SAS Best Practices white paper: An examination of your ...€¦ · a SAS Best Practices white paper of an ANALYTIC ENTERPRISE ... a competitive advantage. ... but companies like Pandora

by Kimberly NEVALA

best practices

T H O U G H T P R O V O K I N G B U S I N E S S

business ANALYTICS

a SAS Best Practices white paperof an ANALYTIC ENTERPRISEANATOMY

an EXAMINATION of your company’s ANALYTIC physique

Anatomy of an Analytic Enterprisebusiness ANALYTICS

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INTRODUCTION ...................................................................................... 4

It’s Not How Much Data You Have .......................................................... 4

It’s How You Use It .................................................................................. 4

THE ANALYTIC ENTERPRISE ................................................................. 5

THE ANALYTIC MINDSET ....................................................................... 6

We Understand Our Business ................................................................. 7

Remember When? .................................................................................. 7

This is How We Do It............................................................................... 8

MAKING THE CASE: Inciting passion & enlisting commitment .......... 10

Informing and Enabling Business Strategy ............................................ 10

Finding Your 1 Percent .......................................................................... 10

DEVELOPING YOUR ANALYTIC MUSCLE ........................................... 12

The Analytic Process............................................................................. 12

The Analytic Community: More Than A Data Scientist ........................... 13

The Enabling Technology ...................................................................... 14

table of CONTENTS

Anatomy of an Analytic Enterprise business ANALYTICS

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MANAGING THE CHANGE : Organizing for success ........................... 15

Strategic ............................................................................................... 15

Collaborative ......................................................................................... 15

Influential............................................................................................... 16

Skilled ................................................................................................... 16

Results Oriented ................................................................................... 16

CONCLUSION ........................................................................................ 17


4

It’s Not How Much Data You Have

Driven by the big data movement, many conversations about creating a data-driven or-ganization begin by focusing on the acquisition and storage of data in all its forms. Data lakes are being created and data galore is flowing in. The issue immediately becomes: What could and should be done with all that data?

Side note: Storage is cheap. That doesn’t mean managing and maintaining the data comes free.

In the end, the organization with the most data does not win. It is the organization that does the most with its data that will ultimately prevail. And herein lies the crux of the is-sue.

It’s How You Use It

The case for analytics as a competitive differentiator is broadly established. Harken back to at least 2008 when Competing on Analytics: The New Science of Winning was pub-lished and subsequently cited by CIO magazine as one of the top 15 most groundbreak-ing business management books.

Unfortunately, awareness – in theory – does not automatically translate into practice in reality. In fact, emerging research continues to highlight a growing gap between the maturity of analytics in an organization and the ability to translate analytic results into intelligent decisions.

The CIO of a major health care provider stated this point simply: “We’re good at data crunching. Not so good at making decisions.” A number of reports and studies support this statement.

A 2013 Beacon Report by Meritalk on big data in government found that only 60 percent of federal organizations use the data they collect today. And only 40 percent use that data to make strategic decisions.

Forrester reported similar findings in a 2013 business intelligence and big data survey in which 54 percent of respondents reported they were successful or very successful mak-ing informed decisions. Yet only 28 percent reported they were using information to gain a competitive advantage. Disconcerting, given the preponderance of evidence that a company’s ability to survive and thrive in the digital economy is proportional to its ability to use information effectively.

INTRODUCTION

In the end, the organization with the most data does not win.


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Becoming an analytic enterprise and embracing data-driven decision making requires more than analytic tools. Or Hadoop. Or a data scientist.

In a keynote address at the Analytics 2013 conference, Dr. Will Hakes discussed leading companies who truly compete on analytics. Not just the tech titans such as Amazon and Google, but companies like Pandora and eHarmony whose business models are predi-cated upon analytics to find and deliver the answer to some straightforward questions. On an ongoing basis. And for a profit. He called out “analytic DNA” as the basis for each company’s go-to-market concept and success.

But can an organization that didn’t spring from analytic DNA develop some? Let’s ex-plore the anatomy of an analytic enterprise and review some key characteristics and tactics organizations must adopt in order to become data-driven, analytic competitors.

