Foreword
When Information Revolution1 was published in 2006, no Chinese-based companies were among the top 10 largest companies by market capitalization. Apple didn't sell phones. Facebook was something college kids used to connect with their friends. Back then, we talked a lot about the amount of data coming in and faster processing speed.
What we believed then remains true today: Data, and the decision-making process, can be moved throughout the organization to equip every decision maker (automated, line worker, analyst, executive) to make the best choices. By operationalizing analytics, organizations can identify and quantify both opportunity and risk. Information Revolution highlighted SAS' Information Evolution Model, which helps organizations understand how they interact with their information and how to extract more value from it through analytics.
SO WHAT HAS CHANGED?
Business intelligence still matters. But today's global economy requires predictive analytics and forecasting to play a more active role. Insights from unstructured data now hold great promise. New ways to store, move, and process data have made big data more accessible and affordable than ever before. Delivery has moved to mobile. Many leaders run their businesses from tablets and smartphones.
A persistent myth is that technology alone enables all this. Sure, you need technology, but it's just one component: People, information processes, and culture are equally critical. That's really what this book is about—transforming your organization to harness all four components.
PUTTING THE SPOTLIGHT ON PEOPLE AND CULTURE
After Information Revolution was published, accelerated processing speeds gave rise to near-real-time results. More granular exploration of data became possible in ways that weren't quick or easy before. Organizations that treat their data as an asset continue to:
Invest in people with the skills to extract the insights that were hidden in the data and surface them to decision makers throughout the organizations.
Foster a culture that encourages using data to uncover new business opportunities and gain a better understanding of their customers.
Have an executive sponsor who leads the effort to find, hire, cultivate, and support individuals who embrace fact-based decision making. This executive sponsor pays particular attention to the communication challenge that data-driven decision making presents. It's important to have an executive who can articulate what the analytical insight returns can mean to the business units—and win over skeptics.
If top executives still make decisions based on gut feeling and data-driven individuals are still a separate part of the business, no amount of technology and data governance processes will make a difference. But if an organization is committed to using data successfully, one strategic hire can have a huge impact. A new type of professional, the data scientist, can bridge the communication gap that prevents an analytical culture from taking hold. Tom Davenport, in his Harvard Business Review article “Data Scientist: The Sexiest Job of the 21st Century,”2 describes a data scientist this way: “It's a high-ranking professional with the training and curiosity to make discoveries in the world of big data…. Their sudden appearance on the business scene reflects the fact that organizations are now wrestling with information that comes in varieties and volumes never encountered before.” Data scientists help organizations get the most out of their data, in part, by using business requirements to drive the information exploration and the application of analytics. Data scientists often have a background in math, statistics, and computer science, but aren't necessarily experts in any one of those fields. They have to be very good at translating the value of data to the business and helping analysts understand what they need to do.
Internal communication and business and IT alignment continue to present challenges for organizations. Many rely on enterprise centers of Excellence to boost business-transformation efforts.
My point is: You can't just bring in technology tools to solve your business problems and expect them to do all the work. You must have the infrastructure capabilities, the skilled people, the information processes, and the cultural commitment to derive the most value from your data.
AND SOME THINGS STAY THE SAME …
Some things haven't changed, and one of them is taking a structured approach to building toward the enterprise level of information maturity—and beyond. The five levels outlined in 2006 remain relevant today (though we've grouped the levels into three key categories). Unfortunately, many organizations are in a quandary about how to reach information maturity. Now here's the clincher: “By 2015, 15 percent of organizations will modernize their strategy for information management capability and exhibit a 20 percent higher financial performance than their peers,” according to Gartner.3 These are clear signs of strategic initiatives by many organizations to reach higher maturity level.
To get started, you need to understand where your organization is today before you can build toward the future. This is particularly important as it relates to purchasing technology. Organizations that say they have not received a strong return on their investment in analytic technology frequently suffer from information maturity issues and may benefit from a business-transformation effort. Assessing maturity is a process, but well worth the effort in the knowledge you will gain. It can be painful to find out your organization is not at the maturity level you assumed. But, you will have a clear picture of how to begin developing your road map to get to the next level.
A fact-based decision-making culture is no longer an option; it's a requirement spreading across industries. To stay competitive, be proactive. Use the Information Evolution Model. Let your data give you a fresh perspective on your business—see what's working, fix what isn't, and set your sights on new opportunities.
—Jim Davis
Senior Vice President and
Chief Marketing Officer
SAS
NOTES
1. Jim Davis, Gloria J. Miller, and Allan Russell, Information Revolution: Using the Information Evolution Model to Grow Your Business (Hoboken, NJ: John Wiley & Sons, 2006).
2. Thomas H. Davenport and D. J. Patil, “Data Scientist: The Sexiest Job of the 21st Century,” Harvard Business Review, October 2012, http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1.
3. Eric Thoo, Mark A. Beyer, Ted Friedman, Merv Adrian, and Andreas Bitterer, “Predicts 2013: Advancing Data Management Maturity,” Gartner, December 10, 2012.