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Not so 'Big Data'

Companies are investing in data scientists, data analysists, data warehouses, and data analytics software. But many of them don’t have much to show for their efforts. They probably never will.

Big data has been hyped so heavily that companies are expecting it to deliver more value than it actually can. The main reason that investment in big data fails to pay off, is that most companies don’t do a good job with the information they already have. They don’t know how to manage it, analyze it in ways that enhance their understanding, and then make changes in response to new insights.

Companies don’t magically develop those competencies just because they’ve invested in high-end analytics tools. They first need to learn how to use the data already embedded in their core operating systems, much the way people must master arithmetic before they tackle algebra. Until a company learns how to use data and analysis to support its operating decisions, it will not be in a position to benefit from big data.

At Vision Quest, our experience demonstrates that those organizations that consistently use data to guide their decision making are few and far between. The exceptions, companies that have a culture of evidence-based decision making, have all seen improvements in their business performance—and they tend to be more efficient, effective, and profitable than companies that don’t have a similar culture.

The digital economy is all about capturing, analyzing, and using information to serve internal and external customers. Most companies can significantly improve their business performance simply by focusing on how operating data can inform day-to-day decision making.

So why don’t more companies make better use of data and analysis?

The primary reason is that most management practices haven’t caught up with the organization’s existing technology platforms. Every organization has installed more than one form of digital platform such as ERP and CRM systems, real-time data warehouses, homegrown core information systems, accounting software, email services, telephony systems, yet most organizations have not cashed in on the information those platforms make available.

In addition, adopting evidence-based decision making is a difficult cultural shift: Work processes must be redefined, data must be scrubbed, and business rules must be established to guide people in their work. The good news is that once companies have made the cultural change, they usually don’t go back, and their operating improvements are not easily replicated by competitors.

An organizational culture of evidence-based decision making ensures that all decision makers have performance data at their fingertips every day. They also follow four practices:

  • They establish one undisputed source of performance data
  • They give decision makers at all levels near-real-time feedback
  • They consciously articulate their business rules and regularly update them in response to facts
  • They provide high-quality coaching to employees who make decisions on a regular basis

Enable Employees to Make Good Decisions

In the 1970s Southland Corporation, known for pioneering the concept of the convenience store chain with its 7-Eleven shops, divested its Japanese stores, and Seven-Eleven Japan was born. Toshifumi Suzuki, the first CEO, decided early on that the key to profitability for the company’s tiny stores would be rapid inventory turnover. So he placed responsibility for ordering—the single most important decision in the business—in the hands of the stores’ 200,000 mostly part-time salesclerks. Those employees, Suzuki believed, understood their customers and, with good information, could make the best decisions about what would sell quickly.

To support salesclerks’ decision making, he sent each store daily sales reports and supplemental information such as weather forecasts. The reports detailed what had sold the previous day, what had sold the previous year on the same date, what had sold the last day the weather was similar, and what was selling in other stores. Because Seven-Eleven Japan carries fresh food, Suzuki arranged for deliveries three times a day so that the clerks could base their orders on immediate needs. And he connected the clerks with suppliers to encourage the development of items that would suit local customers’ tastes. The result? Seven-Eleven has been the most profitable retailer in Japan for more than 30 years.

This is not a story about big data, or even about big investments in data. This is a story about a lot of little data. More important, it’s about betting your business success on the ability of good people to use good data to make good decisions. Enabling employees in this way, and arming them with the data they need, helps them make better operating decisions on a daily basis. It can also lead to a constant stream of innovation. At Seven-Eleven Japan, approximately 70% of the products on the shelves each year are new, designed by salesclerks in response to customers’ preferences.

In contrast, consider the U.S. department store executive who proudly proclaimed that the company’s systems alerted corporate managers instantly when a store ran out of yellow sweaters and needed inventory to be shifted from stores that were overstocked. When asked, he acknowledged that his systems could not tell him how many orange sweaters would have sold if the company had carried them. Only his salesclerks would know about orange sweater demand—and he had no formal way of collecting their insights.

