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.