Data Governance - DG

Data Governance Framework, Data Stewardship, Data Quality - DQ, Data Scrubbing, Metadata Repositories, Metadata Management - MDM, Data-Driven Decision Management - DDDM, Data Stewardship

At HubBucket Inc. ("HubBucket") we conduct Science and Technology, Research and Development - R&D, and our research and development produces large quantities of data. We have implamented a robust Data Governance Famework in order to ensure that our research and development adheres to the required standards, regulations and policies.

What is Data Governance - DG?

Data Governance - DG is the overall management of the availability, usability, integrity, and security of data used in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures and a plan to execute those procedures.

What is a Data Governance Framework?

A Data Governance Framework refers to the process of building a model for managing enterprise data. The framework or system sets the guidelines and rules of engagement for business and management activities, especially those that deal with or result in the creation and manipulation of data.

Businesses benefit from data governance because it ensures data is consistent and trustworthy. This is critical as more organizations rely on data to make business decisions, optimize operations, create new products and services, and improve profitability.

Data Governance Implementation

The initial step in implementing a data governance framework involves defining the owners or custodians of the data assets in the enterprise. This role is called data stewardship.

Processes must then be defined to effectively cover how the data will be stored, archived, backed up and protected from mishaps, theft or attacks. A set of standards and procedures must be developed that defines how the data is to be used by authorized personnel. Moreover, a set of controls and audit procedures must be put into a place that ensures ongoing compliance with internal data policies and external government regulations, and that guarantees data is used in a consistent manner across multiple enterprise applications.

Once an overarching strategy is defined and data owners and custodians are identified, data governance teams are often formed to implement policies and procedures for handling data. These teams can comprise business managers, data managers, and staff, as well as end users familiar with relevant data domains within the organization.

Associations dedicated to promoting best practices in such data governance processes include the Data Governance Institute, the Data Management Association - DAMA and the Data Governance - DG Professionals Organization.

Pillars of Data Governance - DG

Often, the early steps in data governance efforts can be the most difficult, as it is characteristic that different parts of an organization have diverging views of key enterprise data entities -- such as customer or product; these differences must effectively be resolved as part of the data governance process. To the extent that data governance may impose strictures on how data is handled, it can become controversial in organizations.

Data Stewardship

An essential trait of the data steward is to be accountable for various portions of the data. The major objective of such data governance is to assure data quality in terms of accuracy, accessibility, consistency, completeness, and updating.

Teams of Data Stewards typically are formed to guide actual data governance implementations. These teams may include database administrators, business analysts, and business personnel familiar with specific aspects of data within the organization. Data stewards work with individuals positioned in the overall data lifecycle to help ensure data use conforms to a company's data governance policies.

Data Quality - DQ

Data Quality - DQ is the driving force behind most data governance activities. Accuracy, completeness, and consistency across data sources are the crucial hallmarks of successful initiatives.

Data Scrubbing

Data Scrubbing, also known as Data Cleansing, is a common element in the data quality initiative, as it identifies, correlates and removes duplicated instances of the same data points. Data scrubbing accounts for the various ways in which, for example, the same customer or product may be described. Data editors, data mining tools, data differencing utilities, data linking tools, as well as version control, workflow and project management systems are included among software types that help organizations attain better data quality.

Master Data Management - MDM

Data Governance touches on nearly every aspect of data management, but one area of data management very closely associated with data governance processes is Master Data Management - MDM. This is a discipline that establishes a master reference to ensure consistent use of data across large organizations.

Metadata Repositories and Metadata Management - MDM

Metadata Repositories, which hold data about data, are often used in establishing cross-group reference data in MDM programs. Product and customer data is a major emphasis on MDM systems. As with data governance generally, master data management projects can also encounter controversy within organizations, as different product groups or lines of business in the company promote different views on how to best present data.

The purview of master data management expanded as corporate computing came to include much more externally generated data, often collected via the web or the cloud.

Much of this data is unstructured and different in nature from the structured relational data that was traditionally the focus of MDM. That is one of the reasons that some MDM tools have begun to utilize graph data stores that support descriptions of more complex data interrelationships. Continuing advances in big data and a general flattening of corporate organization structure have led to an increasing emphasis on flexible approaches to governance that support incremental implementations over big bang, waterfall-style projects.

