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Data is everywhere, with IoT and the growing interconnectivity of the different data feeds, data sources, channels, and data consumers, the need for Data Governance controls guided by a Data Strategy are highly arise to support business decisions and fulfill compliance requirements such as European General Data Protection Regulation (GDPR) mandated since May 2018 and BASEL II/III for banking and insurance industry.
There are probably many definitions of Data Governance out there, but any reasonable definition would have to be consistent with the general meaning of governance. A dictionary definition of govern is “to exercise authority over.” And authority is “the power or right to give commands, enforce obedience, take action or make final decisions.” If we are to truly govern our data, we must have systems and processes that ensure the data is consistent with the decisions of the people in authority. So, “Data Governance” can be defined as the “The formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.”
Because Data Governance is a strategic initiative involving multiple functions across the enterprise, a Data Governance program should include a governing body (steering committee or council), an agreed upon common set of procedures, and a plan to communicate and execute those procedures.
Initially there is a need to analyze the maturity of the current Data Governance (as is state). This will then lead to developing the organization, policies, procedures and standards, together with the associated Data Governance processes and the necessary supporting technology.
Finally, the human and change management aspects need to be considered, thus there also needs to be training and mentoring provided in development and the deployment.
Data Management Strategy is the centric high-level descriptor for all Data Management functions listed below:
Governance defines the control and decision rights to operate the data management strategy; orchestrates people, processes and technology to ensure effectiveness and efficiency of data leverage securely and turns strategy into actions with key objectives of:
- Govern accountability for the definition, structure, storage, owner, movement, security, metadata, quality, and ownership
- Contains vision, scope, goals, objectives, methodology, and principals
- Track and enforce conformance of the Data Strategy.
- Manage the data related issues and resolution.
Data Governance will show the strategy in terms of:
- Where we are (current situation)
- What can be done (desired situation)
- How we are going to get there (Gap Analysis)
- Implications / Effect on Business and IT department
- Implementation Dependencies
- Recommended approach for the implementation
- How much it will cost
- How long it will take
- What resources are needed, staff requirements, and training needed
- Development schedule and key milestones
Data Governance implementation will be resulting in a set of deliverables:
- Current Data Governance assessment report including pain points
- Data Governance charter, organization structure and Operating Model.
- Define lifecycle and process for Data Governance assets management (policy, process, workflow, etc.)
- Define the different set of processes which need to be utilized across the organization to manage the different pillars of the Data Governance model
- Define Data Governance decision and issue resolution processes
- Define SLAs and KPIs related to Data Governance assets.
- Define Data Governance and management policies
- Define Data Governance roadmap (maturity increase oriented)
- Define Data Architecture viewpoints
- Define Data Architecture artifacts
- Define templates and forms for Data Governance assets
- Define Data Dictionary, Business Glossary, Data Catalog, and Data Lineage reports
- Data Governance regulation bodies
- Identify data owners, stewards, custodians and information management professional.
- Define groups, roles and responsibilities
- Work with Data Governance council and board to ensure Data Governance polices and processes are implemented.
- Define required dashboards to monitor the progress of development
- Establish a control mechanism and compliance process to ensure compliance
- Establish relationships among Data Governance asset and various assets.
- Demonstrate and approve the new Data Governance assets from Data Governance Council and Board
- Data Governance technology tools application.
Data Governance Key Roles & Responsibilities
Chief Data Officer (CDO); takes responsibility for all enterprise data and plays an integrative role covering all data domains. The CDO particularly deals with conflicts of interest that may arise among the different data owners and data consumers.
Data Owners; are generally senior managers without the detailed knowledge of data sets and uses, hence they rely on business experts who handle the data. Data Owners could be accountable but delegate responsibility for detail to Data Stewards. Their role is to understand what information is held, what is added and what is removed, how information is moved, and who has access and why.
Data Stewards; are normally a Subject Matter Expert (SME) for the dataset they have responsibility for. Generally, Data Stewards are from the business and understand the true value of the data to the organization.
Data Custodians; are from within the IT function and can make corrections to the data at source. They have responsibility for the IT infrastructure providing and protecting data in conformance with the policies and practices prescribed by Data Governance.
Data Governance Organization Structure Models
Following preferred different models of the Data Governance organization structure:
- Collaborated teams from IT and lines of business
- A separate Data Governance unit that collaborates with the responsible data representatives from the lines of business
- Establish a competency center from the BI team
Data Governance Considerations and Success Factors
- Technology is not the limiting factor for implementing Data Governance
- Practical practice of Data Governance focus on data quality monitoring, and control which is a prerequisite for utilizing data and analytics to foster innovation, as well as data integration and SOA
- Although data warehouse has been seen as the only feasible vehicle to establish a harmonized view of enterprise data and is therefore the first natural target for organized governance of data and how data is used, but Data Governance practice targeting the data warehousing have a limited usefulness, and the Data Governance can be fully effective when applied to source systems
- Data strategy and governance must be closely aligned with the enterprise and digitalization strategy, and with an overarching view of business processes
- Management support and the identification of priorities based on corporate strategy are the key Data Governance success factors
- Training and awareness for business users is crucial for Data Governance initiatives success