Business Transformation

A Roadmap for Maximizing Organizational Insights

Aiman Zeid

• Publisher: Wiley
• Year: 2014
• ISBN:
  9781118891612
• Edition: 1
• Topic: Business
• Subtopic: Business intelligence
• Index: Business

Effectively introduce and promote analytics within your enterprise

All companies use information to set strategies and accomplish business objectives. But how many CEOs and CIOs would say they are satisfied that their companies get maximum value from information? Business Transformation reveals how SAS's Information Evolution Model (IEM) can be used together with analytics for groundbreaking results. Author Aiman Zeid provides the necessary information you need to introduce and promote the use of analytics and insight across your organization. Along with examples and best practices of global companies that have successfully been through this process, you'll learn how to identify the starting point and develop a road map for execution.

• Reveals how to introduce and promote the use of analytics and insights across your organization
• Written by a lead developer at SAS global Business Intelligence Competency Center program and services
• Features global case studies and examples

Practical and insightful, this reference provides businesses with an essential blueprint for creating improvements that optimize business returns and put the potential of data analytics to work.

Chapter 1
The Critical Role of Business Insight

Have you ever asked a question that no one in your organization could answer? Or maybe someone answered it, but it turned out the information driving the answer was so flawed that you really got no answer at all. Or maybe you got multiple conflicting answers that took hours, days—or maybe even weeks to straighten out.

Have you ever wondered why—in an age when bar codes are on everything, every conversation with a customer is recorded, and the Internet is full of comments about your products and services—you don’t really know who your customers are? Or what they want? Or, more important, what they might want next year? Are you frustrated because your organization isn’t achieving its goals? Or are you wondering why your organization is not keeping up with the competition?

You are not alone. Information, and the business insight you can derive from it, is coming so fast, from so many sources, in so many different formats, and at such incredible volumes that it is difficult to grasp. It’s not hyperbole to suggest that gaining insight from data is like drinking water from a fire hose. Business insight is derived from an organization’s information by using the domain knowledge of its resources and applying analytics to mine the data for critical trends, forecast revenue, determine customer propensity to buy products and services, and predict attrition in critical talent. Business insight is also produced by using business intelligence for querying and reporting, performance management to monitor critical business key performance indicators (KPIs) and validate strategies, and industry solutions to optimize the operations of critical business functions. Deriving business insight depends on developing an enterprise information foundation to properly integrate data from all business units in the organization. Organizations also have to take advantage of external and unstructured data to develop business insight. Yet most organizations find they can’t generate the information they need (from their data)—or, if they can, it is not coming fast enough to make a difference.

Business insight adds value in several areas, ranging from a simple view of historical performance driven by the use of business intelligence to predictions of sales volumes and customer behaviors developed by the use of analytics. Successful organizations create a competitive advantage by maximizing the use of analytics to guide their decision making and strategies. This book highlights the importance of developing and aligning the organization’s resources and talents, information infrastructure, and use of analytics with the required processes and culture to create business insights that strengthen its competitive advantage.

You have questions, your organization has data—but can you generate the insight you need from the data? And can you do it fast enough? You need more than technology, a team of consultants, or a visionary data guru to lead you out of the data forest. You need a comprehensive approach to evolve your current information management practices to generate more insight. You need to introduce new talent, new processes, and a new culture that will help make your organization more data-driven and analytical. You need to promote the use of analytics and insight across the organization in a repeatable and effective way, and learn how to identify a starting point and develop a strategy for getting this done. And that’s what you will read about in this book.

THE DISRUPTIVE NATURE OF DATA

During the recent U.S. presidential election there was a spirited discussion around whether aggregating poll data could accurately predict its outcome. Much of the debate related to the work of a blogger and statistician named Nate Silver (www.fivethirtyeight.com). Silver built a model that weighs and averages numerous polls based on multiple factors. Every few days, on his blog, he would update the percentage chances that Mitt Romney and Barack Obama had of winning the election. In 2008, Silver’s model of the presidential election was accurate to within one percentage point of the final popular vote,¹ and he correctly predicted the race in 49 of 50 states. But at that time he was a comparatively unknown blogger. In 2010, his blog was licensed to the New York Times, and in the fall of 2012 he had recently published a book.² As his 2012 model increasingly showed a likely victory for the incumbent (it stood at 90.9 percent the day of the election), pundits began to howl. Some tried to pick apart the model, while others claimed bias. But one savvy commentator noted what was really up: Silver was a disruptive force. “Silver’s work poses a threat to more traditional—and, in particular, to more excitable—forms of political punditry and horse-race journalism,”³ explained a Washington Post columnist. Silver was threatening a traditional profession. And it wasn’t his first time. Silver initially gained notice for forecasting professional baseball player and team performance by using less traditional statistical measures. A variation on his system was adopted by the Oakland Athletics baseball team’s general manager, who hired an analyst and began selecting players based on skills that weren’t as highly valued in the marketplace. The book Moneyball,⁴ by Michael Lewis, captured the tension fueled by the general manager’s decision to ditch the conventional wisdom of scouts who looked at traditional statistics. Despite regular declarations that “moneyball” is “dead,”⁵ it not only survives but also continues to grow as more teams, both inside and outside of baseball, hire analysts.

