Explore the Possibilities 2013 HR Service Delivery Forum Best Practices in Data Management: Creating a Sustainable and Robust Repository for Reporting and Insights 2013 Towers Watson. All rights reserved.
Where is the need for HR data management coming from? How do we increase value? Deliver consistent and reliable data to business users in a timely manner Better leverage information for reporting, which creates confidence in HR data Increase cross-functional capability through related data integrity Improve company value and image through controls and compliance Ensure compliance and integrity across systems with all users of our data Can we reduce costs? Improve productivity through process design and technology when working with our data Allow for faster decision-making Reduce risk through improved data integrity Reduce data duplication and associated effort Reduce errors from wrong or delayed information Improve efficiency associated with data management Lower audit costs Better align efforts across internal functions, e.g., disaster recovery, security, data warehouse 1
The data maturity model Mature Data Maturity Fractured data standardization or data integrity best practices Individual applications are maintained separately Business need drives data without considering other factors Organized Comprehensive view into data elements with flow across functions and systems Organization is able to react to data integrity issues Rules for maintaining data are limited to particular systems or functions Governing Policies and procedures in place to maintain data integrity Roles established to measure and enforce data governance Organization takes a proactive approach to data changes Data redundancy and multiple administration points eliminated Dedicated MDM (Master Data Management) program or COE Data management automation and related tools in place Organization-wide practice of data stewardship New data elements are integrated seamlessly Time 2
Data management is driven by an effective data governance program A typical data governance program has three key components: data owners, data stewards, and technical support These are coordinated through a Data Governance Steering Committee, where all of the key stakeholders for the organization are represented The Data Governance Steering Committee acts as a crossfunctional leadership team to provide direction and oversight in the Data Governance model. IT Support Group IT Support Group owns tools and system-related processes (e.g., Data Governance enabling tools) and ensures that the IT organization can support the business and the Data Organization around data topics (data modeler and data architects, etc.) Data Governance Steering Committee Data Ownership Group Data Management Group The Data Ownership Group has a joint ownership of processes and data. They communicate a clear business vision of data and identify the data needed to meet business objectives. The Data Management Group members are the primary caretakers of the data asset. These are the Data Stewards. 3
This is not an easy program to put into place Metrics Executive Support Policies & Standards Processes Corporate View of Data Data Governance should be viewed as an ongoing program, not a project, and be regularly reviewed, updated and enhanced Data Governance must have executive sponsorship from the highest levels of the organization. Executive sponsors must be actively involved, take significant ownership of the effort and champion the initiative Data Governance programs must have real authority. This includes the ability to resolve business issues, review project data issues and settle disputes Data Governance principles cannot be viewed as optional Data Stewards should be Subject Matter Experts (SMEs) in their respective process, function or domain There should be a clearly defined set of data quality and Data Governance metrics and success measurements associated with the program There must be a clear and timely communication method for Data Governance initiatives, at all levels The organization must embrace acceptance and ownership of Data Governance 4
Within HR, data governance becomes everybody s job HR Leadership CPO VP/AVP of HR Compensation Benefits Performance Management Payroll COE HCM Strategy/ Governance Tech Leadership CIO CTO HR Relationship Manager Operations HR Business Partner Service Center Manager Business Operations Representatives Process and System Governance and Controls 5
Implementing data management Planning Design Implementation Administration Lay the foundation Establish framework Plan into action Measure to improve Conduct gap analysis and identify pain points Build business case Link investments to: Compliance Risk Management Cost reduction Value/risks defined Get buy-in from Finance and Operations Gain leadership buy-in Vision Value proposition Guiding principles Proposal and approval Refine scope Create and validate project plan Key measures of success Establish foundational architecture Design end state Design people roles: Governing body Stewardship Ownership Design process: Functional requirements Compliance Risk management Policies Design technology: Technical requirements System design Usage validation Determine audit points Design security Data standards and quality Meta data management Establish rollout approach (phased or Big Bang) Populate roles Communication High impact users Broader organization Conduct training Core roles Functional users Implement policies and processes Implement technology tools: Workflow Audit reporting Security Reporting Identify key governance metrics: Availability Accessibility Auditability Consistency Quality Security Assess and report program success to Steering Committee and Executive Sponsors Work with early adopters and supporters to get feedback and incorporate improvements Develop a governance community to share practices Expand program to other areas 6
w that we have the data under control, how do we report on it? Data Reporting and Analytics Security controls and consequences Feedback mechanism Quality control and issue resolution Feedback mechanism (including functional review) Quality control and issue resolution Data need by audience Data source consolidation methodology Integration of work flows into reporting processes Reporting need by audience Prioritization methodology Integration of work flows into business and HR processes Authoring and publishing model Metric definitions and index Data dictionary Data entry guidelines and process flows Ongoing alerts and triggers Master report list Format guidelines New report rollout and training Ongoing alerts and triggers 7
Example process: Finding the required data 8
Example process: Building the metric Year 1 Updated at least quarterly Benchmark available Year 1 Year 2 Metric Current data source Multi-audience application Targeted data source Required (e.g., compliance) Easily accessible Year 1 Used in existing standard report Year 2 Year 2 Requested on ad hoc basis t Priority 9
Example process: Creating the report Year 1 Easy to interpret Year 2 Actionable Report Has high org. value Needed to manage org. Requested frequently Year 1 Year 2 t Priority 10
Visualizations for HR and workforce dashboards Four-Quadrant Dashboard Pure Numeric Reporting Blended Dashboard Heat Map Format 11
Getting the information to your customers: Business intelligence tools vs. HR analytic reporting solutions Build from Scratch with Traditional BI Tools Training/Rollout Define Metrics and Dashboards Pre-Built Content Pre-built solution: Faster time to value Assured business value Lower total cost of ownership DW Design Back-end ETL and Mapping Months or Years Tool Training / Rollout Define Metrics and Dashboards DW Design Back-end ETL and Mapping Weeks or Months Solution Assumption: Ability to use 60% 70% of pre-built content as-is out of the box Source: Workforce Information Program Business Intelligence Solution Evaluation (May 2011). 12
Data warehouse database management systems Master data management product data solutions are software products that: Support the global identification, linking and synchronization of product information across heterogeneous data sources through semantic reconciliation of master data Create and manage a central, databasebased system of record or index of record for master data Enable the delivery of a single product view (for all stakeholders) in support of various business processes and benefits Support ongoing master data stewardship and governance requirements through workflow-based monitoring and corrective action techniques MDM Software market is mostly dominated by large ERP vendors or organizations that often build/customize into their own solutions 13
Business intelligence and analytics platforms Gartner analysis has been expanded to include Analytics in scope for these solutions Many use different cases and levels of maturity that span four distinct phases: descriptive, diagnostic, predictive and prescriptive analytics More organizations are building diagnostic analytics that leverage critical capabilities, such as interactive visualization, to enable users to drill more easily into the data to discover new insights User activity in the BI and analytics platform market is from organizations that are trying to mature from descriptive to diagnostic analytics The trend toward decentralization and user empowerment will greatly enhance organizations ability to perform diagnostic analytics 14
Bringing it all together 15
Questions 16
Today s presenters Dave Young Consultant dave.young@ 972.365.1840 David Zinn Sr. Consultant david.zinn@ 972.701.2753 17