Gartner's maturity model gives data and analytics leaders a way to identify the level of development their BI and analytics initiative must reach in order to support enterprise goals. The model also helps chart a roadmap for improvements.
A BI program includes people, skills, processes, metrics and other components, as well as technologies.
As the BI program matures, the architecture will evolve together with the processes and skills needed to support it.
Organizations at lower maturity levels often have siloed processes, disparate systems and data sources, and are heavily reliant on spreadsheets.
Organizations at higher maturity levels have integrated processes, use agile development, set enterprise standards for data and systems, and combine internal data with external data.
Use this maturity model to talk to business managers about the value of increasing the maturity of your BI and analytics program.
Always seek sponsorship from business or corporate managers for any effort to increase the maturity of your program.
Conduct a maturity assessment using Gartner's ITScore for Business Intelligence and Analytics diagnostic tool to establish a starting point, then periodically reassess to ensure that you are moving toward higher levels on pace with the established business analytics roadmap.
Table of Contents
Overview of Maturity Levels or Phases
- Level Descriptions
How to Use This Assessment
Gartner Recommended Reading
- Figure 1. BI and Analytics Maturity Model
Many enterprises have begun to take a strategic approach to business intelligence (BI) and analytics. This is because the individual projects that prevailed in the past have created silos of information without giving managers the wider insight they need to make good decisions. However, enterprises cannot enact a strategic approach in one simple step; it takes time to build all the skills needed for the right BI and analytics program. New methods and concepts, such as agile development and bimodal IT, as well as technical innovations, like cloud, mobile, data discovery and big data, increase the need for organizations to evolve their BI and analytics maturity. BI and analytics leaders should consult Gartner's maturity model to understand the five levels of BI and analytics maturity, to identify their enterprise's current level of maturity, and to determine what steps the enterprise must take to move to the next level. This research describes the maturity model itself. A companion Toolkit enables clients to diagnose the maturity of various aspects of their BI and analytics program.
Figure 1 shows the rising levels of maturity for a BI program that includes people, skills, processes, technologies and other components listed on Gartner's enlarged BI framework (see "How to Architect the BI and Analytics Platform" ). The maturity model assumes a portfolio that includes traditional BI applications — such as ad hoc query, reporting, dashboards, online analytical processing (OLAP), data integration and data warehouse — prebuilt analytic applications (for example, customer service analytics), as well as newer capabilities (such as data discovery, big data platforms, data lakes and advanced analytics). As the program matures, the technical architecture — along with the processes and skills needed to support it — will evolve.
Source: Gartner (September 2015)
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.
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.
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.
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.
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.
Since its introduction in 2010, Gartner's ITScore for BI and Analytics has been used by more than 800 organizations. Gartner's ITScore diagnostic tool can help clients assess the maturity of their BI and analytics programs. The tool consists of about 20 questions that focus on five aspects of these programs:
Platform — data, BI and analytics tools and technology
Each question offers five possible answers (corresponding to the five levels of the maturity model). The answers describe conditions that are typical of a BI and analytics program. Clients should choose the answer that best describes the current state of their enterprise's program. When the client has answered all the questions, the tool will calculate a score (from one to five) for each of the five dimensions and the results will display in a spider diagram.
Clients should use the results to determine the next steps they need to take to increase the maturity of their programs. Maturity will vary between aspects for most enterprises, and the ratings on the five aspects will show where the enterprise lags. For example, an enterprise that is relatively strong in business drivers and tools may be weak in processes and program management, so BI and analytics leaders should focus more on the latter two aspects to raise the maturity of the overall program. The ITScore assessment contains recommendations for actions around the five aspects that are appropriate for advancing the enterprise from one level of maturity to the next.
Go to Gartner's ITScore Diagnostic Tool
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