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
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
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
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
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
They provide high-quality coaching to employees who make decisions on a regular
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
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.
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’
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
Develop a data aggregation center
Develop a Global Data Management System accessble at all levels of the
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
Gartner Debunks Five of the Biggest Data Myths
Analysts to Discuss the Impact of Big Data at the Gartner Business Intelligence
& Analytics Summit 2014, October 21-22 , Munich, Germany
For Gartner - Rob van der Meulen
10 Observations on Big Data in Late 2015
The topic first began to take off in late 2010 (at least according to search
results from Google Trends) and, now that we’re approaching a five-year
anniversary, perhaps it’s a good time to take a step back and reflect on this
major approach to doing business.
By Tom Davenport, Distinguished Professor, Babson College