Explore the Possibilities
2013 HR Service Delivery Forum
Best Practices in Data Management: Creating a Sustainable
and Robust Repository for Reporting and Insights
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
The data maturity model
No data standardization or data integrity best practices
Individual applications are maintained
separately
Business need drives data without considering other factors
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
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 Data
Maturity
Fractured
Organized
Governing
Mature
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
Data Management Group IT Support Group
Data Ownership Group
Data Governance Steering Committee
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.
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.) The Data Governance Steering
Committee acts as a cross- functional leadership team to provide direction and oversight in the Data Governance model.
The Data Management Group members are the primary caretakers of the data asset. These are the Data Stewards.
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
This is not an easy program to put into place
Executive Support
Corporate View of Data
Metrics
Policies
& Standards
Processes
Process and System Governance and Controls
Within HR, data governance becomes everybody’s job
HCM Strategy/
Governance HR Leadership
Tech Leadership
Operations COE
CPO
VP/AVP of HR
CIO
CTO
HR Relationship Manager
HR Business Partner
Service Center Manager
Business Operations Representatives
Compensation
Benefits
Performance Management
Payroll
Implementing data management
Planning Lay the foundation
Implementation Plan into action
Administration Measure to improve Design
Establish framework
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
Now that we have the data under control, how do we
report on it?
Feedback mechanism (including functional review)
Quality control and issue resolution
Security controls and consequences
Feedback mechanism
Quality control and issue resolution
Reporting need by audience
Prioritization methodology
Integration of work flows into business and HR processes
Authoring and publishing model
Data need by audience
Data source consolidation methodology
Integration of work flows into reporting processes
Master report list
Format guidelines
New report rollout and training
Ongoing alerts and triggers
Metric definitions and index
Data dictionary
Data entry guidelines and process flows
Ongoing alerts and triggers
Reporting and Analytics Data
Example process: Finding the required data
Example process: Building the metric
Current data source
“Required” (e.g., compliance)
Targeted data source Multi-audience
application
Used in existing standard report Updated at
least quarterly
Yes Yes
No Yes
No
Yes
No
Benchmark available
Metric …
Yes
No Not
Priority
No Yes
Year 1
Year 2
No
Requested on ad hoc basis No
Yes
Year 2 Year 1 Year 1
Easily accessible
Year 2 Yes
No
Example process: Creating the report
Report …
Has high org. value
Needed to manage org.
Actionable
Easy to interpret
Yes
No Yes
No
Yes
No
Yes
No Not
Priority
Year 2 Year 1
Requested frequently
Year 2 No
Year 1 Yes
Visualizations for HR and workforce dashboards
Four-Quadrant Dashboard Pure Numeric Reporting
Heat Map Format
Blended Dashboard
Getting the information to your customers: Business
intelligence tools vs. HR analytic reporting solutions
Build from Scratch
with Traditional BI Tools Pre-Built Content
Weeks or Months Back-end
ETL and Mapping DW Design Define Metrics and Dashboards
Back-end ETL and Mapping DW Design Define Metrics and Dashboards Training/Rollout
Training / Rollout
Months or Years
Pre-built solution:
Faster time to value
Assured business value
Lower total cost of ownership
Assumption:
Ability to use 60% – 70% of
pre-built content as-is out of
the box
Tool Solution
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, database- based 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
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
Bringing it all together
Questions
Today’s presenters
David Zinn
Sr. Consultant
[email protected] 972.701.2753
Dave Young
Consultant
[email protected] 972.365.1840