THE ANALYTIC ENTERPRISE

MIND MUSCLEHEART

figure 1. dimensions of an analytic enterprise


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Analytic innovators exhibit common attitudes and traits. Most notably: clarity of vision, a willingness – indeed, a mandate – to constantly challenge prevailing ideas and wisdom, and the will to follow where the data leads. Organizations not gifted with analytic DNA must cultivate a similar mindset. How? By providing education on the art of the possible and overcoming inherent organizational biases. The first is simple. The latter less so.

Organizations, like individuals, have memories. These memories, and the behaviors they influence, tend to be long-lived. Rethinking the playing field (or even considering the pos-sibility) requires acknowledging and breaking down entrenched attitudes and behaviors. Consider how three types of memory – semantic, procedural and episodic – influence corporate decision making.

THE ANALYTICMINDSET

“WE KNOWOUR BUSINESS.”

“THIS IS HOWWE DO THINGS.”

“REMEMBERWHEN...?”

semantic memory episodic memory

procedural memory

figure 2. barriers to an analytic mindset

• Semantic Memory: Knowledge of ideas, facts and concepts not related to specific experiences.

• Episodic Memory: Knowledge of specific events and experiences complete with emotional context.

• Procedural Memory: Implicit or unconscious knowledge of behaviors, habits or skills: how to.

Author’s note: I am not a neuroscientist. Nor do I play one at SAS. If I have taken liberties with these concepts, the error is mine. Regardless, you’ll get the gist.


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We Understand Our Business

Semantic memory supplies our knowledge of ideas, facts and concepts independent of specific events or experiences. And it is, historically, the foundation on which corporate seniority is based and decision-making authority is conferred. We’re rewarded, and in fact promoted, based on our ability to understand the playing field, intuit the next best move or make the right call.

Simply put, business expertise is grounded – although not solely dependent – upon our mastery of business semantics. But what happens when the landscape changes and new ideas enter the fray? Or historical operating parameters or assumptions no longer hold true?

Malcolm Gladwell, the author of Outliers and The Tipping Point, asserts that many cata-strophic business failures have been caused by errors of expertise. And while data-driven decision making cannot eliminate errors of expertise entirely, a willingness to chal-lenge ingrained ideas is certainly a hedge against them.

This is not to say, of course, that seniority is dead. Data-driven decision makers utilize their hard won knowledge and business savvy to best effect. Not by predicting the future and then finding numbers to support it. Rather, they use their hard-won understanding to home in on the right questions to ask and areas to explore. Ironically, data-driven deci-sion makers are comfortable with and even court ambiguity. And last but not least, they have the willingness to learn from and act on the information received.

It should be noted that overturning the inherent bias toward the HiPPO (the highest paid person’s opinion – Andrew McAfee) is not just an executive or senior management challenge. Data-driven decision making requires organizations to think differently – as a collective – and modify the processes and pathways by which questions are raised and decisions are made. Fostering an analytic mindset and implementing data-driven deci-sion making is a collaborative effort.

Remember When?

This brings us to the next barrier to embracing a no-holds barred approach to analytic discovery and the controversial insights that often ensue. Episodic memory, the ghost of experiences past complete with emotional context: That was great, bad or other!

Data-driven decision makers are comfortable

with and even court ambiguity.


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Yes, organizations herald and reward spectacular success. But in the long run it’s the failures and even the near misses where the lessons lie. In the realm of analytics this includes the specter of historical business intelligence and analytic investments or data warehouse projects that ended with a fizzle. Or worse, never ended – but never created value either.

Ultimately, analytics is about exploration. And whether drilling for oil, gold or the next business insight, it can take a lot of holes to find the proverbial nugget. As a result, adopting an analytic mindset often requires organizations to redefine success.

Enter the concept of failing to succeed. In the analytic enterprise, disproving a hypoth-esis or getting a null result doesn’t translate to a failing mark. Analytic organizations recognize that results are neither good nor bad. They are just results. And every result (or the perceived lack thereof) is a clue to what to investigate or not investigate, do or not do next.

Of course, this assumes that each analytic experiment arrives at a result. Any result. Yes, analytic enterprises embrace the fact that failure is a requisite part of success. But they also ensure analytic practices are disciplined enough to redirect attention and refocus efforts before too many resources have been consumed in vain. This practice is often referred to as “failing fast.”

Perhaps John W. Holt Jr., co-author of Celebrate Your Mistakes, said it best: “If you’re not making mistakes, you’re not taking risks, and that means you’re not going anywhere. The key is to make mistakes faster than the competition so you have more chances to learn and win.”