The Seven-Eleven Japan approach to generating big value from little data relies on providing transparent information to decision makers and setting clear expectations for how they will use it. That is the essence of evidence-based decision making. You could design a computer model to spit out predictions of what might sell quickly, but the computer would not have data on all the requests that couldn’t be fulfilled or insights from casual conversations with customers. There would be far fewer opportunities to identify successful new-product concepts.

Most examples of evidence-based decision making have been in divisions and functions rather than across companies. That’s probably because it’s less daunting to improve how data are used in one unit than to do so throughout an organization. Now let’s examine four practices.

Create a Single Source of Aggregated Data

Most data-centric organizations do not have a single data repository; however, they do insist on using performance data from just one authorized source. When Ron Williams became the head of operations at Aetna, he found that all the divisional heads could show him a spreadsheet with performance data indicating that their divisions had been profitable the previous year even though Aetna as a whole had recorded a loss of almost $300 million! One of his first initiatives was to mandate a single information system that defined the data everyone would use to measure performance.

At first, some Senior managers saw the data as seriously flawed. Some revenue and expense items, they believed, were inaccurately calculated or allocated, eventually they got into the habit of focusing on the metrics Williams had designated. As IT and business leaders cleaned up the data, management gained a better understanding of costs and profitability. Soon executives were creating new health plans with more-targeted pricing and working their way back to profitability. In 2005 Aetna recorded profits of $1.6 billion. In 2006, reflecting on his company’s success, Williams said, “When you have a pre-agreed set of numbers presented in a uniform way, you can train the company how to think about problems. It gives you the context for making choices.”

The story of Seven-Eleven Japan’s success is about betting on the ability of good people to use good data to make good decisions. Getting everyone to accept the single source of data may require appointing one executive to oversee its management.

At Foxtel, Australia’s largest provider of pay-TV services, CFO Peter Tonagh (now COO of News Corp Australia, one of Foxtel’s parent companies) maintained primary control over the definitions of the data in the company’s data warehouse. “There is only one source of truth in this business, and that’s what comes out of my team,” he says. Tonagh also keeps a lid on reports in order to focus everyone’s attention on what matters most. “I don’t want people thinking, How many customers have taken multiroom service?” he notes. “I want them to be thinking, How am I going to sell more multiroom services?” Tonagh’s approach has led to a significant decrease in the number of regular reports generated, down to 180 from a high of 600. That in itself has generated cost savings for Foxtel, but the greater benefit has been helping management focus on strategic objectives.

Universal acceptance of one source of truth is the first step in adopting a culture of evidence-based decision making. As both Aetna and Foxtel learned, it’s okay if the data are initially flawed, because it takes time for people to learn how to use a single source. But over time, quality matters, so companies will want to initiate processes for improving data capture. Invariably, that means reviewing business processes and identifying where mistakes enter systems. People required to use data will take an active interest in governance processes designed to clarify data definitions and in learning how information flows through the organization.

Explicitly Manage Your Business Rules

Little data can have a big effect on performance when managers use the data (about customers, products, transactions, and so on) to continually assess and improve the business rules that govern their operations. Business rules are the mechanism for specifying what actions should be taken in a given circumstance. They may be broad (“Do whatever it takes to make the customer happy”) or quite granular (“Accept returns from customers only if they bring a receipt and the receipt shows that they purchased the item in the past 30 days”).

Ideally, business rules align the actions of operational decision makers with the strategic objectives of the company. But that happens only when relevant individuals understand the rules and management regularly adjusts them in response to new information. Companies with a culture of evidence-based decision making see to it that business rules are continually assessed and improved by articulating them clearly and ensuring consistency across the company.

Consider Citrix Systems, a $2.1 billion technology firm that has 250,000 customers in 100 countries. Most of Citrix’s customers are served directly by one of the company’s 10,000 business partners. Citrix has traditionally offered its best partners discounts on Citrix products to encourage and reward their loyalty. But company executives found wide variation in managers’ discounting practices and increasingly observed negative impacts on revenue. So Citrix established a new companywide set of business rules that award rebates on the basis of how many Citrix product certifications (which attest to the ability to service a product) the partner firm’s employees have collectively earned. Management anticipated that these rules would optimize revenue and, by encouraging partners to earn product certifications, improve partners’ capabilities.

