Business analytics

Business Analytics is a term that describes how organizations gather and interpret data in order to make better business decisions and to optimize business processes. Analytical activities are expanding fast in businesses, government agencies and not-for-profit organizations.

Analytics are defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based decision-making. Analytics may be used as input for human decisions; however, in business there are also examples of fully automated decisions that require minimal human intervention. In businesses, analytics (in addition to data access and reporting) represents a subset of business intelligence (BI).

The Age of Analytics may be thought of as a new subcategory of the Information Age, with the key difference that the early years of the Information Age were a time when information was a scarce resource, whereas today there is an abundance of information. The Age of Analytics therefore represents another way to think of the activities necessary for success in a knowledge economy or increasingly typical of a modern information society.

Application Of Analytics
Many organizations already use analytics in some form. Operating metrics and performance gauges such as the balanced scorecard are familiar to most managers. For instance, a manufacturer may track, interpret and use data to improve how it manages product quality, and a marketing group might base decisions on the long-term analysis of different customer segments. Businesses as diverse as global cement giant CEMEX, California winemaker E & J Gallo Winery, industrial equipment maker John Deere, retailer Tesco, and Bank of America are regularly applying analytics to achieve advantage. For example, Gallo, operating in a business built on using intuition to gauge unpredictable consumer preferences, now quantitatively analyzes and predicts the appeal of its wines. Between 2002 and 2005, John Deere saved more than $1 billion by employing a new analytical tool to better optimize inventory.

However, only a handful of companies are using analytics as a foundation for their business strategies. Capital One is among those full-fledged analytical competitors. The financial services provider is very open about its use of data analysis to differentiate among customers based on credit risk, usage and other characteristics, and to match customer characteristics with appropriate product offerings. Harrah’s, the world’s largest gaming firm, is another aggressive analytical competitor, particularly in the area of customer loyalty.

Research by global management consultancy Accenture found that high-performance businesses — those that substantially outperform competitors over the long term and across economic, industry and leadership cycles — are twice as likely to use analytics strategically compared with the overall sample, and five times more likely to do so than low performers.

Common to all those aspiring to that level of competitiveness is the need to focus on developing four fundamental assets:


 * Committed senior executives: Taking a broad analytical approach to business calls for big changes in culture, process, behavior and skills for many employees. Such changes must be spearheaded by senior executives who are passionate about analytics and fact-based decision-making.
 * A strong base of skills in use of data: It is very important to have a broad base of employees who are data-savvy — or who can quickly become data-savvy. This calls for hiring, training and rewarding for analytical skills, especially at management levels. It also highlights the need to understand where those skills matter most and where they will matter most in the future.
 * Fact-driven business processes: Analytical competitors begin with “a single version of the truth” — not the conflicting views of the same metrics that stymie other companies. What’s needed is an integrated, cross-enterprise view of the data — a state that may require business process redesign on a broad scale.
 * Technology to capture, sort, and make sense of the data: The processing power to support an analytics thrust is readily available. There is wider use of dedicated “business intelligence appliances” — supercomputer — like machines that can quickly find and sort data in large databases and analyses. Much of the necessary analytical software is also available. “Real-time BI,” in which automated decisions are embedded in operational business processes, is gaining ground.

Example
Netflix, the movie rental company, relies on analytics to drive its growth. At the heart of its business is a movie-recommendation “engine” based on proprietary software. Cinematch, as the tool is called, analyzes customers’ choices and feedback on the movies they have rented — more than a billion ratings of movies they have liked, loved, or hated — and then recommends movies in ways that optimize both the customer’s taste and Netflix’s inventory.

Analytics also help Netflix decide what to pay for the distribution rights to DVDs, essentially giving the company a powerful information advantage during negotiations. For example, when Netflix bought rights to Favela Rising, a documentary about Rio de Janeiro musicians, company executives knew that a million customers had rented 2003’s City of God, also set in Rio. About half a million had rented the Oscar-winning documentary Born Into Brothels, and 250,000 had seen both. So Netflix paid a fee based on 250,000 rentals. If it rents more than that number of copies of Favela Rising, the film’s producers and Netflix split the upside.

