Data Governance is a crucial agenda in organizations that want data-driven business decisions and operations. Data-driven enterprises offer excellent digital experiences to their audience groups, and increase CSAT, employee satisfaction, revenues, productivity, and profitability.

The crucial question is how to implement effective enterprise-wide Data Governance (DG) and what benefits to expect from it in the medium to long-term. DG, as most practitioners would readily agree, is a complex long-term program encompassing people, new roles and responsibilities, virtual organization, organizational policies, data standards (global, regional and local), operating processes, procedures and last but not the least, the implementation and adoption of multiple platforms, systems and tools.

To rollout a new DG program or optimize an existing DG program across all its dimensions, following questions need to be deliberated.

1. What can an enterprise expect to gain from the rollout of the DG organization and the DG program? What are the steps for the rollout? By when the expected benefits may accrue? 
2. How to incentivize the members [business units (BU) and individuals] of the DG organization once the organization is set-up with the new roles? 

1. What can an organization expect to gain from the rollout of a DG program? 

DG initiatives can combine with analytics modernization initiatives to offer integrated high-quality data discovery. This will lend capabilities to data scientists, data analysts, business analysts to ask questions, formulate hypothesis and get answers that can be trusted as a single version of the truth.

This evolution will enable efficiencies like:

• Better user experience and productivity: DG and analytics process changes, application level modifications and enhancements will help users to be more productive in analysis and decision making
• Optimized IT costs: Integrated, secure and privacy-compliant data development, architecture and design updates will lower risks and costs
• Effective data issue management: DG operating framework will help run root-cause analysis on data and reporting issues posted by the end-users, track data lineage back to source systems, and identify issues for long-term resolution

1.1 What are the steps to rollout a DG organization?

• Assign individuals to all roles listed for the DG Management Committee (DGC) – One person should not hold more than one role at this level
• Identify DG functions across all the BUs that are apt for DG strategy implementation. The DG functions within the BUs should be proposed based on the organization structure within the BU. The DG functions should be agreed upon by the BU Business Heads. The Data Owners for the DG functions should be nominated and appointed by the BU Business Heads. The Data Owners for the DG function will in turn nominate and appoint the Data Stewards for their respective DG functions.
• Assign individuals to Data Owner (DO) roles in all DG functions – One person from a BU may be DO for maximum three DG functions
• Assign individuals to Data Steward (DS) roles in all DG functions – One person can be DS for one DG function only
• Assign individuals to Data Custodians (DC) roles for all DG Functions – One person from the DG function may be DC for several data source systems
• Assign individuals to all remaining roles in the proposed DG organization - Set up the direct/dotted line reporting relationships between these assigned individuals as per the DG organization virtual team structure having direct and dotted line reporting relationships

The above set of activities need to be plotted on a Gantt chart showing parallel activities, dependencies and estimated timelines leading to go-live of the enterprise DG program.

1.2 When can the expected benefits from the DG program accrue?

Stabilization Phase: lasts 9-12 months after the go-live

• All assigned individuals should start devoting 4hrs/week to DG work as per their roles.

3 months after the go-live following benefits can start accruing (based upon the 4hr/week time allocation mentioned)

• Support for driving compliance to privacy and security considerations around personally identifiable information, hierarchies, knowledge content, metadata, institutional data
• DG program KPIs for ongoing tracking and assessment of (improvement in) DG maturity level
• Understanding of what is critical data across the DG functions - 5% gain in data quality (DQ) across the enterprise because of establishment of DQ metrics and comparative scorecards between DG functions and processes for driving competitive adoption - Data lineage analysis and root-cause analysis on report metrics quality - Resolution of case-by-case disputes, escalations on existing DG, DQ processes 
• Better change management around data and systems and improved user experience through assessment and control of change management from BUs (by mapping change requests to existing processes) 
• Higher use of data through reporting and analytics due to improvement in business metadata
  
• Increase in rate of resolution of quality issues by 20% every 3 months due to setting up of ‘Voice of User’ surveys on data quality experience of end users
  
• Enhancement in DG maturity level by 0.5 (on a scale of 1 to 5, with 5 being the most advanced level) every 6 months
  

2. How to incentivize the DG organization members?

Upon go-live, considerations of additional remuneration should come into effect. These considerations can work at two levels - BUs and individuals.

2.1 At the level of BUs

• At the end of Q1 after the go-live: BUs having high number of identified DG functions and high number of assigned individuals to roles of DO, DS, DC may be eligible for additional budget to hire additional head count
• At end of Q2 after go-live, BUs which start showing significant improvement in data quality metrics, levels of adoption of DG across parameters should get awarded
• At the end of Q3, formal awards should be given for DG outcomes to selected DO/DS/DC individuals across BUs

2.2 At the level of Individuals

• At end of Q1 after the go-live, top 20% performers should get Award A, next 20% performers should get Award B. Parameters of the Award should be based upon distinct and clearly articulated parameters of evaluation
• For Q2, Q3: Top 20% performers from the DG execution workgroups and top 2 contributors from the DG Committee should get awarded based upon same parameters
• At the end of 1 year: 3 Champion awards should be marked for execution workgroups

DG roles should be calibrated based on the extent of their engagements in slabs of 50-100%, 25-50%, and less than 25% of their available time for DG work. The slab of 50-100% will need new head count, who will need to be assigned with additional work by the respective BU, slab of 25-50% will also need new head count with more diversified work, and individuals in the lowest slab should be offered a package that includes a premium on top of current CTC.

To conclude

For any analytics program implementation to succeed and yield business value, optimum data quality is a pre-requisite. Enabling data quality at enterprise scale requires multi-faceted approaches, enablement and technologies that make up the enterprise-wide Data Governance program. Therefore, well conceptualized and executed DG organization and program are essential to enable high-performance analytics to deliver insights that will be trusted as single version of the truth and will support business decisions and actions across the enterprise.

Rahul K. Srivastava

Lead Consultant- Information Management Data, Analytics & AI Wipro Limited.

Rahul has over 19 years of work experience including Enterprise Data Strategy & Governance, Master Data Management, Business Intelligence & Analytics, and Cloud platforms. He has built and run successful business transformation programs in these domains for diverse sectors. He holds a B.Tech degree from Indian Institute of Technology Varanasi (IIT BHU), India and MBA in Marketing (Research).