This is How We Do It

When in doubt, organizations, like individuals, operate by rote, falling back into routines they may not even be conscious of. This is procedural memory – the unconscious knowl-edge of past behaviors and habits – at work. For example, an individual with amnesia may not be able to recall his or her name, but can sign on the dotted line without think-ing. Or play the piano with no memory of learning.

When it comes to our business practices the analogy applies. In lieu of a conscious and concerted effort to challenge the status quo, things proceed as they ever were. Estab-lished business practices and processes are held inviolate – regardless of whether the original business justification or drivers (if they are even known) still apply. In the IT world this equates to the legacy system that can’t be phased out because no one can remem-ber what it does or how it does it.

Adopting an analytic mindset often requires organizations to redefine success.


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Creating a data-driven culture requires companies to cultivate a mindset that both ac-knowledges and is open to the need for change. A deliberate plan of attack – complete with incentives – is also necessary to make it stick.

While few companies have the luxury of starting with a clean slate, rethinking ingrained beliefs and practices doesn’t necessarily require razing existing operating models to the ground. It does, however, require companies to constantly challenge themselves: Is this the best it can be? Is there another way? What do I not know today that I could use tomorrow?

In many cases, there is much to be learned from simply taking a peek outside traditional industry or market boundaries. As an example, government agencies are taking a page from the public sector and retailers in particular. These agencies have begun using a broad array of data sources to segment their customers (previously known as citizens and the populations they serve) and tailor their interactions accordingly. Utilizing this approach, revenue agencies in Europe have reported up to a 10 percent increase in rev-enue collected. This is a significant chunk of change that can – let us hope – be used to further the public good.

There is much to be learned

from simply peeking outside

traditional industry

boundaries.


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Analytic enterprises manage analytics as a core business practice no different from sales or marketing. And like established business functions, analytics must enable and con-tribute to the company’s core goals and objectives. It’s a point worth repeating.

Interesting models are, well, interesting. But if the business doesn’t see the value or isn’t willing to put action to insight, the exercise is moot.

Informing and Enabling Business Strategy

Analytic innovators gain commitment (and fans) by speaking to the business in its own terms about the things that matter to it. Or, as one executive said: “[They] follow the money.”

To that end, the analytic enterprise is hyper focused on analytic projects and capabilities that will:

• Inform corporate strategy.

• Enable or drive operational execution and decision making.

• Stimulate innovation.

Developing an analytic agenda that addresses these needs without veering far afield isn’t as hard as it might seem.

Most organizations have well-defined processes for corporate strategy development and operational planning. Aligning analytic programs and research projects with identi-fied business objectives and goals is the single best way to achieve this goal.

Finding Your 1 Percent

Of course, for most organizations the issue isn’t necessarily finding a problem to solve. It’s picking just one. Or two.

Analytic competitors are masters of focus, concentrating their attention on questions that tie directly to what makes them tick. That being said, it isn’t necessary to solve all your company’s woes in a single pass. When thinking about where to start and what to do next, the following analogy is useful.

MAKING THE CASEinciting passion & enlisting commitment

Analytics must enable and contribute to the company’s core goals and objectives.


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GE coined the term “industrial Internet” to encapsulate the combination of intelligent machines with advanced analytics and enlightened subject matter experts (SME) to op-timize how people work. GE’s hypothesis is that relatively small changes – single-digit improvements in operational effectiveness and efficiency – can drive HUGE economic and social benefit.

The savings GE projects across industries are impressive. But the real point is to ask yourself and your business partners: What’s our 1 percent? It really can be that simple. And effective.

A multinational insurance company focused on optimizing its processes to highlight claims that appear low-risk but result in costly litigation later. How? They combine and visualize:

• Customer profiles and demographics.

• Behavioral data from call logs, Web visits and social media up to and at the time a claim is submitted.

• Relationships between individuals and companies suspected of fraudulent practices via social network analysis.

The payoff? A 1 percent reduction in the billions of dollars spent annually to settle work-ers’ compensation claims.

Is this innovation in the groundbreaking, state-of-the-art, never-before-seen sense of the word? It’s safe to say no. But it is a new way of doing business. And tens of millions in retained revenue isn’t chump change. More importantly, the project was a powerful proof point for the power of analytics and data-driven decision making. Bigger, cutting-edge projects followed suit.

