BI and Analytics Center of Excellence (CoE): Roadmap for Execution

In every corporation, data volume, variety, velocity, and complexity are rapidly increasing. Big data is finally moving from a strategy buzzword to a mainstream execution.

BI CoE (often called Analytics CoE, Big Data CoE, or Integration CoE) is an organizing mechanism to align People, Process, Technology, and Culture. The target benefits include:

  • Better collaboration between Business and IT
  • Increased adoption and use of BI and Analytics in the lines of business
  • Better data management, quality, and reporting
  • Cost savings from eliminating redundant functions

CoE elements include:

  • Organization & Culture
  • PMO Activities
  • Deliverables
  • Roles & Responsibilities
  • Best Practices & Techniques
  • Technology Platforms


Figure 1.1

BI Shift: Moving from Department Focus to Enterprise

As BI moves from departmental focus to enterprise level, the BI CoE structurally may take three different models depending on reporting structure.

  1. They can be part of an IT unit reporting to the CIO.
  2. They can be a functional shared services model.
  3. They can be part of the corporate shared services model that is leveraged by all divisions.

Center of Excellence (CoE) also makes sense in environments where BI is moving from a departmental project focus to a strategic program focus. A tactical “Project” focus has these characteristics:

  • Start & end date with narrow problem or department scope/focus
  • Sub-optimal organization and use of skills
  • Information & decision latency inadequately addressed

A more strategic “Program” focus has these characteristics….

  • Whole-process view; promoting collaboration and analytic best practices
  • Data and analytics managed as strategic assets (shared services)
  • Business and IT share ownership of the information environment
  • A central point for developing and evolving the analytical infrastructure

If you have a charter to facilitate and promote BI and analytics to achieve business objectives across functional and geographic areas, then the CoE model is the right one for you. Centers of Excellence, especially when applied to BI, analytics, and performance management processes, can help to address the pain of fast-moving technology, time-to-market compression, and marketplace dynamics that grow more complex by the day.

The complexity of a typical BI and Analytics stack today is striking. Without a dedicated team and pooled expertise, it is hard to imagine how any large organization can navigate the landscape below, which is only the technology view. If you add the business/functional apps layer then you have an entirely different, more complex view.


Figure 1.2

Managing the Complex Information Stack

The biggest challenge facing corporations is the explosion of data and tools (see figure 1.2).

The complexity of the information management stack in a large enterprise can be overwhelming. The need to simplify, consolidate, and leverage is often a core driver for establishing a BI CoE. The opportunity is in helping different business units and functions to capture, analyze, and manage all of the data and disparate technology tools/platforms.

Raw Data -> Aggregated Data -> Contextual Intelligence -> Analytical Insights -> Decisions” is a differentiating causal chain which is separating winners from the rest. Bringing consistency, repeatability, reuse, and process to this information supply chain requires discipline.

As companies have become more global and complex – and simultaneously more integrated – the need for cross-collaboration and more leverage of available resources via a shared services model has become a priority. That is the reason why best-in-class firms are implementing a BI CoE (also called BI Shared Services or BI Competency Centers) for better leverage of investments in people, process, and tools/applications.

Organizing a BI/Reporting/Analytics CoE


Figure 2.1

Most organizations have a mix of BI platforms today. They can be departmental silos, integrated platforms, and next generation “data-as-a-service” enabling models. A BI CoE must eventually support and manage all of these models as legacy, current, and new.

The objective of establishing BI CoE’s is to create economies of scale by pooling and sharing expertise, people, processes, and tools/applications rather than it becoming dedicated (or trapped) in departmental, Line of Business (LoB), or functional silos.

The BI CoE defines the structure – roles, responsibilities, and processes – and the resource mix – onshore or offshore, analysts to administrators – which both enable the better execution of enterprise-wide projects (especially in global, multi-LoB, or matrix organization models). The shared services model is becoming fairly common in public and private sector IT for a variety of apps and infrastructure programs – centralized e-mail, data centers, SAP CoE, Oracle CoE etc.

An analytics CoE builds on the BI platform and provides an integrated environment for predictive and descriptive modeling, data mining, text analytics, model management, forecasting, optimization, simulation, experimental design, and more.

The concept of a BI CoE is not new. As early as 2001, Gartner wrote that companies need a BI Competency Center approach (BICC) to develop and focus resources in order to be successful. Since then, the BICC concept has evolved through various implementations.

What does a BI CoE Actually do?

A BI CoE has responsibility for the program and portfolio governance, projects, vendor management, practices, software, architecture, infrastructure, and software licenses. It is responsible for building and executing the plans, priorities, infrastructure, and competencies that different groups need. Typical range of functions in a BI CoE include:

  • Contract management
  • Management of licenses
  • Support/Help desk BI
  • Business analytics
  • Data model management
  • Data warehouse administration
  • Architecture
  • Solution Management – new features, enhancement, and upgrades
  • Production system management
  • Consulting to business units
  • Prioritization of BI projects
  • Management of Strategy
  • Training and Change Management

A well-structured BI CoE drives various enterprise-wide data integration initiatives, including data warehousing, data migration, data consolidation, data synchronization, and data quality, as well as the establishment of data hubs, data services, cross-enterprise data exchange, and integration competency centers.

There are a variety of roles within a BI CoE – Executive Sponsors, BI Leadership, Program and Project managers, Business Analysts, Architects, Administrators, Developers, Data Stewards, Data Modelers, and Data Warehouse Analysts.

A BI CoE’s influence transcends that of a typical business unit implementation, playing a crucial central role in creating the framework for Service Level Agreements (SLA), business change management, and strategic roadmaps. The BI CoE via the PMO, ensures that information and best practices are disseminated and shared through the entire organization so that everyone can benefit from successes and failures.

The BI CoE also plays an important change management role facilitating interaction among the various geographies, cultures, and units within the organization. Knowledge transfer, enhancement of analytic skills, coaching, and training are central to the mandate of the BI CoE.

Figure 3.1 from Data Management Association ( captures the data management foundational elements and the overarching management elements that need to be in place to pull it together.


Figure 3.1

BI CoE – Managing the Project Lifecycle

A key aspect of the BI CoE is the ability to manage and make critical decisions across the end-to-end lifecycle of the project. This can be difficult in a large corporation with multiple stakeholders.


Figure 4.1

Creating the Business Case for BI CoE

BI and analytics investments are skyrocketing as data volumes, variety, and velocity grow exponentially. To manage these investments an organized BI CoE structure makes sense. The business case drivers include:

  • Operational efficiency — More effective support and use of information. More relevant, accurate, consistent, and timely information.
  • Strategic transformation — Use information to transform enterprise performance.
  • Lower Total Cost of Ownership (TCO) — More efficient use of technology. Remove technical and operational obstacles and redundancies.

Despite the drivers, the best-practice companies undertake BI CoE models after a few successful implementations of BI at various departments and business units. The business case is to ensure the following:

  • Ensure BI investments are closely tied to enterprise strategy (Cost Savings, Cost Avoidance, Risk Avoidance, and Better Reporting)
  • All BI Infrastructure, Platforms, Tools, Applications, and People investments are coordinated
  • There is a singular focus and direct accountability for BI, analytics, and performance management programs.


The time of BI CoE has come. “Data -> Intelligence -> Insights -> Decisions” is a core causal chain in business execution.

The previous execution of BI was about recording transactions in large data warehouses with rather simple front-end analytics. Today, BI applications focus around users extracting, manipulating, and analyzing data on demand to create insights and make better decisions.

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