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A Five Level Maturity Model
Most organizations have some form of data and around this data they have some form of data management function in place. However with increasing levels of automation, compliance requirements and better data awareness, there is a need to move from one level of data management to the next higher level. To achieve this we need to know where we currently are and where we want to be. A maturity model specific to data governance can be the right tool to aid in this assessment and is the topic of this article.
Benefits of Data Governance
An organization can realize the value of data through data governance. It can instill confidence in the organization's data to the customers, vendors and the organization's staff. Data governance can spread greater awareness of the organization's data thus achieving better impact analysis. We can achieve better data integration, reduce and over time eliminate silos of data. Data governance can help tackle compliance issues such as Sarbanes-Oxley and Basel II and help manage their requirements. Data governance can reduce the risk of not meeting operational goals and reduce the risk of making decisions based on bad data.
Need for a Data Governance Maturity Model
For organizations with some form of data governance in place, it becomes necessary during the as-is analysis to identify the current state. Also, when suggesting a roadmap, it will be necessary to benchmark that against some kind of standard. A Data Governance Maturity Model can serve as this quintessential benchmark to do the as is, and also for preparing a data governance roadmap to reach the to-be state in the data management context.
The Five-Level LIPHS Data Governance Maturity Model
The Data Governance Maturity Model has five levels of evolution with the enterprise data governance program. The five levels are :
- Level 1 ¡ú Local
- Level 2 ¡ú Impose
- Level 3 ¡ú Process
- Level 4 ¡ú Holistic
- Level 5 ¡ú Supply-Chain-Wide
The Acronym for the above Data Governance Maturity Model is LIPHS
Let us now discuss each of the Maturity Levels of Data Governance in brief :
- Level 1 (LOCAL):
Key Characteristics: Unorganized, ill-defined and localized.
At this level the organization usually relies on the IT department for all their data assets' related issues. There is disconnect between the IT and business departments with respect to data assets. Data governance is very much local to each department.
- Level 2 (IMPOSE):
Key Characteristics: Well defined, siloed and imposed.
At this level, there is a greater awareness to the need for data definitions and standards. However, though the data definitions and data governance program are well defined, this standardization is siloed because some divisions are trend setters, while others are "follow the proven processes" types, and still other divisions are process laggards. At this level there is a greater need to impose a data governance culture within the various divisions of the organization through channels such as data stewards, training and evangelizing.
- Level 3 (PROCESS):
Key Characteristics: Integrated, process-oriented and ownership.
At this level, the data governance program is more process oriented (and less personnel-dependent). The focus is on data ownership through the creation of a data stewardship framework. Here the organization looks at a uniform rollout of an integrated data governance program across all divisions and/or departments. It includes the creation of a formal data governance council, steering committee and lifecycle program to ensure a uniform, enterprise-wide data governance program.
- Level 4 (HOLISTIC):
Key Characteristics: Holistic, proactive , quantifiable and incentivised.
At this level, data governance expands in scope to look at data assets from multiple viewpoints/dimensions and from multiple data touch points within the organization. The formal data governance stewardship structure becomes well versed with data governance processes and evolves into a Data Governance CoE (Center of Excellence).This results in a more proactive approach to implementing and operating the data governance program. The data governance process becomes well-defined, thus enabling the application of a metrics approach to evaluating, monitoring and administration. The metrics approach helps manage incentives across divisions and organization hierarchies.
- Level 5 (SUPPLY-CHAIN-WIDE):
Key Characteristics: Involves suppliers and customers, self-maintaining, aligned with the industry standards, continuous improvements.
At this level, the data governance program almost runs on "auto pilot" with minimal intervention from the executive committee or steering committee. The data governance program goes beyond the enterprise to include customers and suppliers thus evolving to align with the industry standards for data governance. This enables the governance program to interact with external data governance programs through data exchanges. The data governance program becomes "agile" enough to absorb continuous improvements.
Usage of the Five-Level Data Governance Maturity Model
Data Governance consultants can conduct an assessment based on the characteristics of each level of the maturity model and then determine the current data governance maturity level of the organization. The model can also be used to prepare a roadmap for an organization to show the current and desired states of data governance maturity. This can help the organizations to prioritize their governance initiatives from a people, process, technology and budget standpoint.
Summary
As most organizations currently have some form of data management, the next step is to ensure that the data is of good quality, data is complete and data is delivered on time to the right place. This calls for a more process-oriented approach to data management. This can be facilitated with the formal use of people, processes and technologies through a data governance initiative. Subsequently it becomes even more important to assess the current state of data governance in an organization vis-¨¤-vis industry standard data governance practices. The five-level LIPHS Data Governance Maturity Model will help the organizations to assess their as-is and to-be data governance initiatives toward their goal of delivering the benefits of good quality data that is valuable to themselves and also to their suppliers and customers.
Harikrishna S Aravapalli is a senior technical architect at SETLABS, the R&D division of Infosys Technologies Bangalore,INDIA. H has 14 years of IT experience, mostly in data Warehousing, business intelligence and databases and is a TOGAF-certified architect. He can be reached at Harikrishna_SA@infosys.com.
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