Manager's Guide

Data governance guide for managers

The foundation of any data management initiative, data governance program, helps in ensuring availability of quality data, its usefulness, and security. Many organizations in India are implementing data governance projects to better manage their data today.


Download the PDF version of Manager’s Guide to Data Governance for internal training, presentations, and future reference.


This Manager’s Guide to data governance covers the following topics:

 

What is data governance? 

Data governance refers to that set of activities and practices undertaken to ensure the integrity and reliability of an organization’s data. Data governance is an emerging field. Definitions and working methods continue to develop and evolve.

Organizations always have a lot of data on their hands to manage. There is data covering the whole spectrum of the organization’s activities, both internal and external, including supplier, client, and product information and transaction histories, invoices and receipts, reports, memos, minutes, agreements, and so on.

The larger the organization, the larger the pool of data that is generated over time. Out of these, master data relating to suppliers and customers is understood to be the most vital to the continued success of a business.

All organizational data needs to be actively managed to ensure its integrity. Unmanaged data creates inefficiencies and blockages in the organization’s activities. Using incorrect client or product information may not only tarnish a company’s image, but it may also lead to legal action.

A well-managed, accurate, and reliable data is, therefore, vital to running of everyday operations, and for strategic planning for the future. It is in every organization’s interest to make the best use of the data available to it. This is where data governance comes in.

 

Advantages of implementing data governance projects 

1) Data is now seen as a corporate asset, along the lines of traditional assets such as capital, properties, the workforce, and the customer-base. Gathering data, correcting and standardizing it, and making it good for use, demands for a huge expenditure of organizational energy and resources. Data governance aims at obtaining the maximum dividends from this investment.

2)  Data governance enables an organization to make more efficient use of the data available to it. Data governance ensures that critical data is available at the right time to the right person, in a standardized and reliable form.

3) Data governance results in availability of high quality organizational data. This, in turn, translates into better management of business operations and supply chains, and improved efficiencies in marketing and customer relationship management.

4) The internal productivity and efficiency of an organization can be improved by adopting and implementing data governance project, leading to greater profitability and business success.

5) Lastly, the implementation of data governance measures ensures that the organization stays compliant with changing laws and regulations.

 

Data governance challenges 

1) Data governance is an ongoing process, not a single-step deployment. The results will be slow and cumulative, as different parts of the organization align themselves to meet common data governance goals.

2) The biggest challenge to deploying data governance projects is the lack of understanding of the utility of data governance. Top management will need to be persuaded to make the investments needed to implement data governance projects.

3) Choosing data custodians and stewards and holding them accountable for the data under their purview can be tricky initially. These are new methods, and internal resistance to change may have to be dealt with.

4)  There will be a reluctance to share sensitive data beyond a certain circle. Internal confidentiality policies may need review and revamping.

5)  It is all too easy for a data governance initiative to end up as a bureaucratic exercise. As with any organizing activity, data governance projects stand the risk of creating new red tape for users of the data.

6) Data redundancy and inaccuracy are other major sources of worry. It may be necessary to involve customers and suppliers in the drive to improve organizational data quality.

 

Top 5 data governance best practices 

1) Formulate and communicate the organization’s data governance policy clearly to all the responsible personnel in the organization.

2) Delegate data management to individual ‘data custodians’ and ‘data stewards’, with a clear definition of roles and responsibilities for each data custodian and steward.

3) Define, approve, and publish standards and metrics for organizational data.

4) Create provisions for escalating and resolving data integrity issues as and when they arise, including provisions for safe storage, backup, theft, and compromise.

5) Create a system of checks and balances; even an internal data governance council perhaps. The internal data governance council can oversee the organization’s data governance activities and ensure compliance with local laws and regulations.

 

Data governance tool vendors 

A number of software tools have been developed by vendors to help organizations implement data governance projects. Some of the important players in this space are IBM, Microsoft, Oracle, and SAP.

IBM’s data governance targeted offerings include IBM Information Integration Connectivity Software for z/OS, IBM InfoSphere Information Analyzer, and Cognos Datawarehousing and Business Intelligence, among others.

Microsoft’s Information Security is a monitoring tool for protecting data from theft or compromise.

Oracle offers a number of business intelligence and data warehousing products that are useful for executing data governance projects. This includes Oracle Business Intelligence Standard Edition One, Oracle Business Intelligence Tools and Technology, etc.

SAP’s data governance related products include SAP Data Migration, SAP Master Data Governance, SAP NetWeaver Master Data Management, and SAP BusinessObjects Data Quality Management.

 

Leading data governance consultants 

No data governance project is usually carried out without the involvement of consultants. Given below are leading consultants in the Indian data governance projects market.

SRN Consultant Services offered
1 Capgemini Capgemini’s data governance consulting practice focuses on people, processes, technologies, methodologies, and frameworks.
2 Deloitte Deloitte offers data governance consultancy to help organizations meet their compliance requirements.
3 IBM IBM works with Data Governance Council to adopt and improve upon a set of data governance best practices unified under Data Governance Maturity Model for its customers.
4 Infosys Infosys’ framework for data governance incorporates quality planning, quality control, and quality assurance. Focuses primarily on the financial services vertical.
5 Tata Consultancy Services TCS offers a range of IT consulting services including data governance under its ‘IT Strategy and Governance’ and ’Master Data Management‘ practices.

 

Further reading 

Definition from Whatis.com: What is Data Governance?

Tip:Evaluation checklist for data governance tools

Tip: Streamline the data governance process

Tip: Data governance roles and responsibilities call for diverse skill sets

Tip: Data governance best practices: Setting up a data governance program

Tip: Building a data governance framework: governance processes and issues

Tip: Governance of data: Strategy design and best practices

This was first published in March 2011