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Data Governance (n:) Data Governance is a system for defining who, within an organization has the authority and control over data assets and how they are used. It is the people, the processes, the technology that is required to manage such data assets. Data Governance is just one part of the overall discipline of data management, which is the characterized by the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.
When it comes to the Data Management, it’s an integrative discipline for restructuring, describing, and governing information across organizations to improve efficiency, promote transparency, and enable business insights. The Data Governance Institute, however, provides organizations with a more holistic approach to collecting, managing, and securing data. Some more terms to be aware of our as follows:
- Data Architecture – The overall structure of data and data-related resources, as an integral part of enterprise architecture.
- Data Modeling Design – This looks like the analysis, design, building, testing
- Data Storage – Structured physical data assets storage deployment and management
- Data Security – Ensuring privacy, confidentiality, and access
- Data Interoperability – Acquisition, Extraction, Transformation, Movement, Delivery, Replication, Virtualization and Operations Support
- Documents/Content – From storing, protecting, and enabling the access of data found in unstructured sources, making this data available for integration.
- References/Master Data – This calls for managing shared data to reduce redundancy and ensure better quality through standardized use of data values.
- Data Warehousing – Managing analytical data processing and enabling access to decision support data for reporting and additional analyses.
- Meta-Data – Collecting, categorizing, maintaining, integrating, controlling, managing, delivering that meta-data.
- Data Quality – How one can define and monitor the quality of the data that comes through
There are many goals that come along with Data Governance and they are illustrated as the following benefits:
- Minimized Risks
- Established Rules Internally with data usage
- Regulated Compliance Implementations
- Improved External/Internal Communications
- Increased Value of Data
- Reduced Costs
- The continued existence of the company through risk management solutions
When it comes to helping companies remain responsive with their business, especially when it comes to growing the size of their reach, for those employees who can’t perform such cross-functional tasks. Data Governance assists with the next few capabilities:
- A more comprehensive support and more uniform and transparent data across the company
- Clearer rules for changing processes that help business and IT departments become increasingly agile and scalable
- Reduced costs in other areas surrounding data management
- Increased efficiency through the ability to reuse processes and data as needed
- An improved sense of confidence with the data quality
Data Governance Principles
According to the Data Governance Institute, these next statements constitute a Data Governance Principle:
- All participants must have integrity in their dealings with each other. They must be truthful and forthcoming.
- DG and Stewardship processes require transparency
- Data-related decisions, processes, controls that are subject to data governance must be audit-able.
- DG must define who is accountable for cross-functional data-related decisions.
- There is a check and balances system in place between businesses and their tech teams.
- DG will introduce a level of standardization of all entry-level data
- DG will support proactive change management activities for reference data values and the use of master data and meta-data.
So what’s really going on here, and why would this ever affect businesses? Those who delve in Artificial Intelligence, IoT, and Big Data will be impacted the most with this need to hone in their data management skills. DG provides employers with the assurance that their metrics are correct, especially when considering where the KPIs are at. It acknowledges insights into which metrics are the most important and even instills a greater means of confidence in their analytics.
Data governance brings forth peace of mind so that data is generally clean, standardized, and accurate. Aligning with team members is essential for bettering workplace relations to see who holds which responsibility on and around the clock.