Data Governance - DG Use Cases

Data Governance - DG is a particularly important component of mergers and acquisitions, business process management, legacy modernization, financial and regulatory compliance, credit risk management, analytics, business intelligence applications, data warehouses, and data lakes.

As data uses to expand and new technologies emerge, data governance will gain wider application. Numerous high-profile data breaches have made data security a more central part of data governance efforts. Calls for data privacy has also led to the inclusion of data protection and data privacy audits as part of data governance programs. The European Union's - EU's directive concerning the General Data Protection Regulation - GDPR is an example of a use case for data governance.

Data-Driven Decision Management - DDDM

Data-Driven Decision Management - DDDM is an approach to business governance that values decisions that can be backed up with verifiable data. The success of the data-driven approach is reliant upon the quality of the data gathered and the effectiveness of its analysis and interpretation.

In the early days of computing, it usually took a specialist with a strong background in technology to mine data for information because it was necessary for that person to understand how databases and data warehouses worked.

If a manager on the business side of an organization wanted to view data at a granular level, she had to reach out to the Information Technology - IT department and request a report. Someone from the IT department would then create the report and schedule it to run on a periodic basis. Because the process was complex, ad hoc reports, also known as one-off reports, were discouraged.

Today, business intelligence tools often require very little, if any, support from the IT department. Business managers can customize dashboards to display the data they want to see and run custom reports on the fly. The changes in how data can be mined and visualized allow business executives who have no technology backgrounds to be able to work with analytics tools and make data-driven decisions.

Data-driven decision management is usually undertaken as a way to gain a competitive advantage.

A study from the MIT Center for Digital Business found that organizations driven most by data-based decision making had 4% higher productivity rates and 6% higher profits. However, integrating massive amounts of information from different areas of the business and combining it to derive actionable data in real time can be easier said than done. Errors can creep into data analytics processes at any stage of the endeavor, and serious issues can result when they do.

Data Stewardship: The Role of Data Stewards

Effective Data Governance - DG serves an important function within the enterprise, setting the parameters for data management and usage, creating processes for resolving data issues and enabling business users to make decisions based on high-quality data and well-managed information assets. But implementing a data governance framework isn't easy. Complicating factors often come into play, such as data ownership questions, data inconsistencies across different departments and the expanding collection and use of big data in companies.

Data stewardship adds another dimension and more challenges to Data Governance - DG efforts. Whether the organization hires full-time data stewards or delegates stewardship responsibilities to existing employees, business units sometimes are reluctant to accept the new arrangement for maintaining data definitions and enforcing policies on data use. In an ideal environment, all users adopt a stewardship-minded approach and take responsibility for handling data in a way that both meets their immediate business needs and serves the company's overall requirements for data quality and consistency. But data stewardship processes need to be attuned to an organization's corporate culture in order to help foster internal adoption and compliance.

Developing a successful data governance strategy requires careful planning, the right people and appropriate tools and technologies. This essential guide offers best-practices advice for managing data governance projects, an exploration of data stewardship and details about common problems that organizations have experienced while instituting data governance programs and how they solved them.

Data Stewards fulfill important tactical functions by supporting enterprise data governance initiatives in various ways. Learn about the role of data stewards and the function of data stewardship in the following stories, which examine the challenges and benefits of adopting data stewardship programs.

Data Stewardship is the management and oversight of an organization's data assets to help provide business users with high-quality data that is easily accessible in a consistent manner.

While Data Governance - DG generally focuses on high-level policies and procedures, data stewardship focuses on tactical coordination and implementation. A data steward is responsible for carrying out data usage and security policies as determined through enterprise data governance initiatives, acting as a liaison between the IT department and the business side of an organization.

Some organizations have created formal data steward positions, in many cases filling them with workers drawn from business units, while others assign stewardship responsibilities to employees who have other duties as well. A data steward might function as both a "data coordinator" who tracks the movement of data inside an organization and a "data corrector" who understands and enforces internal rules on how data can be used.

Regardless of how the position is structured, an effective data steward maintains agreed-upon data definitions and formats identifies data quality issues and ensures that business users adhere to specified standards.

A corporation may use a Data Stewardship program as part of its overall data lifecycle management effort and/or to help with data quality improvement projects. A Data Steward will often collaborate with data architects, business intelligence developers, Extract, Transform and Load - ETL designers, business data owners, and others to uphold data consistency and data quality metrics. Data quality tools, including data profiling software, are key technology components of many data stewardship programs.