ITScore Overview for BI and Analytics

Characteristics

At this level, BI and analytics occur ad hoc. There are no formal decision-making processes or practices. Typically, executives and managers ask for information, and users scramble to provide it with any operational application that is available. The enterprise has no information infrastructure. No one has defined processes for analytics or decision making, or performance metrics. This approach prevails because it costs little to get started.

Characteristics

At this level, business units undertake every BI or analytics project individually to optimize a process or to help make tactical decisions. Each project or domain has its own information infrastructure, tools, applications and performance measures. Therefore, different applications proliferate across the organization, each one guided by its own team of IT workers, business application users and operational managers. These people do little or no process modeling; they use data integration tools, analytic capabilities, databases and BI platform capabilities — maybe acquired in one packaged application. They deliver results via reports, ad hoc query and dashboards. To feed these applications, they create single-subject data marts with simple aggregates of information and data models, hand-coded SQL extracts, and, perhaps, some data quality technology. Any packaged analytic applications have domain-specific business content.

Characteristics

At this level, people, processes and technologies start to become coordinated across the enterprise. A senior executive, usually from the business side, becomes the enterprise champion for BI and analytics. Process managers and IT leaders oversee projects across multiple business processes that need to share analysis and decisions (for example, financial or marketing processes). Users make decisions based on multiple streams of data to determine trade-offs. Most enterprises implement a BI competency center (BICC) or analytics center of excellence consisting of business users, IT professionals and analysts to share expertise and improve consistency for specific applications or uses of information. Technology standards start to emerge, including for information infrastructure, data warehouses and BI platforms. Such standards are not necessarily mandated, but are preferred for economies of scale and improved support. Nevertheless, the enterprise's projects do not consistently share data or analytic models. At most, one or two processes share a common master data model, and metadata becomes federated for each technology. For example, data integration tools share a particular metadata schema, while BI platforms share a different one. Little sharing of analytic and decision processes, components and resources occurs. Some sharing of performance measures occurs across processes, mostly to help individual business units, but these do not link to enterprise goals.

Characteristics

At this level, top executives become the program's sponsors. This may be the CEO (directly) in smaller organizations, or multiple executives (including the CFO, CMO and COO) in larger organizations. The enterprise has defined a framework of performance metrics that links multiple processes to enterprise goals. These metrics guide enterprise strategy. BI applications support cross-functional or enterprisewide decision processes. Corporate and operational executives can see cause-effect relationships among key activities. Everyone, from analysts to business managers and senior executives, uses the same BI and analytics systems. An enterprise information architecture guides the design of new systems. Enterprise information management (EIM) and information sharing mature and receive significant funding. The enterprise exhibits a high degree of discipline around BI and analytics projects. Teams pursue projects with sophisticated processes and skills for requirements' definition, modeling and program management that includes agile development and rapid prototyping. Common data models, rules and analytics minimize the number of versions of a given set of information.

Characteristics

At this level, BI and analytics have become a strategic initiative, jointly run by the business and IT organization and supported and governed at the highest levels of the organization. The CEO sponsors the program, or the role of chief analytics officer (CAO) or chief data officer (CDO) may have been established. The enterprise thinks about information as a strategic asset and uses BI and analytics to generate revenue, operate efficiently or provide best-in-class customer service. The enterprise has completed its performance metrics framework and even extended it to include partners and customers (for example, to measure the performance of the supply chain). While lower maturity levels are focused on internal processes and measurements, the focus here is ultimately on business value. All of these stakeholders use the information from BI and analytics systems to coordinate a response to changing business conditions across the whole value chain and to make transformational decisions. Users come from multiple levels within the organization, multiple business units and multiple geographies, as well as from customers and partners. They all trust the information and analysis that systems generate as the basis for making decisions in pursuit of the enterprise's strategic goals. EIM and information sharing have become sophisticated. All projects use standard processes and models, with some customization for the needs of particular projects or regions. Decision processes include decision simulations that incorporate decision-making best practices and optimization technologies.