DATA GOVERNANCE
Data is critical to every aspect of how an organization functions. Data is continuously growing, for that reason it's so
important to have the right processes and practices in place to manage company data.
More than 80 percent of an organization's stored data is an "unstructured data." in the form of spreadsheets, word processing documents, presentations, media, virtual images, and many other file types that are not residing in a database. In addition to the data growth, these all act as add on for effectively managing company data.
What is Data Governance?
Data Governance is a series of activities and processes that help to ensure the formal management of data assets inside an enterprise. Data governance also involves other concepts such as Data Architecture, Data Integration, Data Quality, and others to help organizations get greater control of their data resources, including processes, technologies, and rules relating to effective data management. Furthermore, it manages security and protection, integrity, accessibility, integration, enforcement, reliability, tasks and responsibilities, and the proper implementation of data from multiple sources within the enterprise.
Data governance policies describe company rules on a variety of factors, including data access, storage, confidentiality, use and discard.. Most definitions pertain to the proper, consistent, and legal management practices related to how data is stored and secured within an enterprise.
While most organizations have specified policies, but lack of enforcement within an enterprise is troubling. Many factors prevent enforced data governance policies, including:
- Lack of automated management
- Unawareness regarding the significance of stored data, and who should have access to certain types of data
- The lack of time to manage data governance tasks
- And many other factors
Fortunately, pioneering tech companies have created strategies to overcome the above-mentioned data governance challenges.
Data Governance vs Data Management
Data governance is however, only one essential aspect of the overall data management discipline. Whereas data governance is about the roles, responsibilities, and processes for ensuring accountability for and ownership of data assets, DAMA defines data management as "an overarching term that describes the methods used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.
While data management has become a common term for the discipline, it is sometimes referred to as data resource management or enterprise information management. Gartner describes data management as "an integrative discipline for structuring, describing, and governing information assets across organizational and technical boundaries to improve efficiency, promote transparency, and enable business insight."
Data Governance Framework
Data governance can best be seen as a mechanism that supports the overall data management strategy of the company. The Data Governance System gives the company a comprehensive approach to gathering, managing, protecting and preserving data. In order to better explain what the structure for data governance should cover, DAMA envisages data management as a wheel, with data governance as a core.
Data governance may best be thought of as a function that supports an organization's overarching data management strategy.The Data Governance System gives the company a comprehensive approach to gathering, managing, protecting and preserving data.
10 data management knowledge areas radiate:
Data Architecture : Structures overall data and data-related resources as an integral part of the enterprise architecture.
Data Modeling and Design Analysis, design, building, testing, and maintenance
Data storage and operations Structured physical data assets storage deployment and management
Data security Ensuring privacy, confidentiality, and appropriate access
Data integration and interoperability Acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization, and operational support
Documents and content Processing, securing, encoding and allowing access to data contained in unstructured sources and making these data accessible for integration and interoperability with structured data
Reference and master data Managing shared data to reduce redundancy and ensure better data quality through standardized definition and use of data values
Data warehousing and business intelligence (BI) Handling analytical data collection and allowing access to decision support data for reporting and analysis;Managing analytical data processing and enabling access to decision support data for reporting and analysis.
Metadata: Collecting, categorizing, maintaining, integrating, controlling, managing, and delivering metadata
Data quality: Defining, monitoring, maintaining data integrity, and improving data quality
When establishing a strategy for data governance, each of the above facts of data collection, management, archiving, and use should be considered.
PiLog - Master Data Governance
Data governance is a steady process rather than a technology solution, but there are tools that can help support that program. The device that suits your enterprise will depend on your needs, data volume, and budget.
Like any governance model, Master Data Governance starts with policies, guidelines, business rules and a governance approach that covers all facts of the individuals, processes and technology involved. Although data management processes handle the actual production and ongoing preservation of master data, the methodology directs the best practices of the industry to be practiced, such as compliance with ISO 8000. data standards, whereas the business rules define the proper use of the data to drive an efficient and effective business operation. Specifically, business rules.
Introducing Data Governance as a Service
PiLog Group MDRM (MDM Tool) launch a Data Governance as a Service.
- Analyze reports on data storage, access, and growth for your organization so that you have the information you need to decide on corrective action.
- Develop procedures for the life cycle management of your data according to your organization data governance policies
- Establish policies that can lock down access to high-value targets on your network
- Monitoring group memberships such that authorized users have access to the right data
- Establish line-of-business data owners to assist IT in determining appropriate access permissions for users
- Assist organization to demonstrate compliance with policies and regulations in an audit
- Assist in access reviews for both access to applications and sensitive data located in the network file system.