🔢Neera Bhansali - Collected Quotes

"Are data quality and data governance the same thing? They share the same goal, essentially striving for the same outcome of optimizing data and information results for business purposes. Data governance plays a very important role in achieving high data quality. It deals primarily with orchestrating the efforts of people, processes, objectives, technologies, and lines of business in order to optimize outcomes around enterprise data assets. This includes, among other things, the broader cross-functional oversight of standards, architecture, business processes, business integration, and risk and compliance. Data governance is an organizational structure that oversees the compliance and standards of enterprise data." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)

"Data governance is about putting people in charge of fixing and preventing data issues and using technology to help aid the process. Any time data is synchronized, merged, and exchanged, there have to be ground rules guiding this. Data governance serves as the method to organize the people, processes, and technologies for data-driven programs like data quality; they are a necessary part of any data quality effort." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)

"Data governance is the process of creating and enforcing standards and policies concerning data. [...] The governance process isn't a transient, short-term project. The governance process is a continuing enterprise-focused program." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)

"Having data quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. Data quality is an elusive subject that can defy measurement and yet be critical enough to derail a single IT project, strategic initiative, or even an entire company." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)

"Understanding an organization's current processes and issues is not enough to build an effective data governance program. To gather business, functional, and technical requirements, understanding the future vision of the business or organization is important. This is followed with the development of a visual prototype or logical model, independent of products or technology, to demonstrate the data governance process. This business-driven model results in a definition of enterprise-wide data governance based on key standards and processes. These processes are independent of the applications and of the tools and technologies required to implement them. The business and functional requirements, the discovery of business processes, along with the prototype or model, provide an impetus to address the "hard" issues in the data governance process." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)