Having instituted new business rules, Citrix can analyze their impact. If results aren’t as anticipated, the company can change its rules again. That kind of analysis doesn’t involve the massive processing associated with big data, nor does it engage data scientists in sophisticated statistical modeling. Instead, it involves ordinary managers’ close monitoring of changes in key indicators. That is how a company uses its little data to improve performance.

Analyzing the impact of business rules doesn’t involve the massive processing or the statistical modeling associated with big data. Business rules become complex as they become more granular: An airline’s elite customers can check a bag free of charge; other customers must pay. Some tickets are refundable; others are not. Companies address the complexity of their business rules by embedding many of them in software. For example, an airline passenger’s elite status is stored electronically so that the system will calculate the accurate baggage fee. Retailers can store customers’ purchase data so that computers can check whether a given return qualifies for a refund.

Embedding business rules in software—automating them—frees people from routine decisions, allowing them to focus on activities that demand individual discretion. Citrix automated its partner certification rules so that the partners are not required to track eligibility for rebates. The system does the tracking and grants the rebates. It even has a built-in grace period for partners that temporarily fall below thresholds for rebates. Automating business rules also permits increasing granularity, because systems can deal with more details than people can. It tends to be easier to test the effects of changes in automated business rules than in rules that are not automated.

Business Rules Are Running Your Company, and You Don’t Even Know It

Most companies have thousands of business rules, and as those companies become more complex, they generate more rules. It used to be that employees had to learn all the rules in order to execute their jobs. Their ongoing experience would lead to questions, which would lead to reassessment of the rules. But companies today manage the proliferation of rules by automating them in ERP and CRM systems.

The upside is that the rules are consistently executed; the downside is that they can become outdated or misaligned, and only very proactive employees will notice. For example, one insurance company automated business rules for processing claims related to stolen automobiles. The process involved reimbursing the policyholder after the car had been gone for 30 days. After many years, as the company was implementing a new system, a thoughtful analyst reviewed this rule. He found that in some parts of the United States, cars that have been missing for 24 hours are almost never recovered—they are driven out of the country and sold. His analysis led to a change: Policyholders in those parts of the country are now compensated 24 hours after the theft is reported.

Rules embedded in enterprise systems basically run some companies and provide benefits such as easier analysis and more opportunities to test and learn. But companies won’t achieve those benefits unless they make two changes.

First, they must specify who is responsible for a given set of rules and has the authority to change them. If no one is in charge, it’s that much easier to forget rules once they’ve been implemented.

Second, they need to introduce rules engines, which separate the rules from the enterprise software in which they’re embedded. As a result, managing and changing rules no longer requires IT expertise and so is easier and less expensive.

The temptation may be to treat this cultural shift like any other major business change initiative, starting at the top by defining and communicating goals, establishing metrics, assigning accountability, and training people. It is best to begin more modestly. Although Aetna was able to start near the top of the company, many business leaders would be wise to aim lower. Pick important repetitive work that includes some discretion and some application of rules—service work is a good example. Imagine how that work would be performed if people had clear business rules and metrics, along with all the data they needed to make good decisions. Then assign coaches to those employees and coach the coaches. These early efforts may reveal misguided business rules, low-quality data, and dysfunctional metrics.

Much of the hype around big data focuses on getting more information and more people to analyze it. But the opportunity presented by the information economy is best tapped by getting all people to use data more effectively. That may seem like an expensive and risky endeavor. But it’s actually a cheap and powerful way of taking advantage of all the big—and little—data you are accumulating.

The following checklist will help move your organization towards a data-centric culture.

  • Develop a data aggregation center
  • Develop a Global Data Management System accessble at all levels of the organization
  • Ensure that decision makers have clear business rules
  • Create and revise business rules on the basis of business analytics
  • Give operational decision makers the information they need to do their jobs
  • Create a data dictionary or other data asset specifying enterprise master data, transaction data, and historical data
  • Ensure leaders accept ownership of key data
  • Ensure that findings from post-implementation reviews guide future projects
  • Ensure key stakeholders are engaged in major projects throughout their life cycles


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