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Analytics — TDWI

Where does an organization stand in its use of analytics and BI, and why do so many teams get stuck halfway?

≈ 2 min readReading
At a glance
  • 5 stages, from nascent to mature.
  • A chasm between early adoption and corporate adoption.
  • 5 categories: organization, infrastructure, data, analytics, governance.
On this page
  1. Profile
  2. Why this matrix matters in my field
  3. The maturity scale
    1. Nascent
    2. Pre-adoption
    3. Early adoption
    4. Corporate adoption
    5. Mature / visionary
  4. The dimensions assessed
    1. Organization
    2. Infrastructure
    3. Data management
    4. Analytics
    5. Governance
  5. How to use it
  6. Limitations and precautions
  7. Sources and verification

Profile#

Organization
TDWI (formerly The Data Warehousing Institute)
Purpose
Place the organization's maturity in its use of analytics and data
Scale
5 stages: Nascent, Pre-adoption, Early adoption, Corporate adoption, Mature / visionary
Dimensions
Five categories in the TDWI maturity assessment: organization, infrastructure, data management, analytics, governance
Particularity
A “chasm” would separate early adoption from corporate adoption
Access
TDWI publications and online assessment; some resources are reserved for members

Why this matrix matters in my field#

This model speaks the language of BI and the data warehouse: it describes how an organization moves from isolated spreadsheets to analytics integrated into its processes. For a BI team, it helps explain why a project that succeeds in one department does not spread on its own: scaling up is an organizational change, not just a technological one.

The maturity scale#

The first three stages reproduce the descriptions published by TDWI. The last two are reworded; consult the TDWI publications for the reference version.

  1. Nascent#

    No analytics, apart from the spreadsheet. A culture not driven by data: decisions rely on intuition. A few people glimpse the value.

  2. Pre-adoption#

    The organization does its homework. One or more departments invest in an isolated BI tool or decision-support database. The value of analysis for decision-making starts to be perceived.

  3. Early adoption#

    Tools and methods are put in place; people think about data management, reports, dashboards. A frequently long transition, with a “chasm” to cross: skills, resistance, ability to act on the analyses.

  4. Corporate adoption#

    Analytics is operationalized in business processes and reaches a growing number of users: it can transform the way the organization works.

  5. Mature / visionary#

    Analytics is a strategic competency, integrated into decisions and innovation; the data culture is established and continuously improved.

The dimensions assessed#

The five categories of the TDWI maturity assessment:

  • Organization#

    Strategy, sponsors, skills, culture and team organization.

  • Infrastructure#

    Technical platforms, storage, processing, tools.

  • Data management#

    Integration, quality, modeling, data access.

  • Analytics#

    Types of analyses performed, methods, distribution and use.

  • Governance#

    Rules, responsibilities, security and compliance.

How to use it#

  1. Assess each category separately: infrastructure may be ahead of the organization, or the reverse.
  2. Identify the stage of the largest number of departments, not that of the best team.
  3. Spot what blocks scaling up: skills, sponsor, governance, shared platform.
  4. Build a roadmap of use cases that help cross the chasm.

Limitations and precautions#

  • The model has had several variants (analytics, big data); specify the version used.
  • The stage describes trends, not measurable thresholds.
  • An organization can be “mature” in one use and “nascent” in another.

Sources and verification#