So far I’ve provided an overview on 6 different data governance maturity models which I will post links below in case you’d like to learn more about them. Maturity models can provide a road map to follow in order to advance from one maturity stage/ level to another. Today I’ll provide an introduction into the Kalido data governance maturity model, but here are the other ones covered so far:
- Stanford’s Maturity Model
- IBM’s Maturity Model
- DataFlux’s Maturity Model
- Gartner’s Maturity Model
- Oracle’s Maturity Model
- Open Universiteit Nederland Maturity Model
Kalido Data Governance Maturity Model
Overview: Published in September 2010, the Kalido data governance maturity model is based on Magnitude’s own market research with more than 40 companies at varying stages of maturity. Similarly to DataFlux, it has 4 stages, which map to the evolution of how organizations treat data assets. The model also offers a free online self-assessment tool which I will link to at the end of this article.
Here are the stages with their characteristics, outlined across 3 areas:
Level 1 – Application-Centric
At this stage, some organizations attempt to govern data through enterprise data modeling, which is mostly an academic exercise. Efforts are mostly driven by IT without the broad organizational support and authority to enforce compliance.
- Authority and data stewardship do not exist
- There is little or no collaboration between IT and the business
- Business views data as IT’s responsibility
- No processes in place for data governance
- Models of data and business processes as well as rules are entirely embedded in applications
- There are no tools for modeling, managing, and ensuring data quality
- There are no repositories that capture enterprise-wide, cross-functional views of data
Level 2 – Enterprise Repository-Centric
Data governance is typically siloed around individual enterprise repositories, such as a data warehouse or an Enterprise Resource Planning (ERP) system. Also, governance is informal, lacking a distinct organizational structure and clearly defined and executed processes.
- Some authority for data exists in IT
- No official recognition of data stewardship, nor defined roles and responsibilities
- Inconsistent collaboration between IT and business when it comes to data
- Loosely defined processes exist around enterprise repositories (ex: a data warehouse, master data hub, and large operational systems)
- Data issues are tackled reactively without addressing the root cause
- No institutionalized process for making enterprise-wide, business centric decisions for data
- Data warehouses and/or master data hubs exist
- Investments in data quality and metadata tools are made around these systems
- Managing data across multiple systems follow a bottom-up approach with limited influence
Checkout this online course on Data Governance Maturity Model(s) to learn all there is to know about them, the best practices and pitfalls on selecting and using them, and so much more.
Level 3 – Policy-Centric
Rather than envisioning ever-larger and more encompassing repositories, organizations put processes in place for defining, implementing and enforcing policies for data. It is acceptable for the same type of data to be stored in multiple places as long as they adhere to the same set of policies. Enterprise repositories continue to be important, but they’re built on governed platforms integrated with enterprise data policies.
Business takes increasing responsibility for data content, and data is widely recognized as one of the most valuable corporate assets throughout the organization
- A cross-functional data governance council is formed
- Data stewards have defined roles and responsibilities and are explicitly appointed
- Business is engaged in managing data
- Data is seen more and more as an asset
- Processes for policy definition, communication and enforcement are implemented
- A clear process for reporting and tracking data issues is established – Check out our free data quality issues log template
- Key enterprise data repositories are governed by a single, streamlined set of governance processes
- A centralized repository of data policies exists and sets policies in a top-down fashion
- The process of data governance is supported using an automated workflow
- Data quality is regularly monitored and measured
Level 4 – Fully Governed
- The data governance organizational structure is institutionalized
- Data governance is seen as business critical and has the same level of importance such as HR and Finance
- Business takes full ownership for the data and data policy making
- Data governance is a core business process and always taken into account
- Decisions are made with quantifiable benefit/cost/risk analysis
- Business policies for data model, data quality, security, lifecycle management are integrated with user interactions with data
- Centrally defined policies and rules drive behavior of systems where possible
- Data are monitored and issues are addressed proactively
Take away: This model argues that the organization, process and technology need to advance in lock-step for data governance to be successful. They offer an online self-assessment tool, listed below, but be aware that the results may not all fall in a single stage.
- Kalido Whitepaper
- Kalido Maturity Assessment
- Magnitude’s “Why do you need another maturity model” article
Next I’ll go over the TDWI Data Governance Maturity Model.