Traditionally companies have looked at their business model in terms of people, processes and technology and have implemented technology systems that are application-centric and/or process-centric. In this traditional approach, information is often thought of as an afterthought and is “bolted on” to the ERP application. This traditional approach has resulted in the information assets of the company being in a state of being disjointed and fragmented, hindering the company’s ability to take advantage of its information assets. As companies are started to take advantage of their information assets for the purposes of competitive advantage, increasingly there is a need to have an information-centric approach. In the information-centric approach, information is at the core of people, processes and technology.
Information Governance: Reducing Costs and Increasing Customer Satisfaction Chris Cingrani SAP EIM Practice LeadCapgemini
What We Hear From Our Clients Regarding Data Issues “I have a single instance of SAP, do I need MDM?” “I don’t have a single view of key things like customer, vendor, product, stock.” “I am convinced that I need an MDM solution - should I ‘build’ or ‘buy’ it.” “Who really owns the data? Is it IT or the Business? My Business counterparts want me (IT) to clean up their data!” What we hear in the market “How do I get my business counterparts to take ownership of their data?” “I now have more and more data about consumers, citizens, complaints, sales transactions, web/social activities – if we can harness this we would really be really good.”
Why Does Data Governance Matter? Wisdom Joining of Wholes
Organizations increasingly relyon their data for reporting and decision making
Data is an Organizational Asset, which is increasingly becoming a Business Enabler rather than merely being stored for administrative purposes
Information is a Product built up of pieces of data that are collected from within as well as outside organizational boundaries
Organizations need to develop a Vision on Data Governance in order to have good control of their organizational information
A Governance Strategy is needed to create awareness, get management support and provide tools to execute
Knowledge Formation of a whole Information Connection of Parts Data Gathering of Parts
The Impact of Information Initiatives without Data Governance MDM without Data Governance Leads to un-clear / un-unified / un-approved corporate definitions Leads to unbalanced data quality requirements Leads to lot of ‘transformations’ between environments due to a missing single definitions BI without Data Governance Leads to extra effort in alignment of data structures Leads to publications to unauthorized people Leads to publications of wrong information Data Distribution and Sharing (cloud) without Data Governance Leads to unauthorized distribution of data Leads to ‘spreading’ of incorrect data throughout the organization or beyond the organizational limits Data Governance is about setting principles on how data shouldbe used and how it is used to support operational growth andhow to maintain that in the future
What Is Information Governance? Information GovernanceA discipline that includes people, processes, policies, and metrics for the oversight of enterprise information to improve the business value High Value Information: Optimized Business Processes Smarter Business Analytics Timely Mergers and Acquisitions Compliance with Laws and Regulations People Process Policies & Standards Metrics
What Aspects of Information Can Be Governed? Begin with the business goal that is enabled most by quality information. People Information ownership and accountability Information access (who, when, where, what) Process Data handling (creation, updating, deleting) Information storing (archiving, security) Metrics Data quality levels Reporting (who, when, what) Policies and Standards Data definitions Allowable values Information architecture
Data Governance Overview – 3 Simple Constructs Roles: What roles and responsibilities are needed for, or impacted by data governance? Legislation Jurisdiction Execution Process:
List legislation processes (example: Define Scope, Create Common Definition, Define Meta-data)
For each process, create process flows, list activities and participating roles.
For each process, create process flows, list activities and participating roles.
Tools: What tools are needed to support processes, store documents, etc. Scope Data Area
Which data is in scope (Part, Customer, etc.) and at what level (entity, attribute, etc.)?
Which aspect of architecture (data-model, integration, data-warehouse, etc.)?
What is aim (data quality, common definition, redundancy, consolidation, etc.)?
Which region or process area?
Traditional Approach Information Centric Approach People People Process Technology Information Process Technology Application & Process centric – information is often an after-thought Information-centric – information across people, process & technology Information is at the Core of Capgemini’s SAP ERP Approach
Reducing Costs: Improved Data Management Rising costs associated with managing data Lack of policies and processes in place to manage the data. Clients often discuss issues around multiple versions of the truth. Multiple versions of the truth exist may exist from a duplicate data standpoint, as well as in inconsistent business rules. Lack of basic Information Governance Lack of a centralized business glossary (or data dictionary) that is readily available to document the single version of the truth within a given data domain. Lack of an approval process. How to handle changes to the data definition or business rules? Inability to understand the impact of a change. What other implications the change will have (to data, systems, processes, etc.)