What’s your 1 percent?


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An appropriate mindset and commitment to the analytics cause is for naught if your or-ganization doesn’t have the ability to execute on the vision. To succeed you must devel-op and nurture the skills, processes and tools that enable data-driven decision making.

The Analytic Process

Data-driven decision making is about much more than just development of an analytic model. And yet, as shown in the figure , discussions regarding the analytic life cycle have typically focused on the process by which the analytic model will be created and man-aged (orange), almost to the exclusion of the overarching business process and decision points (blue) that surround it.

DEVELOPING YOUR ANALYTIC MUSCLE

IDENTIFY /FORMULATEPROBLEM

DATAPREPARATION

DATAEXPLORATION

TRANSFORM& SELECT

BUILDMODEL

VALIDATEMODEL

DEPLOYMODEL

EVALUATE /MONITORRESULTS

ANALYTIC BUSINESS PROCESS

ANALYTIC MODEL LIFECYCLE

business hypothesisor problem space

probable businessvalue & utility

• go/no go• “go to market” strategy

• update policies & SOP• business process & application integration

• monitor business outcomes• refine business processes

figure 3. analytic business process


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But as hard as finding the nugget is, moving from insight to action is harder. Turning in-sight into action requires a holistic approach to both creation of analytic models and the care and feeding of the insights they provide.

Analytic leaders address data-driven decision making as an integrated business pro-cess.

The Analytic Community: More Than A Data Scientist

There is a lot of talk about the shortage of trained analytic workers. In 2013 Gartner pre-dicted the creation of 5.5 million data scientist-related jobs in the next five years – and predicted only 30 percent would be filled. The numbers are staggering. But the fact re-mains that a data scientist in a vacuum does not an intelligent organization make.

As shown above, data-driven decision making requires input and participation from us-ers across the spectrum, from executives to business analysts. Change management is also critical, as enterprises new to analytics must:

• Educate executives and decision makers on the benefits and enlist their sup-port.

• Enculturate analytic skills and awareness from the C-suite to the feet on the street.

• Proactively engage business owners to identify areas for investigation and to apply found insight.

• Redefine existing decision-making practices and protocols within both op-erational and strategic business processes.

Data scientists are a unique breed and possess a necessary and rare skill set. But in the analytic enterprise, executives, business decision makers and knowledge workers also need to step up to the plate.

A data scientist in a vacuum does not an

intelligent organization

make.


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The Enabling Technology

The analytic enterprise recognizes that data-driven decision making requires input and participation from multiple user communities. It also recognizes that the enabling tech-nology is not one solution, but an ecosystem in which different solutions enable different functions and stages of the analytic life cycle. The good news here is that the technolo-gies to support the full life cycle now exist. And this is really the big deal about big data. So technology, while a key ingredient, is no longer the key barrier to data-driven decision making.

DELIVERY & CONSUMPTION

ANALYTIC SOLUTIONS

PROCESSING

ADVANCED ANALYTICS

DATA MANAGEMENT & ACCESS

CAPTURE & STORAGE

reporting OLAP visualization knowledgediscovery

modelmanagement

businessapplications

Hadoop NoSQL columnarSQL relationaldatabases

files(XML, docs)

text voice video naturallanguage

sentiment network geospatial

in-memory in-database complexevent

eventstreaming

machinelearning

Map-Reduce

DM(DQ,

Metadata)

MDM/RDM

dataaccess

dataintegration

datavirutalization/

federation

datasecurity &

privacy

visualanalytics

data mining

forecasting optimization machinelearning

figure 4. the analytic ecosystem


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Organizations are recognizing the need to manage analytics as a program and make the journey toward data-driving decision making like any other business practice. Thus the advent of chief analytic officers and data scientists as well as the re-emergence of ana-lytic centers of excellence. Regardless of the approach, successful analytic programs exhibit common characteristics. They are:

Strategic

They develop an analytic agenda based on a broad, cross-functional view of the organi-zation. Why? In order to develop a focused analytic agenda based on corporate strategy and objectives that will meet the needs of multiple functions. In this way, the center of excellence ensures it is driving a transformative agenda and does not become a help desk or order taker.

The implication here is that analytics needs to be governed. Creating awareness, engen-dering organizational buy-in and staying the course requires a steady – and authoritative – hand on the wheel.

