BI Maturity Life Cycle

A BI Strategy is a long term strategy, not a short term, one time project.  As your business continues to change, and technologies improve, this “lifecycle” can reiterate into a completely new form of performance management across the Enterprise.

Are you wondering “what is a BI Maturity Life Cycle”?


Transforming Information into Intelligence

By understanding how data becomes information, and how information can then be transformed into intelligence, real time decision making is supported and gives you a better understanding of how to advance your company by leveraging information.

There are four main phases in Data (DW) and Business Intelligence (BI) Lifecycle:

  • Collection – Infancy
  • Reporting – Adolescence
  • Analytics – Adulthood
  • Visualization – Maturity

Collection (Infancy)

Organizations in this stage of the BI/DW life-cycle have not selected a standard BI tool nor a formal BI application; however management realizes the need to adopt a BI strategy to meet not only the immediate need, but future ones as well.  No formal centralized data management strategy exists.  While transactional/operational data is collected the mechanisms for turning the raw data into useful information about the business has not yet been achieved at an enterprise level.

Impact to Bottom Line:

No BI/DW tools have been adopted as a company/organization standard.

Data creating and collection from:

  • CRM – Customer Relationship Management
  • Custom Application Development
  • Other Enterprise Applications – ERP, HR

Stage 1 Data Requirements

  • Transactional Databases (OLTP)
  • Unstructured Data Files

Reporting (Adolescence)

At this stage of the life-cycle, at least one and quite often multiple tools have been chosen for BI and DW however no standards have been agreed upon or enforced.  Some report writing and data management using the tools has been undertaken.  At this stage the BI applications and the databases are frequently made up of silos of information often disparate in nature, and there is little to no integration of these information assets.

Impact to Bottom Line:

A web-based self-service BI application has not been deployed and no enterprise reporting database model like a data warehouse, data mart or micro-mart has been implemented.

Data Reporting and Export

  • Parameter driven reporting
  • Report distribution via Email and Web
  • Basic data derivation and summation

Stage 2 Data Requirements

  • Transactional Databases
  • Data Replication
  • Data Libraries and Models

Analytics (Adulthood)

At this stage of the life-cycle, a standard BI and DW tool has been chosen and a significant Web-based self-service BI application has been deployed.  Another characteristic of this life cycle stage is an enterprise reporting database model, like a data warehouse, a data mart, or a micro-mart has been implemented.

Impact to Bottom Line:

Companies at this stage have not yet achieved independent and wide-spread end-user access to the data via Web-based ad hoc reporting, and similarly, sophisticated end-user analysis using OLAP tools has not yet been undertaken.

Data Analysis

  • Ad Hoc Query and data analysis by end users
  • Dimensional data analysis (OLAP)
  • Data Transformation, Integration and Lineage

Stage 3 Data Requirements

  • Dimensional Data Design
  • Basic data warehouse, data mart of ODS
  • Semantic Layer or Cubes

Visualization (Maturity)

Common characteristics include a data warehouse (DW) strategy for creating a single version of the truth for important data entities, self-service reporting applications have been deployed via the Web and a facility has been rolled out to the end-users to allow data mining and self-sufficiency in composing their own reports and queries.

Impact to Bottom Line:

The dependency on IT by the business community has been significantly reduced and so has the information backlog. All key decision makers and knowledge workers have access to the data that drives their business which results in unbridled problem identification and opportunity discovery.  KPI’s are visually presented for at-a-glance review by executive management and in general, via common distribution mechanisms (such as the Web and email) & using an assortment of popular output formats (such as HTML, Excel, PowerPoint and PDF) the health of the business is readily observable at any given time.

Data Visualization

  • Metric Dashboards & Alerting
  • Scorecards (Strategies and Goals)
  • Advanced Analysis & Data Mining

Stage 4 Data Requirements

  • Advanced Semantics
  • Advanced Data Warehouse Design
  • Metric Repository