With many companies organized along lines of business (LOBs), system and application complexity have increased steadily, decision making has become decentralized and data has become siloed. Critical business information is often unsynchronized across silos and may conflict across systems. Too often, there is no single data steward or even enterprise-level data governance.
In this environment, availability and rapid data access are ongoing issues, and poor data quality is eroding business user trust. Without consistent, accurate and reliable enterprise data, an organization can make inaccurate, misleading and incomplete decisions that may have a lasting effect on performance and results.
Who is Responsible for Data Governance?
Regulatory pressures, executive concerns and customer demands are driving organizations to improve data quality and access, and to treat data as an asset. Many organizations now realize that data governance
and control are ever more critical to corporate viability and the ability to compete. The challenge posed by data governance, however, is particularly difficult to address because it is not clear whether the business or IT is responsible for data governance and what roles each should play. The ongoing debate typically focuses on the following questions:
- Where in the enterprise should data governance sit?
- Why are data owners necessary if data is owned by the enterprise?
- Who should spearhead the data governance organization (DGO)?
- Who is accountable?
Definitive answers to these and other questions are elusive. Still, they must be answered before embarking on a data governance initiative.
When IT is first to identify the need for data governance, there is often a power struggle between it and the business. IT must “sell” data governance by articulating its business value. This can be difficult because the business believes that return on investment (ROI) is hard to quantify. The business normally takes a hands-off approach; and, with employees who are over-extended or working on other critical initiatives, it lacks the resources to engage. Additionally, the business hesitates to get involved with anything that requires change that will impact the organization or will be perceived to “slow them down.” Even though the overall value of data governance to the enterprise is large, business stakeholders are focused on goals within their own LOBs and direct responsibility – making it difficult to achieve the buy-in, commitment and sponsorship required to succeed.
If the business understood the importance of data governance, would it take the action necessary to invest in it? What is the role of the business in data governance? How should the business engage with IT? To address these issues, let us begin by examining the goals of data governance and the business.
The Goals of Data Governance and the Business
At the outset, it is important to understand and align the goals of business and data governance:
- The goal of the business is to create revenue, reduce costs and increase operational efficiencies.
- The goal of the data governance organization is to provide direction on the capture, collection, transfer, quality and management of data.
The business relies on the data governance organization to meet its goals. Effective data governance makes this possible by controlling and standardizing data creation and management. Its aim is to provide data that is of high quality, is highly available, consistent and secure. Data governance eliminates redundancies and provides a forum in which the business and IT can reach consensus on data that is shared across the enterprise.
Aligning business and data governance goals is achieved by making business goals actionable, with the business driving a data governance initiative across the organization. Engaging IT with business stakeholders is an important first step.
The Data Governance Mandate
When the business mandate is to implement an enterprise-wide data governance program, then the business is authorized by the data governance steering committee to form a data governance working Group (DGWG) – which is the DGO’s governing body. Decisions made within DGWG override all other data decisions made by the business units.
To comply with its mandate, the business must provide resources that will work with IT to highlight issues, provide solutions and then implement them. At a minimum, the business should devote the following resources to the effort:
Business Sponsor – spearheads the data governance initiative.
Business Leadership – provides, at the steering committee level, direction, vision and oversight from each LOB.
Data Governance Lead – serves as gatekeeper of data governance best practices, acts independently across business units and IT, and drives the development and ownership of the data governance roadmap. The Data Governance Lead has an IT peer.
Data Governance Coordinator – helps administer the data governance organization.
Business Steward Leads (BSLs) – work with business data stewards to understand information quality needs and ensure best practices are followed. They provide the expertise required to define use of key data elements and improve business processes.
Data Stewards – act as the conduit between IT and the business and are accountable for data and data management process definition and for data quality levels for specific subject areas.
Data Governance Working Group (DGWG) – comprises business representatives across the enterprise. The DGWG creates, maintains, implements, and monitors policies, processes, and standards to ensure data reliability and sustainability.
Business leadership is also responsible for guiding data governance activities based on a value-driven project plan and for making sure that data governance and business strategies are aligned. Specific business activities include the following:
- Defining Data Governance Strategy – Defining the data governance organization’s vision and mission, charter, goals and guiding principles; and aligning business and IT goals and objectives.
- Ownership and Accountability – Defining a set of data attributes to be managed and governed; defining ownership and accountability via a responsible, accountable, consulted, informed (RACI) matrix of data attributes to be governed at data element level.
- Facilitating LOB Training and Communication – Providing guidance, training and mentoring, resolving issues and communicating data governance information to the business; promoting data governance concepts across the organization; serving as a communication conduit between the DGWG and LOB business stakeholders; and demonstrating data governance value.
- Creating and Enforcing Policies, Processes and Standards – Standardizing data definitions, policies and processes, enabling data to be effectively interpreted, shared and used; and creating and monitoring data policies, processes and standards to meet all relevant requirements.
- Decision Making and Issue Escalation – Providing clear escalation processes for enterprise data issues; supporting decision making and enhancing customer and partner services; eliminating business and technical obstacles to achieving business goals; and working with IT to resolve data issues and alerting the data governance organization to exceptions.
- Data Protection, Integrity and Sharing – Ensuring information integrity to support the data governance mandate and business functions; protecting information, while also facilitating access; and assisting in information sharing and dissemination.
- Governance, Stewardship and Efficiencies – Providing a coherent data governance framework, including key roles and players; creating an extensible data governance model to ensure that management of enterprise data is independent of a specific application context; reducing costs and increasing efficiencies through coordination of efforts; and creating transparency in data governance and management.
- Reporting Metrics and Successes – Using metrics to report the effectiveness and adoption of DGO policies, processes and standards; reporting data quality metrics to the business units; and reporting key performance indicators (KPIs) critical to running the business.
- Assessing and Monitoring Enterprise Projects – Assessing LOB projects for data impacts in order to identify data governance candidates; monitoring LOB projects to understand data governance needs and communicate them appropriately to the DGO; consulting, providing subject-matter expertise and representing data governance to enterprise projects; and monitoring LOB projects to identify duplication of effort and redundancies.
A key outcome of data governance is the ability to manage data as an enterprise asset, thus enabling an organization to more easily and accurately identify opportunities to increase revenue, reduce costs and improve efficiency and productivity. An effective data governance initiative provides broader customer visibility, and it makes it possible for the business to more accurately gauge risk, improve regulatory compliance and reduce the time required for data reconciliation and reporting throughout the fiscal year. In short, an intelligent data governance program can vastly improve management decision making through its ability to manage and deliver accurate data.
In this context, IT can not only reduce infrastructure costs but also provide value through consolidated licensing and simpler architectures. Of course, IT has an important role in improving ROI from customer relationship management
and business intelligence
deployments by delivering better quality data.
For data governance to be successful, business and IT must be willing to collaborate – with business taking the lead in defining its goals and IT providing strong support in helping to make them actionable. Business and technology stakeholders must clearly understand their roles and what is required of them. And because so much of the execution of a data governance initiative must be handled by the business, we recommend, as a best practice, that data governance be owned by the business from the beginning.
SOURCE: The Role of Business in Data Governance
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