Better Decisions: Leveraging Your Data Multiple Versions of the Truth Exist Need a process to address the issues associated with multiple versions of the truth (both with data itself and the definitions of data. Traceability - process and tools to trace back to our business glossary how data is defined and ultimately who is responsible for the data (both from a business and IT perspective). Customer Satisfaction – Internal and External Customers Internal Customer Increase satisfaction due to having clear visibility into the data and data definitions External Customer Increased satisfaction may be as basic as not having multiple versions of my account when I call in to place an order
The Business Case for Data Governance The main benefits of being in control of your data: Increased Revenue
Revenue growth increasingly depends on the creation of innovative products that are constructed wholly from information. Effective data management integrates data from multiple sources, multiple markets and multiple asset classes. With accurate, timely data, professionals can craft products that respond to urgent market demands - and anticipate emerging opportunities.
Reduce Time to Market
One of the biggest barriers to new product development is inadequate access to actual data. By integrating multiple data streams into a single view that can be monitored and managed, high quality data management facilitates effective financial modeling and efficient decision making.
Business professionals must be at liberty to focus on what they do best – manage assets, products and portfolios. The right data management tools deliver the information your professionals want when they want it – and allow them to make changes in sources, field assignments and reports without the time-consuming intervention of developers or IT technicians.
Business Process Benefits to Information Demand Identification Demand Generation Lead Management OpportunityManagement Invoicing Cross-/Up-sell Service Lead Management Opportunity Creation Data Business processes Yearly Billing – DSO 360-Degree View of the Customer Core Data Processes – CREATE, UPDATE, DELETE Enterprise Information Management – CRM Technology Improvements, Data Archiving Core Data Processes
Capgemini’s Data Centric Framework:Data Governance and Stewardship Business value and value longevity are driven from business decisions that are traceable back to reliable facts generated from repeatable processes. The dimensions of Data are: Data Governance Activities 5 Metrics – meaningful for improvements to the processes, define objectives for the technology and reward the organization for adherence. 1 Processes – to provide consistency, understanding, transparency and accountability. 5 1 DataGovernance 4 2 4 Technology – to help enforce standards, policies and delivery of information at the time and place of need 2 Operations – that are predictable with controls for quality and drivers for operational decisions 3 3 Organization – to execute the processes with the technology for business value realization and operational improvements. Governance coupled with tools are the key to creating and delivering a healthy data lifecycle.
If Collaboration is Key – What Should My Organization Be Doing? Traditional Approach Information is shared via email or spreadsheets Lack of an audit and decision trail No change management Unable to track progress of the data conversion/definitions Processes are available, but unable to manage this process A New Approach – Best Practices for Information Governance
Best Practices for Information Governance Key Requirements for an Information Governance Solution Collaborative Environment Ability to provide role and project based security Project Management capabilities Approval process for data objects and data definitions
SAP Best Practices for Data Migration Architecture SAP Target Environment Data Staging and Test Environment Legacy Data Environment Loading Performance Analysis Dashboards and Business Reporting Flat Files/ Excel Staging Area Pre-Built load routines for SAP Objects Databases Data Services Platform Name Parsing Business Validation Rules Files Extract & Profile IDocs SAP Configuration Extraction Address Parsing & Correction Applications Automatic SAP Config Validation Validate & Load Cleanse Transform Material/Product Parsing Matching XML Reconciliation Transform Data into SAP structure Transparency, Communication and Governance SAP Business One
SAP Best Practices for Information Governance in Data Migration v1.0 (PILOT) Key Features: Information Governance Data Dictionary provide a collaborative environment through which project teams and business users can document and govern key metadata associated with their business data. Data Conversion Inventory for Data Migration allows for the cataloguing of data objects that will be converted into your SAP environment. Best Practices Repository for Data Migration, Data Quality and MDM provides a variety of best practices documents, links, that are aligned with SAP’s ASAP Methodology. Extend the Best Practices for your Organization provides the ability to add additional business areas, source and target system information, and other key data metrics that will allow you to monitor and measure Information Governance in your SAP and non-SAP systems.
SAP Best Practices for Information Governance in Data Migration v1.0 (PILOT) Key Features: Project Management All information management initiatives require a good project management framework in order to ensure successful delivery. In order to support projects, the following core capabilities are included: CRM, ERP, HCM, Information Governance and Data Quality Project Plans Assign Planned & Non-Planned Tasks to Users Support the Register of Issues & Risks Defect Tracking Status Reporting
Information Governance is an ongoing process that involves both business and IT parts of the organization Benefits of Information Governance often realized are reduced costs and increased customer satisfaction The SAP Best Practices for Information Governance v1.0 provides a collaborative environment and project management capabilities that are often lacking for Information Governance Key Learnings 32