Competing on Analytics: The Architecture of Business Intelligence

The size of some corporate databases is even approaching one petabyte (a quadrillion bytes). While improvements in technology's ability to store data have been astonishing, most organizations struggle to manage, analyze and apply it.

Companies such as Capital One and Continental Airlines have been able to turn this mass of data into a competitive advantage by using business intelligence to make better decisions and to extract maximum value from their business processes.

Building a robust analytical capability requires much more than just collecting and storing data in large quantities. There are many moving pieces to put in place, including software applications, technology, data, processes, metrics, incentives, skills, culture and sponsorship. An important initial step for any organization is to understand the elements of a business intelligence architecture so that structure can be imposed on otherwise chaotic bytes of data.

The term "business intelligence" (often shortened as BI) encompasses analytics as well as the processes and technologies used for collecting, managing, and reporting decision-oriented data. The business intelligence architecture (a subset of the overall IT architecture) is an umbrella term for an enterprise-wide set of systems and governance processes that enable sophisticated analytics, by allowing data, content and analyses to flow to those who need them, when they need them.

Top management, functional heads, knowledge workers and statisticians all need information at various times and in various forms. The BI architecture must be able to quickly provide users with reliable, accurate information and help them make decisions of widely varying complexity. It also must make information available through a variety of distribution channels, including traditional reports, ad hoc analysis tools, corporate dashboards, spreadsheets, e-mail and pager alerts. This task is often daunting: Amazon.com, for example, spent more than 10 years and $1 billion building, organizing and protecting its data warehouses.

As part of Accenture's research into high-performance businesses, we have found that a growing number of companies have recognized the power of leveraging data-driven insights through the use of business intelligence. Some forward-thinking companies have gone a step further and are building their competitive strategies around analytics—that is, the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Accenture's High Performance Business research found a powerful link between organizations with pronounced analytical orientations and market out-performance.

Breaking the business intelligence architecture into its six elements can help IT executives leverage the analytical power of their IT investment:

1. Data management
2. Transformation tools and processes
3. Data repositories
4. Analytical tools and applications
5. Presentation tools and applications
6. Operational processes

Data management
Data management ensures that an organization has the right information and uses it appropriately. We found that companies that compete on analytics devote extraordinary attention to data management processes and governance. Capital One, for example, estimates that 25 percent of its IT organization works on data issues.

While data management can be a large effort, the payoff can be more than worth it. For example, Continental Airlines integrates 10 terabytes of data from 25 operational systems into its data warehouse. The company estimates that it has saved more than $250 million in the first five years of its data warehousing and business intelligence activities—representing an ROI of more than 1,000 percent.

Transformation tools and processes
For data to become usable by managers, it must first go through a process known in IT-speak as ETL, for extract, transform and load. While extracting data from its source and loading it into a repository are fairly straightforward tasks, cleansing and transforming data are not. The first step is to clean and validate data using business rules and data cleansing tools. For example, a simple rule might be to have a full nine-digit ZIP code for all US addresses.

Data repositories
Organizations have several options for organizing and storing their analytical data. Data warehouses are regularly updated databases that contain integrated data from different sources. Data marts are used to support a single business function or process and usually contain some predetermined analyses so that managers without statistical expertise can slice and dice some data. A metadata repository contains technical information and a data definition, including information about the source, how it is calculated, bibliographic information and the unit of measurement. It may include information about data reliability, accuracy and instructions on how the data should be applied. A common metadata repository used by all analytical applications is critical to ensure data consistency.

Analytical tools and applications
Choosing the right software tools or applications depends on several factors. The first task is to determine how thoroughly decision making should be embedded into business processes. Should a decision be automated or made by a person? Next, companies must decide whether to use a third-party application or create a custom solution. According to IDC, projects that implement a packaged analytical application yield a higher ROI than custom development using analytical tools.

Presentation tools and applications
Business intelligence will only work if people can impart their insights to others through reporting tools, scorecards and portals. Presentation tools should allow users to create ad hoc reports, to interactively visualize complex data, to be alerted to exceptions through communication tools (such as e-mail, PDAs, or pagers) and to collaboratively share data. A new generation of intuitive visual tools allows managers to do sophisticated analyses without any statistical skills and without danger of altering the underlying data.

Operational processes
This element of the BI architecture answers questions about how the organization creates, manages and maintains data and applications. Issues such as privacy and security as well as the ability to archive and audit the data are of critical importance to data integrity.

Top management can help the IT architecture team plan a robust technical environment by establishing guiding principles for analytical architecture. Those principles can help to ensure that architectural decisions are aligned with business strategy, corporate culture and management style. Working with IT, senior managers must establish and rigorously enforce comprehensive data management policies and they must emphasize that the business intelligence architecture should be flexible and able to adapt to changing business needs and objectives. A rigid architecture will not serve the needs of the business in a fast-changing environment.

About the Authors

Jeanne G. Harris is executive research fellow and senior executive at the Accenture Institute for High Performance in Boston, Massachusetts.

Thomas H. Davenport is the former director of the Accenture Institute for High Performance. He currently holds the President's Chair in Information Technology and Management at Babson College in Wellesley, Massachusetts.

Their new book, Competing on Analytics: The New Science of Winning, is available now.