In several columns this year and last, I've discussed success criteria and pitfalls to avoid in implementing an enterprise data management (EDM) initiative. This month, I'd like to continue that theme but narrow the focus to one of the more critical components of EDM - data governance (DG). I'm going to discuss how effective DG "looks" when it is practiced.
You can install market-leading systems and data integration and analytics technologies, and you can overhaul your business processes to derive your defined future state of process excellence, but if you don't have a sound DG strategy and organization in place, all your work may be for naught. Why? Because how well you administer (i.e., govern) the data that passes through all that technology and those processes is, in my opinion, one of the key determinants of the effectiveness of most EDM projects.
Effective Data Governance
Effective DG starts with effective data ownership, oversight and stewardship. However, companies that have no - or poor - EDM processes and technology in place usually lack clear ownership for organizational data definitions and have no data stewardship. Indeed, the data environment is often chaotic, and no one has a clear idea of where data resides in the company, who "owns" the definitions or how data flows through the company's information systems.
On the other hand, in companies that practice DG excellence, ownership of and stewardship responsibility for critical data elements are clearly defined. Theses companies have formal, rigorously enforced corporate-wide data oversight processes and clearly defined standards. Consequently, metadata, master data and all operational and analytical data is easily accessible and standardized across the company. There are also data stewards who are responsible - and held accountable - for definition and oversight of the policies, procedures and requirements for critical data elements (rolling up to the entity level) across the company.
Effective DG also has a data quality component. Data quality is often the Achilles' heel of companies that don't have a formal EDM structure in place. There is frequently a lack of or poorly defined formal data quality processes or quality metrics. Alternately, companies that practice sound DG principles have effective data quality processes and quality metrics in place. The well-defined data processes are focused on the integrity of activities such as data entry, change management, improvement and migration. The metrics are used as a data quality measure to help identify any potential problems before they propagate through the company's information systems.
DG also has a technology element. To be sure, DG is a people and process issue, but organizations that practice effective DG have a few commonalities in their technical architectures. They are:
- A single, enterprise-wide data repository that feeds all organizational data warehouses, data marts and analytical applications.
- A single extract, transform and load (ETL) tool to move data from source systems into the enterprise data repository. This eliminates the chaos caused by using different tools and technologies to move data through enterprise systems.
- A service-oriented architecture (SOA) that provides a flexible, scalable environment for data to move through the enterprise from source system to end user that can grow and change with the company.
Analytic and reporting tools and standards are also components of effective DG. In companies that don't practice effective DG, there are often numerous competing technologies that enable analytics and reporting. These multiple tools tend to foster the segmenting and siloing of data that results in an incomplete picture of corporate performance and no single version of the truth about organizational data. Conversely, in companies that have a first-rate DG organization, there are enterprise-standard analytical and reporting tools deployed using enterprise-wide standards. Employing standardized analytic and reporting tools can enable companies to tear down data silos and share information across the enterprise.
I know you may be wondering what most of this data quality, technical and process discussion has to do with DG. It is simply that without a DG organization that has a clear charter and ongoing executive sponsorship and that employs effective DG processes and data stewardship, it is possible that your enterprise information architecture will resemble Hercules' multiheaded Hydra, with an almost unending set of problems. It will be very difficult to move data through enterprise information systems to provide the right information to the right people at the right time and in the right format. With sound DG, however, it is more likely that you'll be able to spend less time fighting the Hydra and more time creating good customer relationships, and understanding and growing the business!
This publication contains general information only and Deloitte Consulting LLP is not, by means of this publication, rendering business, financial, investment or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional adviser. Deloitte Consulting LLP, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this publication.
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