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Master datamanagement13 02-12


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In class presentation for Business Intelligence by students.

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Master datamanagement13 02-12

  1. 1. Master Data Management Student Name Student Number Andrea Harrison 106006019 Chris Corcoran 112221431 Deirdre O’ Leary 112221671 Niamh O’ Farrell 108427127 Christine Coughlan 108322724 1IS6120 Master Data Management 13-02-2013
  2. 2. The Evolution of Data Processing and Data Management • 1960’s: data in digital format became centralised in a few locations • Allowed the firm to easily maintain single sets of data about the basics of the business • 1980’s: evolution of microelectronics and programming languages • 1990’s: Customer Relationship Management 2IS6120 Master Data Management 13-02-2013
  3. 3. Examples of Master DataDimensions • Customer • Products • Supplier • Financial 3IS6120 Master Data Management 13-02-2013
  4. 4. Types of Data in an Enterprise • Unstructured • Meta-Data • Hierarchal • Transactional • Analytical Most Important • Master Data 4IS6120 Master Data Management 13-02-2013
  5. 5. 5IS6120 Master Data Management 13-02-2013
  6. 6. Transactional, Analytical, Master Data • Transactional data supports the applications • Analytical data supports decision-making • Master Data “is any information that is considered to play a key role in the core operation of a business” 6IS6120 Master Data Management 13-02-2013
  7. 7. What is Master Data Management • Master Data Management (MDM) refers to the process of creating and managing data that an organization must have as a single master copy, called the master data • Can be referred to as ‘Golden Record’ • Without a clearly defined master data, the enterprise runs the risk of having multiple copies of data that are inconsistent 7 with one anotherIS6120 Master Data Management 13-02-2013
  8. 8. Importance of MDM 8IS6120 Master Data Management 13-02-2013
  9. 9. How is it important?• Described as “the DNA of every company”• Imperative to manage it correctly• A major improvement for business intelligence 9IS6120 Master Data Management 13-02-2013
  10. 10. Growing Significance• Gartner: MDM software revenue estimated to have reached $1.9 billion worldwide last year• Expected to reach $3.2 billion by 2015• Social data, “Big Data” and data in the cloud 10IS6120 Master Data Management 13-02-2013
  11. 11. Why is MDM an issue / why are we even talking about it? • MDM issues impact the business • Increasing complexity and globalisation • All sides see a major opportunity • Compliance initiatives • Enables data governance 11IS6120 Master Data Management 13-02-2013
  12. 12. Benefits• Complements services- • Improves accuracy oriented architecture • Improves data sharing• Reduces errors • Consistent interactions• Reporting accuracy between systems• Data usability • Data quality and reliability• Simplifies design • Clean data• Trustworthy data• Eliminates data • Authoritative source of 12 inconsistency informationIS6120 Master Data Management 13-02-2013
  13. 13. Areas that benefit from MDM 13IS6120 Master Data Management 13-02-2013
  14. 14. Case Studies – The Look of MDM Success An American National A Major European Financial Institution Telecommunications Group • MDM allowed • MDM greatly improved synchronisation of information quality financial reporting and across the board analytical systems • Reduced time that • Now able to focus on experts needed to spend more value-adding 14 updating systems initiativesIS6120 Master Data Management 13-02-2013
  15. 15. Technologies of MDM 15IS6120 Master Data Management 13-02-2013
  16. 16. Problems with MDM• Multiple data stores• Disparate systems and inconsistent methods• Information is fragmented• E.g. House Hold Charge“The data is in a number of different formats and it was a huge amount of work to try and match it. There has never been data matching like this done before, so there will be 16 imperfections”IS6120 Master Data Management 13-02-2013
  17. 17. MDM Information Architecture 17IS6120 Master Data Management 13-02-2013
  18. 18. MDM ProcessesThe key processes for any MDM system to bring quality data to the organization are to:• Profile- Understand all possible sources and the current state of data quality in each source. All existing systems that create or update the master data must be assessed as to their data quality.• Consolidate- Consolidate the master data into a central repository and link it to all participating applications.• Cleanse -Clean it up, de-duplicate it, and enrich it with information from 3rd party systems. 18IS6120 Master Data Management 13-02-2013
  19. 19. MDM Processes Contd..• Govern - Manage it according to business rules. Data Governance refers to the operating discipline for managing data and information as a key enterprise asset.• Share - Synchronize the central master data with enterprise business processes and the connected applications. Clean augmented quality master data in its own silo does not bring the potential advantages to the organization.• Leverage - Leverage the fact that a single version of the truth exists for all master data objects by supporting business intelligence systems and reporting. 19IS6120 Master Data Management 13-02-2013
  20. 20. MDM Processes Contd..• Version and Audit - It is important to be able to understand how the data got to the current state. The version management should include a simple interface for displaying versions and reverting all or part of a change to a previous version• Hierarchy Management - If the MDM system manages hierarchies, a change to the hierarchy in a single place can propagate the change to all the underlying systems. 20IS6120 Master Data Management 13-02-2013
  21. 21. Kalido MDM 21IS6120 Master Data Management 13-02-2013
  22. 22. IBM InfoSphere MDM 22IS6120 Master Data Management 13-02-2013
  23. 23. Organisational issues and consequences of MDM 23IS6120 Master Data Management 13-02-2013
  24. 24. Six issues identified: • Lack of data governance • Change management • Lack of executive buy-in • Lack of focus on business processes • “Big Bang” approach • Lack of data validation 24IS6120 Master Data Management 13-02-2013