Collaborative

Effective analytic groups engage the customer in the process. If your model is interest-ing, but the business doesn’t see the value, the entire exercise is moot.

Acknowledging this point, a national financial services institution has established an ana-lytics lab where the business consumers work side by side with analytic experts and data management teams from knowledge discovery through to model validation and business process re-engineering. The organization has also borrowed from agile devel-opment philosophies to create staged decision gates at each point in the life cycle to encourage failing fast.

Yes, conflicts will inevitably arise between the long-in-the-tooth executive or business user who knows how things run (inside out) and the data scientist who takes a different view (outside in). But rather than discouraging this discourse, we need to encourage it. Because it’s in this margin that innovation occurs.

Leading organizations also partner with IT or data management teams to deliver both the required data environments and the suite of analytic tools to exploit them. That be-ing said, IT cannot own analytics. IT can partner with the business to enable data-driven decision making. IT can and should serve as a thought leader educating the business on the art of the possible. In fact, this is critical. But the business must have both the willing-

MANAGING THE CHANGE organizing for success

Manage the journey toward

data-driven decision making

like any other business practice.


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ness to explore and the will to act on generated insights, which translates into the need for the business to own and operate the analytic engine like any other business process, be it sales, marketing or supply chain management.

Influential

Analytic innovators develop deep relationships and forge productive partnerships with different constituencies, including the executives.

They also recognize and embrace their roles as change agents and the importance of consistent communication, expectation management, education and enlistment. And they ensure that skill sets to play this role are present and accounted for.

Last but not least, they see their mission as one of enablement, not world domination. This means facilitating the growth of embedded skill sets and developing a critical mass of analytic talent, both inside and outside of the centralized analytic team or competency center itself.

Skilled

Successful practitioners establish a reputation for being highly-skilled specialists who are committed to staying abreast of emerging tricks and techniques and incubating those capabilities for the enterprise.

This does not mean, however, that all development occurs on a centralized team or in a center of excellence. Highly functional teams realize when their expertise is and isn’t necessary and strive to engage only in a value-added manner. This might run the gamut from delivering a project or program, advising on it or acting as a consultant.

If analytics is new to the organization or function the analytic center may play a central role in project execution. As the analytic maturity of different functions or the organiza-tion at large improve, the ability to act as a trusted adviser and consultant can be more important.

Results Oriented

Last but not least, highly performing organizations focus on outcomes. Action, not just insight, is the name of the game.

Analytic innovators embrace their role as change agents.


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Cultivating an analytic culture and embracing data-driven decision making is no small undertaking. It’s easy to get lost in the hype or become discouraged, especially when dealing with often inflated expectations for immediate outcomes associated with big data. Or while that so-called little data is still proving to be a big challenge.

But while the journey forward may at times feel arduous, it is achievable – even if your enterprise wasn’t born with analytic DNA.

Organizations that make the turn engage the mind, connect with the heart and develop the analytic muscle of their organizations to achieve their goals. These elements are criti-cal for organizations to transcend traditional mindsets and create an analytic culture that embraces and drives innovation.

CONCLUSION

MIND

ALIGNS analytics to

business strategy.

CULTIVATES a willingness to experiment and

the will to execute.

INVESTS in and nurtures required skills, processes and

solutions.

figure 5. the analytic enterprise

SAS Institute Inc.100 SAS Campus DriveCary, NC 27513-2414USA Phone: 919-677-8000Fax: 919-677-4444

about the author

KIMBERLY NEVALA is responsible for industry education, key client strategies, and market analysis in the areas of business intelligence and analytics, data governance, and master data management. She has over 15 years’ experience advising clients on the development and implementation of strategic customer and information manage-ment programs and managing mission-critical projects. A frequent speaker and writer, Kimberly is often consulted on the topics of business strategy and alignment. She is the co-author of the first eBook on data governance, The Data Governance eBook: Morals, Maps and Mechanics, as well as Planning Your BI Program: A Portfolio Management Approach and Top 10 Mistakes to Avoid When Launching a DG Program.

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. 10 _S1 .0 14

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SAS Best Practices white paper: An examination of your ...€¦ · a SAS Best Practices white paper of an ANALYTIC ENTERPRISE ... a competitive advantage. ... but companies like Pandora