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Interview with Jill Dyché, Vice President of Best Practices, SAS Institute

About Jill Dyché

Jill Dyché serves as Vice President of Best Practices at SAS, where she and her team counsel business and IT leaders on how to succeed and make changes stick. Prior to joining SAS, Ms. Dyché l co-founded Baseline Consulting, a management consulting firm. SAS acquired Baseline Consulting in 2011. She has written about and worked in business-IT alignment for more than 20 years. Her extensive experience makes her a mentor in the field. She judges several best practices awards and is frequently invited to speak at industry conferences, vendor events, and universities. Ms. Dyché and her work has been featured in major publications, including the Wall Street Journal, Harvard Business Review, Newsweek, Computerworld, and

A skilled writer, Ms. Dyché blogs about innovation for and her own website, Her advice column, Q&A with Jill Dyché, is available on Ms. Dyché has written a number of books in the field of data analytics: Her first book, e-Data, was published in eight languages. She also penned the bestselling The CRM Handbook and Customer Data Integration: Reaching a Single Version of the Truth, which was the first book about master data management every published. Ms. Dyché’s newest book is titled The New IT: How Technology Leaders Are Enabling Change in the Digital Age (McGraw-Hill, 2015).

Ms. Dyché holds a degree in English from the University of California Los Angeles and conducted graduate work in French.

Interview Questions

[] You are known for your tremendous experience working in and writing about analytics. For readers completely new to the field of analytics, could you please offer a high-level description of what it is and where it stands today? Why are analytics skills in such high demand?

[Ms. Dyché] Analytics is the use of data and technology to improve decisions. It can be as simple as accessing customer contact information to send a customer a new product offering, or as complex as watching financial transaction patterns and detecting money laundering activities in real time.

The point of analytics is to help businesses—and business people—refine the decisions they make in order to save their companies money, protect them from fraud, comply with regulations, and generate revenues based on fact, not gut-feel. It’s all about being what they call “data driven.”

[] Your specialties include data analysis and business intelligence, among others. These phrases often come up when one researches analytics programs and employment opportunities. What is the difference between data analysis and business intelligence? Do they, for instance, call for different skills or technical expertise? Could you offer examples of job titles commonly associated with each?

[Ms. Dyché] The business intelligence vernacular is tricky, fluid, and often confusing to newcomers. Often terms that mean different things (like ‘business intelligence’ and ‘analytics’) are used interchangeably.)

Data analysis is any activity involving looking at data to determine what to do next. The data can come to you via spreadsheets, statistical models, streaming video, or smoke signals for that matter. It’s about interpreting the meaning of data.

Business intelligence is the general rubric under which other practices like data analysis, analytics, or data science fall. It’s really a general term that many people use to encompass the field of analytics and its various moving parts. For some, business intelligence connotes the most basic components of decision making–like viewing a dashboard to confirm a customer’s mailing address, or looking up a product’s dimensions so you know how many bottles of Clorox will fit on the shelf.

Technically speaking, data science is a subset of analytics, which may include everything from rudimentary reports that are pushed to business people to complex data mining and beyond. Analytics is the general category name for everything having to do with using data for decision making. The term “data science” is more subjective, but most people interested in data science focus on gleaning new insights from data that they can use to monetize new products, services, or business models. Most data scientists have more than a passing interest in statistics, and can deploy advanced algorithms to understand data patterns.

[] Knowing what BI professionals do can be quite different from understanding what it is like to work in the field on a day-to-day basis. How would you describe the experience? What parts of the work might one consider challenging? Which drive and inspire you personally?

[Ms. Dyché] There are actually dozens of different jobs in BI. BI encompasses both tactical and strategic roles, and both right-brained and left-brained thinking, introverts, and extroverts. A well-run BI program will include programmers, software experts, business analysts, data scientists, statisticians, and consultants. It will likely involve executives—Chief Analytics Officer and Chief Data Officer are newly visible roles in the BI space. And, of course, there are business users, who typically represent every major business unit in the company.

The thread that’s interwoven throughout these various job roles is the need to make better decisions with data. Many BI and analytics early-adopters insist that this is a lofty goal that is never fully reached as their businesses change and evolve and data gets more complex (read: “bigger”). BI programs are a lot like orchestras, where everyone plays a different instrument—and there are quite often long solos! —the whole being bigger than the sum of its parts.

[] One could imagine employers’ expectations of BI professionals might evolve as the field grows and new technology emerges. What technical skills do employers want most from today’s BI analysts? What additional skills and qualities could really set one apart?

[Ms. Dyché] Well it’s tempting to call out the data scientist here. After all, it’s the Sexiest Job of the 21st Century. (Or is it?) The truth is that this title—like many in the BI space—is subjective according to a company’s specific analysis needs.

For instance, some big banks have re-branded their statisticians into data scientists. Other industries prioritize speaking, writing, and storytelling skills for their data scientists. It really depends on where the need is, and which skills companies need to put forward to evolve their ability to make data-driven decisions.

Right now we’re seeing executive attention increasingly focused on the role of the aforementioned Chief Analytics Officer. Companies are realizing that analytics transcends any one business unit and department, so putting an executive in charge of an enterprise-focused analytics program is the most straightforward way to understand the scale of analytics, as well as confront the realities of talent and technology changes.

[] What do you predict the future holds for the rising generation of BI professionals five or ten years down the line, and how can they succeed in it? As a highly-experienced professional, is there anything you would like to tell them? Any key lessons or advice?

[Ms. Dyché] I’m predicting two trends in BI and analytics that will shake up how they’re deployed, and the talent management necessary to sustain them.

The first prediction is that IT organizations—which are in so much upheaval already—will continue to cede control of analytics and BI technologies. This means more analytics responsibilities will fall to the business. Business units like Marketing and Finance will have more power and responsibility to select their own BI technologies, and sometimes even implement them. Most of the time they’ll turn to the cloud for help, but some might actually spin up their own shadow IT teams to help deliver analytics solutions.

The second prediction is that analytics will become a lynchpin in emerging corporate innovation programs. We’re finding that executive teams are investing in innovation at a record rate. What many aren’t aware of (yet) is that analytics, and particularly the subsets of knowledge discovery and machine learning, are becoming staples of innovation. Companies are recognizing that innovation is the surest path forward to competitive differentiation. And forward-thinking companies understand that, by tightly coupling innovation with analytics, they’re already leaders.

The advice I give to people who are getting involved with analytics is this: Learn the data. It’s much easier to learn to use the latest software tool than it is to truly understand how to access, format, correct, annotate, provision, and explain important business information. As a rule, corporate data is heterogeneous and difficult to integrate. People who understand key corporate data—customer behaviors, billing information, transactions, sensor and device data, product data, partner data, and an array of other information that’s woven throughout the fabric of their companies—are becoming very sought after by employers. Moreover, show me a company’s strategic objectives and I’ll show you critical business objectives that must be data-enabled.

Put another way: Software tools come and go, but data is forever.