Data Governance in Mass upload processes Case KONE Finnish Winshuttle User Group 6.11.2014, Helsinki
Just IT Mastering the Data Just IT is a Finnish company focusing on Data Governance and Data Management. We focus on delivering the business benefits to our customers by securing effective and high quality data. Just IT is a pioneer in extended data governance governing your data both inside and outside the enterprise boundaries. We do things differently no more long & heavy projects, but concrete results, fast in use for the business.
Why do we need a Data Governance Model? Some research about data quality: 30% of all operational errors are due to poor information quality (Reuters) 83% of companies experienced data issues during their most recent IT project (David Rint: The users view of why IT projects fail Gartner Insights) Customer data degrades at a rate of 24% per year (Beth Eisenfeld, Research Director, Gartner) It is worth every cent to invest on Data Quality and Data Governance Take 1% of top-line revenue, that s the annual benefit one gets from having a data governance program (IDG)
Data Is The Common Element People Data Process Technology DATA All of these four elements must work together Previously data was tied to a specific system or technology, today data is spread throughout an enterprise or outside in the cloud Data is now treated as a separate type of asset Accurate data can provide a strong competitive advantage
Data Governance and MDM Why Corporate Goals & Strategies IT Strategy Data Strategy Business Strategies Data Governance Data Management Who, What, When, Where Roles / Responsibilities Policies Procedures Business Rules Data Usage Awareness Data Context Awareness Workflow Data Audits Measures / Metrics Reporting Data Maintenance (create, update, delete) Data Quality Data Integration Data Lifecycle (creation-archive) How Conventional MDM
The importance of Data Governance in Mass Maintenance / Winshuttle Winshuttle is a powerful tool to maintain different types of data: The fast, flexible approach to optimize and automate your ERP data operations The tool itself has good capabilities to support Data Governance model (e.g. authorizations, roles, approving of scripts, ) But the organization, roles & responsibilities need to be clear to all participants. But when a large amount of data is maintained A solid, bulletproof Governance Model is needed PEOPLE, PROCESS, TECHNOLOGY, DATA
Start with the People Name the players: Business & IT Sponsors Data Owners Solution Owners Data Management Organization (DMO) Leader Data Stewards / Data Managers Data Custodians / Data Administrators Domain, Process or System Experts Data Architect(s) Business Intelligence Experts
Plan the Process Individual data maintenance vs mass maintenace What are the differencies in the process What additional steps are needed (quality checks, approvals, etc.) How are the creations / changes documented How are the processes documented Workflows Communication!
Implement the Technology Winshuttle project But that is naturally a subject for another presentation
Select the Data Master Data Employee Customer Material Partner / Supplier / Vendor Organizational Metadata Metadata is information regarding the characteristics of any object, such as its name, location, perceived importance, quality or value to the enterprise, and its relationships to other objects that an enterprise has deemed worth managing. Basically, it s data about data Configuration Transactional Data Usually fairly well governed by ERP systems. But is that data validated against the master and/or meta data? Unstructured How about all that data in Intranets? Or Social Data like Facebook or Twitter? Or your website? Or other websites?
Key Elements of Data Governance The main focus of Data Governance should be to support the business not the other way around People Organization: Business, Data and IT representation Networking with business is a must - Communication is the crucial element Focus on roles and responsibilities Processes To support the business Focus on simplified processes Interaction and integration Technology Enabler not the reason Productivity and Quality Data Business rules Correct and complete data all the time Continuous monitoring and development
An example of Data Governance Case KONE All mass upload are Governed by KONE MDM Team In 2013, KONE had annual net sales of EUR 6.9 billion at the end of the year over 43,000 employees. Winshuttle has been in production use since fall 2013 Now 46 active users in MDM & Shared Services teams As a total, more than 400 people are in Data Management Network managed by KONE MDM Team
An example of Data Governance Case KONE Roles, Responsibilities & Processes are defined, documented and communicated Roles in KONE Head of MDM (Responsible for the process) Developers (for each Business Process) Runners (by Geography & Domain) Solution Owner (Winshuttle & Central) Platform Owner (Technology) Two main processes Daily process Special Cases Used in projects, aquisitions or exeptionally large amounts of data Requires special resourcing, scheduling and controls
An example of Data Governance Case KONE Practical example KONE France quick needs at the end of 2013 Kone France made a new service contract with a very big customer in November 2013 Needed to be in SAP and operational by the beginning of 2014 Over 2900 equipment Over 500 locations and service contracts With a large scope of pricing, maintenance data etc. Business realized that data can t be created manually and one by one needed mass upload First Special case meeting in mid November 2013 With Winshuttle and planned Governance Model + Excellent team, all data in SAP by the beginning of 2014 Most of the time was spent on data quality & duplicate management A big succes for the new process and tools for Mass Maintenance in KONE
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