  25. 25. Lack of Data Governance• Confusion over who owns master data• Confusion over who is responsible for master data• Factors to consider: core competencies for organisation, decision rights, accountability, corporate policies and standards• Common components of a data governance model include:  Data management review board  Enterprise data governance team 25  Management and execution functionIS6120 Master Data Management 13-02-2013
  26. 26. Change Management• Master data will constantly change and this needs to be managed to provide full traceability.• The challenge is achieving timely and accurate synchronization across different systems.• Key elements of change management include the following: justification for change, impact of change and version control.• Changes need to be approved by key stakeholders.• Each information system uses its own “version” of master data.• IT departments use manual and time-consuming processes to keep track of changes, validate them, determine which systems are affected by the changes, and finally update them. 26IS6120 Master Data Management 13-02-2013
  27. 27. Lack of Executive Buy-In • It is common for an organisation to embark on an MDM implementation focusing solely on how they define their data elements and entities • Trouble arises when this activity detracts from a corporate standard or produces information inconsistent with the viewpoint of senior leadership • Senior stakeholders must see the value of the initiative and act in an enforcement role to ensure accountability amongst various stakeholders 27IS6120 Master Data Management 13-02-2013
  28. 28. Lack of Focus on Business Processes• Common to believe that technology automation can act as an acceptable alternative to a defunct operational process. This is untrue• Must allow time for process optimization and re-engineering• At each stage of the data chain, clear business processes are necessary to support the flow of data and, ultimately, the integrity of that data• Business management resistance to change or surrender control 28IS6120 Master Data Management 13-02-2013
  29. 29. “Big Bang” Approach • When companies try to identify and standardize all their master data elements in a single initiative • Many organizations make the mistake of taking on a “big bang” deployment, and find themselves surrounded by project delays, cost overruns, and lost productivity • Instead of trying to resolve all master data issues at once, it is advised to begin small with a pilot project on a single master data 29 elementIS6120 Master Data Management 13-02-2013
  30. 30. Lack of Data Validation• MDM implementations require a significant amount of data validation at various points within the architecture• Solid data validation plan is required both during the implementation and also as part of an ongoing production process• If the scope of the MDM plan only validates the inputs and outputs of the solution, it will become susceptible to downstream issues• End –to- end validation testing must be anticipated and 30 completedIS6120 Master Data Management 13-02-2013
  31. 31. Consequences of MDM on Organisation • Potential to improve business efficiency • Eradicates the difficulty in trying to optimise the customer and supplier relationship • Leads to an increase in information quality • Removes the consequence of poor data management • Leads to faster results • Leads to an increase in productivity, sales and in tangible business benefit 31 IS6120 Master Data Management 13-02-2013
  32. 32. What is the relevance of Master Data Management for Business Intelligence? All material taken from Oracle White Papers 2010 & 2011 [1] management/018874.pdf [2] management/018876.pdf 32IS6120 Master Data Management 13-02-2013
  33. 33. Business Intelligence • 60 - 65% of BI projects fail to deliver on customer requirements • BI tools are designed to help organizations understand their operations, customers, financial situation and other key business measurements • BI tools used to create reports and aid decision making • Poor business intelligence results in poor decision making & impacts on business performance • Operational data feeding the analytical tools is filled with errors, duplications and inconsistencies 33IS6120 Master Data Management 13-02-2013
  34. 34. The Data Quality Problem • Data entered into transactional applications is error prone and poor data quality problems begin at this point • Master Data is not static. It is in a state of constant change with an average of 2% change per month • Across North America, in any given day: • 21984 individuals and 1920 businesses will change address • 1488 individuals will declare a personal bankruptcy • 1200 business telephone numbers will change or be disconnected • 96 new businesses will open their doors • MDM is the glue that ties analytical systems to what is actually 34 happening on the operational side of the businessIS6120 Master Data Management 13-02-2013
  35. 35. Business Intelligence Solution 35IS6120 Master Data Management 13-02-2013
  36. 36. Master Data Management Solution • Previous tools used to analyze data include data mining techniques, OLAP and real time decisions via dashboards • But these tools continue to operate on poor quality data and produce faulty reports and misleading analytics. An analytical solution cannot get to the root cause of the data quality problem. • MDM provides tools that can eliminate duplicate data, standardize data, manage data change and synchronize data • MDM combats data quality issue at the source – transactional applications 36IS6120 Master Data Management 13-02-2013
  37. 37. Ideal Business Intelligence Solution 37IS6120 Master Data Management 13-02-2013
  38. 38. BI Solution Without Master Data 38IS6120 Master Data Management 13-02-2013
  39. 39. In Conclusion…. • MDM improves data quality that is fed from operational applications through to Business Intelligence tools • Provides single view of key business dimensions to data warehouse • Combats the problem of poor data quality at the source • Improves output from Business Intelligence analytical tools 39IS6120 Master Data Management 13-02-2013
  40. 40. Thanks for listening! Any Questions??? 40IS6120 Master Data Management 13-02-2013