Master datamanagement13 02-12

Master Data Management

Student Name Student Number

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

1

IS6120 Master Data Management 13-02-2013

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
2


Examples of Master Data
Dimensions

• Customer

• Products

• Supplier

• Financial

3


Types of Data in an Enterprise

• Unstructured
• Meta-Data
• Hierarchal

• Transactional
• Analytical Most Important
• Master Data

4


5


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”

6


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 another

Importance of MDM

8


How is it important?
• Described as “the DNA of every company”

• Imperative to manage it correctly

• A major improvement for business intelligence

9


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

10


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

11


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 information


Areas that benefit from MDM

13


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
initiatives

Technologies of MDM

15


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”


MDM Information Architecture

17


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.
20


Kalido MDM

21


IBM InfoSphere MDM

22


Organisational issues and
consequences of MDM

23


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 24


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 function


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.
26


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
27


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
29
element

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
completed


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
32


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 33


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 business

Business Intelligence Solution

35


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 36


Ideal Business Intelligence Solution

37


BI Solution Without Master Data

38


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
39


Thanks for listening!

Any Questions???

40


Master datamanagement13 02-12

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (6)

Similar to Master datamanagement13 02-12

Similar to Master datamanagement13 02-12 (20)

Master datamanagement13 02-12

Editor's Notes