master datamanagement13 02-12

IS6120 Master Data Management 13-02-2013

Master Data Management

Student Name Student Number

Andrea Harrison 106006019Chris Corcoran 112221431Deirdre O’ Leary 112221671Niamh O’ Farrell 108427127Christine Coughlan 108322724

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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 Management2


Examples of Master Data Dimensions

• Customer

• Products

• Supplier

• Financial

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Types of Data in an Enterprise

• Unstructured• Meta-Data• Hierarchal

• Transactional• Analytical Most Important• Master Data

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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”

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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 with

one another7


Importance of MDM

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• Described as “the DNA of every company”

• Imperative to manage it correctly

• A major improvement for business intelligence

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How is it important?


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

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Why is MDM an issue / why are we even talking about it?

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• MDM issues impact the business

• Increasing complexity and globalisation

• All sides see a major opportunity

• Compliance initiatives

• Enables data governance


Benefits

• Complements services-

oriented architecture

• Reduces errors

• Reporting accuracy

• Data usability

• Simplifies design

• Trustworthy data

• Eliminates data

inconsistency

• Improves accuracy

• Improves data sharing

• Consistent interactions

between systems

• Data quality and reliability

• Clean data

• Authoritative source of

information12


Areas that benefit from MDM

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Case Studies – The Look of MDM Success

An American National Financial Institution

• MDM allowed

synchronisation of financial

reporting and analytical

systems

• Now able to focus on more

value-adding initiatives

A Major European Telecommunications Group

• MDM greatly improved

information quality across

the board

• Reduced time that experts

needed to spend updating

systems14


Technologies of MDM

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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

imperfections”

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MDM Information Architecture

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MDM Processes

The 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. 18


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. 19


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.

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Kalido MDM

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IBM InfoSphere MDM

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Organisational issues and consequences of MDM

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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


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 boardEnterprise data governance teamManagement and execution function 25


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.

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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

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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 28


“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

element29


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 completed 30


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


What is the relevance of Master Data Management for Business Intelligence?

All material taken from Oracle White Papers 2010 & 2011

[1] http://www.oracle.com/us/products/applications/master-data-management/018874.pdf

[2] http://www.oracle.com/us/products/applications/master-data-management/018876.pdf

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http://www.oracle.com/us/products/applications/master-data-management/018874.pdf








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

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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 happening on the operational side of the business

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Business Intelligence Solution

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http://www.google.ie/url?sa=i&rct=j&q=master+data+management+architecture&source=images&cd=&cad=rja&docid=cFzphryzGUs9IM&tbnid=ql5mglj6tfYg0M:&ved=0CAUQjRw&url=http://www.information-management.com/specialreports/20060720/1059570-1.html?pg=2&ei=kuUUUabXFY-GhQfpsICwDw&bvm=bv.42080656,d.ZG4&psig=AFQjCNG412ZqNJYQ6PhFz5xR1GdTCfdGew&ust=1360410293192250


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 applications36


Ideal Business Intelligence Solution

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BI Solution Without Master Data

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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 tools39


Thanks for listening!

Any Questions???

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master datamanagement13 02-12

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