History
Data analysis has been used in business since the dawn of the industrial era — from the time management exercises initiated by Frederick Winslow Taylor in the late 19th century to the measured pacing of the mechanized assembly lines developed by Henry Ford. But it began to command more attention in the late 1960s when computers were used in experiments to aid decision-making. These earliest “decision support systems” addressed repetitive and non-strategic activities such as financial reporting. (One notable exception was at American Airlines, which depended on Sabre, its breakthrough yield management system, to beat its competitors.) Analysis of statistics became more routine in the 1970s with the arrival of packaged computer applications. But few executives embraced the strategic use of data; number-crunching was left largely to the statisticians.

Since then, analytics have evolved with the development of enterprise resource planning (ERP) systems, data warehouses, and a wide variety of other hardware and software tools and applications. But until recently, companies have focused on analyzing historical data rather than developing predictive analytics for decision-making.

Many companies today are collecting and storing a mind-boggling quantity of data. In just a few years, the common terminology for data volumes has grown from megabytes to gigabytes to terabytes (TB) — a trillion bytes. Some corporate databases are even approaching one petabyte — a quadrillion bytes — in size. The 583TB in Wal-Mart’s data warehouse, for example, is far more than the digital capacity needed if all 17 million of the books in the U.S. Library of Congress were fully formatted. Gargantuan storage facilities are not the only technological frontier: statistical software, high-end 64-bit processors, and specialty “data appliances” can quickly churn through enormous amounts of data—and do so with greater sophistication.

Awareness Of Analytics
Executives are increasingly aware of the power of information technology to help make better decisions. More than 30 percent of senior managers polled in a 2006 study by Accenture said they use their enterprise systems for “significant decision support or analytical capability”; four years earlier, only 19 percent agreed with that statement.

There is also growing sensitivity among executives to the relevance and utility of business intelligence. A 2007 Gartner survey of chief information officers found that BI is the top technology priority for IT organizations for the second year in a row. Only 36 percent of CIOs surveyed by Gartner believe that management is using the right information to run the business.

Gartner has identified four strategies that CIOs should pursue: technical excellence; enterprise agility; information effectiveness; and innovation. The third strategy involves using analytics and applying information to how business decisions are made, rather than how information is moved around a company’s computer systems.

In most cases, the top performers have already mastered those strategies. As they develop their analytical capabilities further, they are increasingly migrating toward more powerful techniques such as predictive modeling, forecasting and optimization.

Challenges Of Analytics
Analytics is dependent on data. If there is no data, there can be no analytics. However, if data is sparse or non-existent, an organization can conduct surveys or a census to obtain data. In many cases to save expenses, organizations can look for data obtained from situations that are similar but not quite meet the current requirements, and make minor modifications (i.e.,a company trying to introduce an energy drink for the first time in a town can use existing data from a survey of athletes that drink carbonated beverages). However, in these cases, businesses should be aware of the risks inherent in using data obtained in such manner.

For many organizations aspiring to be analytical competitors, the primary problem is not that they lack data. It is that they must contend with dirty data. The challenge is that they do not know which data is trustworthy — “clean” — and which contains duplicates, outdated records and erroneous data entries.

According to Gartner, an alarming proportion of all business data is inaccurate. The research firm estimates that at least 25 percent of critical data within Fortune 1,000 companies will continue to be inaccurate through 2007. In a separate study by a leading accounting firm, only a little more than a third of executives were “very confident” in the quality of their corporate data.

A company that finds it has poor-quality data should postpone any plans to compete on analytics and instead should fix its data first. UPS demonstrates the patience that is often necessary. Although the delivery company has been collecting customer information for more than five years, it took more than half that time to validate that data before it was usable.