris wants us to believe that we can learn to throw

darts with greater accuracy. But if we can bring down the cost of experimentation – throwing many more

z



3. The New IP. Enough with isolated, protected approaches built around intellectual property. The new “IP”

stands for “interdisciplinary perspectives.” It’s open and wants to connect, as opposed to closed. The new IP

These three guiding principles underpin all that we do and believe at Mu Sigma. They serve as the bedrock on which

Mu Sigma's Art of Problem Solving is built.

Mu Sigma's Art of Problem SolvingThe Art of Problem Solving (AoPS) isn’t a project, but rather, a system comprised of a series of habits, artifacts, and

software – each of which must be continuously sharpened, but also applied daily to your problem solving work

(see Figure 2).

The four habits of Mu Sigma's Art of Problem Solving

Mu Sigma's Art of Problem

Solving



s of excellence (CoEs) or governing councils,

decentralized governance models. We discuss governance models later in this paper.

through mapping work, then into problem design, and ultimately execution (see Figure 3).

Don’t misconstrue this as a simple linear journey, however. Another way to visualize the habits and their companion

artifacts is as a spiral, where the further outside on the connected ring, the more broad we are in our thinking,

ultimately zeroing in on concrete actions and making the promise of Big Data real. No matter how you visualize it,

tight feedback loops will be required throughout.

If followed closely, AoPS will provide your company with the impetus to move beyond just mitigating the risks of

change, to actually taking advantage of those risks. In the following sections we review each habit and its companion

Mu Sigma artifact, but keep their treatment at a relatively high level.

1: Plan for Outcomes and Transformation in Harmony

Mu Sigma artifact: muOBITM

a course toward realizing measurable outcomes and transformative change. Figure 4 maps both dimensions to a

continuum, using an example many can relate to: Health.

muDSC

Harmonize the Creation and Consumption of Analytics

Figure 3: Habits and their artifacts aligned with a traditional lifecycle



be successful, one needs to constantly push boundaries laterally and seek transformation, but at the same time focus

on realizing measurable outcomes along the way.

The Vertical Axis: Planning for Outcomes

such a process, analytical teams typically begin bottoms-up as an

form of measurable outcomes.

Unfortunately, it’s all too common for the upfront planning around a

business problem to follow the same sequence – that is, beginning with

data. Instead, planning must be orchestrated as a top-down, deductive

process, beginning with desired outcomes. Hence the planning and

execution can be thought of as two macro cycles moving in opposite

direction, as illustrated in Figure 5.

Figure 4: Planning (deductive) and execution (inductive) activities managed together

Figure 5: Planning (deductive) and execution (inductive) activities managed together



In our planning, we associate each business problem or network of business problems to be solved with a set of

design” process that backs into desired outcomes.

outcomes as derivatives of actions. But the “rational human” theories that have dominated economic and

management science theory, on which this traditional approach is based, have been disproven.

Simply put, human beings don’t always make rational choices. The actions they take come more from their behaviors

and habits

analyses to shape, and the insights needed to trigger those behaviors. In short, when trying to solve a problem, you

must strive to humanize the insights.

The Horizontal Axis: Planning for Transformation

The horizontal axis of the muOBITM grid recognizes that individual outcomes, behaviors, and insights themselves live on

a continuum. The continuum represents maturity and level of impact, ranging from the most basic on the left to the

most transformative on the right.

The path taken on the continuum is important (see Figure 6). Top-down thinkers usually peg themselves to driving

transformation and might miss out on the more basic raw materials, or products and services, needed to reach that

transformation. Bottom-up thinkers usually peg themselves to identifying the most basic and fundamental of solutions.

muOBITM nudges one to think about the entire journey towards transformation as a series of “Goldilocks state” steps.

Furthermore, we believe that the most value is, in fact, created not in the end zones, but in the region in the very middle.

Figure 6: The muOBITM artifact leads us to abductive conclusions

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Traversing the Two Dimensions of the muOBITM

The muOBITM seeks harmonized progress along the dimensions of both realization and transformation. But how should

one traverse this journey during execution?

transformation. This is typically how a functional business unit would go about things – using a very sequential

manner. But this approach prevents the seeds of transformation from being sown early enough and won’t create an

This process develops a constraint-driven mentality where goals are lost sight of, and there’s a clear risk of getting

bogged down.

The right way to move through this framework is by moving one step towards higher realization then coming one

step down to better transformation (see Figure 7). This movement will create some chaos or innovation and give up

on some order or realization. Order will be brought back by moving again to one higher step in the realization, and

then back again. This is what we mean by “Goldilocks states” – in this case between order and chaos. Move towards

the desired current outcomes constantly, but at the same time lay foundation for the next transformational result.

Figure 7: Traversing the muOBITM



2: Map the Interactions Across the Problem Universe

Mu Sigma artifact: muUniverseTM

Too often, analytics professionals get locked into a particular problem, seeing only their piece of the puzzle at a particular

point in time. But business problems – especially those in the River of Reasonable Return – are always interconnected.