AN UNCONVENTIONAL LOOK AT CONVENTIONAL WISDOM

In the business world the corollary to the pundits and scouts are those buyers and marketing gurus—even CEOs—who operate on “gut” instinct in choosing what products to launch and what business path to follow. They are often rather hostile to the Nate Silvers, who invaded their territory with analysis that suggests a different path. Unfortunately, businesses tend to have far too many pundits and not nearly enough Nate Silvers. So even if an organization purchases software or a solution to begin to drive the business more analytically, they don’t have the people who can analyze the data, or work with the business unit to decide what to analyze. Or the organization doesn’t have a culture in place that will accept the analysts’ work, a process in place that makes analytics a factor in everyday decisions, and a leadership that understands that gut instinct is old school.

Conventional wisdom has a way of creeping into organizations and holding them hostage. Even companies whose executives talk about their “change management” are often entrenched in approaches and ways of doing business that aren’t really working. An analytic project often provides the aha moment when an organization realizes everything it thought it knew—what its customers wanted, what was most likely to sell, what was its most profitable service—was not completely accurate. Let’s explore a few of those moments.

Customer value is rife with examples. We all know our best customer, right? It’s the one who buys the most stuff from us. Or is it? If you want to create an offer, or provide a discount, or do anything to increase the loyalty of your best customers you don’t need analytics—you just need to know who spent the most money. Many telecommunication providers certainly worked under that assumption for years, until some of the savvier ones used insight from analytics to discover that their “best” customers were actually costing them a lot of money. These were the customers who tied up customer support with questions about their plan, subscribed to a plan that was not profitable, used the service in the least profitable way to the organization, or didn’t keep up with their payments and had a high delinquency rate. All that attention to those high-needs customers was hurting the bottom line. Yet this flies in the face of conventional wisdom that suggests you do anything to keep a customer happy because it costs more to gain a new one than retain one.

Another example of the use of insight from information comes from the banking industry. Banks often have three critical functions operating in silos. The marketing department focuses on customer retention, product innovation, profitability, and the proper channels to promote them. The risk team develops and monitors operational risk factors and scores customers in terms of their liabilities and probability of default. The finance organization keeps its revenue and loss information in its own silo. Banks have operated these three critical functions in silos for years. When the information from these siloed functions is integrated, a brand new perspective provides more business insight for making better decisions and setting strategies. The actual revenue from each customer is produced from the financial side. The risk rating for each customer can be provided by the risk team, and now the marketing organization can use all it knows about the customers to identify the proper product to market to each one based on his or her risk score and total contribution to the bank’s revenue. Banks can now develop a more effective strategy to grow their business with each of their customer segments based on insights derived from their information.

INNOVATING AT THE SPEED OF DATA

Both telecommunications and banking examples highlight the value of speed in working with data. It’s one thing to figure out that certain customers aren’t worth the effort to retain and decide not to send them an offer; it’s another to do it in real time when a customer calls looking for a different rate plan or upgrade or walks into a branch to apply for a home mortgage. Does the service agent have a file that offers suggestions as to whether a discounted upgrade should be offered to the customer or whether a credit limit should be extended? Or should the agent just politely listen and ignore customers’ threats to take their business elsewhere?

When organizations begin using analytical insight they challenge conventional wisdom quickly and effectively. An online floral company stopped gearing its advertising to men when it discovered most of the people buying flowers for various holidays were women. A casino directed its best discounts away from its most loyal customers (they were going to visit either way), and targeted them at the customers who were also visiting their competitors. Engineers on an oil rig use data on conditions in their environment to predict when equipment needs maintenance before a costly breakdown, rather than relying on the manufacturer’s manual that had not proven effective in the past. Automobile companies predict which part may fail, and proactively replace it when a customer comes in for routine maintenance, saving money and increasing customer perception of quality. Hospitals use analytic...