Data Governance

As any other governance (IT governance, BI governance), Data Governance is about replacing a chaos with the order. In practice this means that some guidelines are set/specified about where, by whom, and what data-related decisions are made, how to keep them consistent, and most importantly how to align them with the business needs. It is apparent that such solution must be rather a program-based (ongoing activity), rather than project-based (temporary activity).

We at Adastra believe that:

• Data is enterprise asset ( and should be treated accordingly)
• Data management is driven by business requirements (and not by IT budget)
• Non-invasive approach is preferred
• Data Stewardship centricity is crucial
• Consistent approach to Data Governance based on above described beliefs makes the program working, helps creating your competitive advantage, allows cutting expenses and deepens the trust in data.

Adastra’s conception of Data Governance typically involves treatment of areas as shown in Figure below.

The major focus areas of Data Governance cover:

• Quality of the data
• Common understanding of the data (metadata).
• Depending on customer’s experience, requirements and understanding, the following focus areas may be perceived as part of the Data Governance: 
  • Data Architecture
  • Data Integration & Master Data

Value Proposition

You will appreciate a Data Governance program if you struggle with any of the following situations:

• The organization's data systems get so complicated that traditional management isn't able to address data-related cross-functional activities.
• The organization's Data Architects, SOA teams, or other horizontally-focused groups need the support of a cross-functional program that takes an enterprise (rather than siloed) view of data concerns and choices.
• Regulation, compliance, or contractual requirements call for formal Data Governance.
• Gaining additional benefits from existing DQ/MDM solution.
• Building Enterprise Information Management environment .
• Turning your company into Data Centric Organization

Key deliverables

The typical deliverables of top-down approach include the following:

• Initial Data Governance maturity level assessment (Basic, Foundational, Advanced, Distinctive)
• Data Governance vision formulation
• Policies / standards defining the environment
• Organization and processes design:
  • Roles definitions, including job description, qualification criteria, responsibilities and rights. 
  • Processes definitions.
  • Candidates’ proposals (if applicable).
  • Program / environment KPIs definition a measurement process design. 
  • Action plan for the environment implementation including steps definition and communication & education plan.
  • Benefits identification and ROI calculation.

Typical deliverables of bottom-up approach include the following:

• Enhancement of existing methodologies with Data Quality and Metadata tasks, inputs, outputs, etc.
• 0Introduction of new templates (if applicable). 
• Enhancement of other existing standard documents (e.g. information requirements specification, functional requirements specification, non-functional requirements specification).
• Delivery of particular Data Quality and/or Metadata outputs specific for the project, such as Data Quality Management solution, Metadata solution, business environment (organization, roles, etc.) .
• Communication and education plans.
• Measurements and / or KPIs.

Key benefits

We strongly believe that the Data, as a matter of fact is the corporate means of production. Our efforts are aimed to provide your data with the same attention as shoemaker would do to his stool. Therefore our Data Governance solution offers the following key benefits:

• Enabling the data providers to understand the needs of data consumers based on SIPOC Process.
• Allowing the data to be treated as any other corporate asset.
• Conforming the data management to business requirements primarily.
• Preserving already expended investment by non-invasive approach to Data Governance
• Focusing on Data Stewardship being the cornerstone of Data Governance