There are two ways to take advantage of understanding problem interactions within AoPS: A bottoms-up manner and

a top-down manner.

problems. For example, a problem that’s seemingly rooted in email marketing might have deep connections with

problems like messaging relevance, customer retention, loyalty programs and targeting accuracy, to name just a few.

Understanding those interactions will help you solve the email marketing problem better. Furthermore, the nature of

these connections will help analytics teams uncover the downstream risks of approaching a problem a certain way, as

The top-down approach, using the muUniverseTM planning and visualization tool (see Figure 8), allows us to create a

problem universe, or a galaxy within that universe (e.g. revenue management), and build out the problems and their

connections to each other. Doing so will allow analytics functions to prioritize and defend their roadmap, using not

only the relationships, but the common language metadata encoded in muPDNATM. Furthermore, you can assess the

completeness of your muUniverseTM and discover gaps that might be your next data-powered opportunities.

Figure 8: Screenshot from the muUniverseTM planning and visualization tool



Upon closer inspection, you’ll see that the bottoms-up approach is algorithmic and machine-oriented, while the

top-down approach is more heuristic and manual. The muUniverseTM application enables us to harmonize these two

important perspectives.

same way. There’s an element of distance, which accounts for how closely related the two problems are. We also

use the thickness of the line connecting two nodes to describe the extent to which they share common business

objectives, factors, questions, hypotheses and data elements. These characteristics, along with the fact that our

muPDNATM application is embedded within muUniverseTM, will help you instill a common language for problem solving

across your organization.

3: Encode Problems to Promote the Best Design

Mu Sigma artifact: muPDNATM

The dynamic nature of businesses forces organizations to solve multiple problems on a daily basis, especially those

is required.

Asking the right questions is also key in this step. Let’s face it, traditional education and corporate performance systems

change, questions are more important than answers.

At Mu Sigma, we consider business problems to have an underlying DNA. In fact, the artifact muPDNATM stands for

“Problem DNA.” By encoding a problem’s DNA early in the process, we convert it into a construct or common language,

that can be shared across the organization and retrieved for use in future work. We encode each of the thousands of

breaks into three parts:

» A design element, which pushes us to front-load our thinking and have constructive debates earlier on in

for the business problem, so as to get to the right key questions. This element is most important, as it

captures our design for tackling the problem.

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» A representation element, which helps us visualize all the dimensions of the problem to ensure maximum

» A hypothesis element, which enables us to comb through the potential solution space to assess the

hypothesis exists to strengthen the design.

The muPDNATM activity and software application places a premium on asking the right question in the right way, with the

least amount of misinterpretation – rather than jumping to answers too quickly – and on asking questions that are both

forces problem solvers to be more deliberate and more top-down in nature – again, by design to inform the best design.

4: Harmonize the Creation and Consumption of Analytics

Mu Sigma artifact: muDSCTM

In a world where data is the “new oil,” organizations must manage the decision supply chain with the same rigor and

discipline that their compatriots in manufacturing do with physical chains.

Each day, thousands of decision supply chains operate continuously and simultaneously in your enterprise – whether you

recognize them or not (see Figure 10). Decision supply chains are similar to their physical brethren. You need to retrieve

can help accelerate your organization’s journey from data- or analytics-orientation to true decision sciences.

TM



Figure 10: The decision supply chain

One key tenet of managing a decision supply chain using the muDSCTM artifact is to ensure the harmony of analytics

creation and consumption. Too often, there’s a bias toward just creating analytics. For instance, a large retailer used

analytics to come up with a sound CRM framework. They procured the right customer behavior data, engaging

consultants to mine the data and build predictive models. But they didn’t align their models with the campaign

and their budget was cut. A proper decision supply chain would have forced the team to give early consideration to how

the insights would be used and consumed (see Figure 11).

A harmony of consumption and creation means that when groups create new analytics, they obsess over how those

analytics will be consumed. And when consuming analytics at the end of chain, they obsess on how to improve upon

and design new analytics.

Figure 11: Illustration from muDSCTM artifact, showing an emphasis on Creation activities

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Pursuing a New Art of Problem Solving – While Doing Your Day Job

Let’s explore the important relationship between the art and day-to-day problem solving. Again, consider nature as

a good metaphor. Research suggests that the extinction of any species is inversely proportional to that species’ ability to thrive. And to thrive, a species needs three things: Scale (purely volume), diversity (e.g., heterogeneity in its blood

lines) and symbiotic interactions with its habitat.

The same requirements apply to problem solving in your organization. As an organization, you want to solve many,

many problems. You need an approach that scales. At the same time, you want to solve a wide diversity of problems.

work to scale. That’s where the AoPS comes in – to help scale diverse problem solving and to diversify your scale.

