In my last column, I talked about enterprise data management (EDM) and how important it is to develop and implement an EDM strategy. To refresh your memory, my working definition of EDM is: the ability a company has to successfully locate, retrieve, manage and disseminate all organizational information for use in internal processes and external communication with interested parties. In a nutshell, EDM is the ability a company has to get the right information to people when and how they need it to do their jobs effectively.
I believe the key to effective EDM is comprehensive enterprise data governance. Data governance is just a fancy term for the ability to use IT to standardize data policies across the enterprise so you can gain a reliable view of the data and make better decisions. With an effective data governance plan in place, its possible to:
- Increase data consistency,
- Increase accountability for and ownership of corporate data,
- Achieve more effective data security,
- Mitigate risk vis-à-vis regulatory compliance issues and
- Improve decision-making.
Without effective data governance, its difficult to manage data across the enterprise. If you dont have a data governance program, its a good bet that you dont have an organized system to keep track of organizational data policies. Theres also a high probability that IT and the business dont see eye-to-eye on how to manage enterprise data.
The problem here is that you cant manage what you dont understand. If theres no centralized knowledge and understanding of enterprise data, its difficult to get people to cooperate in managing it.
Now that weve gotten the preliminaries out of the way, lets talk about what constitutes effective data governance. Any data governance effort begins with developing policies for data ownership, control and stewardship. When clear policies are enforced, its easy to tell where the buck stops relative to who or what business unit owns and has stewardship responsibility for key data entities throughout the enterprise. Ownership and stewardship policies are also usually augmented by formal corporate-wide data oversight processes and clearly defined data standards. Consequently, critical data is easily available in standardized form when its needed.
Effective data governance also means that there are formal, standardized data quality processes and clearly defined quality metrics in place throughout the company. These processes govern the performance of daily activities such as data entry, change management, improvement and migration. The metrics are crucial because they can be used as quality checks to identify any potential problems before they proliferate through the companys IT systems.
Effective data governance is also concerned with the technological components that the data flows through. Several components are critical to achieving data governance goals:
- A single, enterprise-wide data repository that serves as the feeder for all enterprise data warehouses, data marts and analytical applications.
- A single extract, transform and load (ETL) toolkit to move and transform data from source systems into the enterprise data repository.
- A service-oriented architecture (SOA) that provides a flexible, scalable environment for data to move through the enterprise from source system to end user. The SOA should be able to grow and change with the company.
Effective data governance also requires high-quality analytic and reporting tools. These tools should be deployed using enterprise-wide standards. Employing standardized analytic and reporting tools enables companies to tear down data silos that may exist and share information across the enterprise.
Once youve implemented an effective data governance program, what do you get for your money? There are several key benefits of effective data governance:
- A clear architecture design for application, technical and data architectures with clear standards for acquiring architecture components.
- A comprehensive metadata environment.
- Standardized IT systems for all business functions (i.e., one enterprise resource planning system, one logistics system, one business intelligence system) with standardized reporting.
- Business-unit driven solutions made in conjunction with IT, with consideration given to the enterprise architecture.
- Comprehensive architecture for migrating data from the operational environment to the analytical environment.
- Clear data integration standards.
Thats a pretty significant set of benefits, and theyre only the tangible benefits - the ones that can be quantified. What cant be quantified is the peace of mind youll have knowing that there is a clear accountability structure for standardized organizational data. The piece of mind comes from the knowledge that theres less of a chance that something will slip through the cracks and cause you to make a bad decision that can cost you revenue, a sale or worse - a customer.
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, anated entities shall not be responsible for any loss sustained by any person who relies on this publication.
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