And most importantly, to promote symbiotic interactions between species and habitat (see Figure 12).

between the two serve to strengthen the species’ ability to evolve and thrive in change. It’s the artifacts and software

outlined previously that provide the connection between the two strands.

To make it more tangible, here are some examples of what we mean.

Figure 12: Artifacts nurture a symbiotic relationship between AoPS and day-to-day problem solving

Whether a centralized analytics function or some other group serves as steward for the AoPS, it’s important that you

to-day problem solving and sustains better decision making going forward. It’s also important to understand that the

strands, coming together at periodic intervals.



» An AoPS group works with frontline teams to create problem DNAs for each business problem they

encounter, pushing them to ask better questions in the process.

» An internal analytics team uses the decision supply chain construct at the problem level to ensure that

activities are in place to drive the consumption of analytics by the business.

»are involved and that the target outcomes account for downstream relationships.

»

» An analyst looks across multiple decision supply chains to identify common attributes of projects that

materially drive consumption, feeding those insights back to a central team.

Charting the CourseThis section addresses how to organize and govern an environment where the AoPS and the day-to-day grind of

nurture both habitat and species.

The Right Governance

A decision sciences program can be built on one of three constructs, each with pros and cons (see Figure 13):

Figure 13: Pros and cons of analytics governance models

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-

short-term focused

TM



» Centralized. A single team owns the data and serves all analytical needs across business functions. While

this carries the promise of an integrated data infrastructure and economies of scale, it won’t provide the

» Decentralized. Each business function owns its data infrastructure and analytics team. While this model

ensures agility and a fast start to decision sciences, it fosters the proliferation of redundant tools and

» Federated. This model seeks the advantages of both the centralized and decentralized approaches.

day problems, a governing council ensures broad alignment on data policies and infrastructure, as well as

the AoPS habits and artifacts. The analytics teams embedded in the business apply these things, but also

provide feedback to improve them.

payback, stands to foster the most collaboration and is best suited for an Art of Problem Solving approach.

discussed later.

Figure 14: Federated governance models are designed to foster cross-organizational collaboration

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The Right Culture

An environment conducive to the New Art of Problem Solving, and an ability to capitalize on change rather than fear,

some of which we readily pull from prior art:

1. A growth mindset. Most organizations look at big data as a collection of datasets to be harvested, skillsets

datasets.

2. Feedback loops.

upward trajectory of improvement (see Figure 15).

Figure 15:

Carol S. Dweck.



won’t uncover those problems with the potential to derail you.

Continuous work streams. Organizations looking to institute a New Art of Problem Solving shouldn’t

think about analytics as a project or initiative, but rather as an ongoing activity. After all, learning is a

every function within the organization and get inculcated into its DNA. A continuous mindset has a better

chance of doing this than a series of projects. Furthermore, a continuous work stream environment,

supported by the right governance model, will do a better job at uncovering latent needs, new ways of

thinking and opportunities for experimentation.

This also relates to how you should think about your problem space. The RoRR and the interconnected

nature of its business problems demand a continuous approach. In such an environment, thinking about

4. Constant prototyping.

it’s historically worked well because the business processes being automated were relatively static. But in a world

demands creativity, agility, and responsiveness, while IT has responded with process, security, and standardization.

anything but static. In decision sciences, the journey from problem to sustained solution goes through a

series of phases as indicated in Figure 16:

Figure 16: Stages of the analytics solution journey



But it’s important to recognize that not all solutions will go through this path completely. Some short

constant prototyping.

5. Signals of dysfunctional behavior. Builders of decision sciences groups must appreciate that they are,

in fact, creating teams of individuals from possibly disparate backgrounds who must work together toward

common goals. Hence, it falls upon the leaders to watch out for signs of team dysfunction. According to

accountability avoidance and inattention to results. Be sure to design mechanisms that signal dysfunctional

behavior, and develop corrective interventions.



This document is confidential and contains proprietary information belonging to Mu Sigma. Neither this document nor any of the information contained herein may be reproduced or disclosed under any circumstances without the express written permission of Mu Sigma.

Summary and More InformationAt Mu Sigma, we believe that organizations needn’t try to cope with or manage change, but should strive to capitalize

on it. You should have a hold on the roadmap of your organization’s analytical problems. We want your work to drive

We help enterprises institutionalize data-driven decision making and harness the potential of Big Data.

The purpose of our New Art of Problem Solving System is to instill large enterprises with a new approach to decision

structured problem solving and insight generation approaches supported by guided, white-box analytics.

For more information, visit www.mu-sigma.com or write to us at [email protected]

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companies approach problem solving in areas of marketing, risk and supply chain. With more than

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THE NEW ART OF PROBLEM SOLVING New Art of Problem Solvi… · The Art of Problem Solving (AoPS) isn’t a project, but rather, a system comprised of a series of habits, artifacts,