Data Rationalization: Why, What and How?
Data Rationalization
White Paper by
Priyanka Mandal
25 July 2016
Nomura Research Institute Financial Technologies India Pvt. Ltd.
© 2016 Nomura Research Institute Financial Technologies India Pvt. Ltd. All rights reserved.
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Data Rationalization: Why, What and How?
Contents
Executive Summary ................................................................................................................................ 3
Need for Data Rationalization ............................................................................................................... 4
Steps for Rationalising Data................................................................................................................... 5
Understanding the Process of Data Rationalization ............................................................................ 6
Ingraining Rationalization in Business reality ..................................................................................... 8
Global IDs: Data Rationalization Suite ................................................................................................. 9
Data Discovery ......................................................................................................... 11
Data Profiling........................................................................................................... 12
Data Quality............................................................................................................. 13
Data Integration........................................................................................................ 14
Conclusion ............................................................................................................................. 15
© 2016 Nomura Research Institute Financial Technologies India Pvt. Ltd. All rights reserved.
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Data Rationalization: Why, What and How?
Executive Summary
Data Management is imperative
to Organizations
Data Rationalization is the radical
step to good data governance
Organisations of all types and sizes have
experienced significant growth in the
amounts of data they use, generate, process
and analyse. With that has increased, the
technology architectures that house it.
Companies incur huge expenses because of
data management problems, they miss the
market with their products and mergers fail to
deliver intended results. Management of the
huge amounts of data in the best possible way
to derive meaningful information out of them
remains a pain to CEOs, CDOs, CIOs and
CROs equally and savages off the health of
the business.
The Butler Group, a division of Datamonitor,
estimates that approximately 80 percent of
vital business information is currently stored
in unmanaged repositories, making its
efficient and effective use a nearly impossible
feat.
Most organizations employ Enterprise
Resource Planning (ERP) to aggregate, store,
manage and analyse data from many business
activities. As ERP provides an integrated
view of core business processes, facilitates
information flow between all business
operations, and handles connections to
internal and external stakeholders, it entails
reliable, integrated and efficiently governed
data. ERP systems don’t work in silos and
generally interact with many other systems
such as CRM or SRM and other ERP systems
as their existence necessitates integrating
varied organizational systems and facilitating
error-free transactions and production. With
such interactions, the complexity of the data
landscape increases aggressively and the
need for a Single Source Of Truth (SSOT) to
ensure that all systems access the same data
keeps becoming more and more imminent.
While the need for SSOT is understood, one
of the biggest trouble which hits back at
enterprises like a Frisbee is the integration of
bad data and the use of such data in business
operations resulting in present losses and
misconstrued future.
“Although the problem is big, so is
the potential payback. The best part
of a Data Rationalization project is
the demonstrable greater and
quicker ROI."
- AMR Research (an independent US research
and industry analyst)
Master data that is correctly classified,
normalized and rationalized with a common
taxonomy is the key to good governance and
successful growth of organizations. Data
rationalization is one of the most cardinal,
crucial and necessary steps that organizations
should undertake to ensure data quality.
Global IDs through automated discovery,
data profiling, quality analysis and metadata
documentation allows companies to create
transparency, enhance accuracy and reduce
the resources required to manage data assets.
© 2016 Nomura Research Institute Financial Technologies India Pvt. Ltd. All rights reserved.
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Data Rationalization: Why, What and How?
Why are organizations failing in governance of data?
Need for Data Rationalization
All large data ecosystems generally
suffer from storing redundant,
outdated and trivial data. The systems
may even contain multiple copies of
the outdated and trivial data. It goes
without saying that enterprises would
like to cleanse their system and take
out the ROT (Redundant-OutdatedTrivial) data.
Such ecosystems contain bad data for
some very basic reasons which are difficult
to weed out.
Large organizations often possess thousands
of databases, acquired through decades of
organic growth or M&A activity. In addition,
many of these databases contain redundant
data. [1]
Hugeness & Complexity in data
In his book, “Too Big To Know, David
Weinberger, an American technologist,
professional speaker, and commentator
explains a key property of the networking of
enterprises: hugeness.
In the era of e-commerce and too big to fail
banks, with technological advancements, the
amount of data has become humungous and
that increases complexity. And as data
ecosystem get more and more complex, it
becomes more difficult to understand and
that in turn means that the governance and
security of that data is increasingly an issue.
Companies today, have environments with
thousands or even tens of thousands of
databases. It is quite impossible to know
which data is sensitive and where it resides,
let alone reacting and reporting in stress
conditions.
However technical this complexity sounds,
such an ecosystem is a major hindrance to
business. If the data environment is not
understood by any, getting analytical
information based on the data becomes
difficult and worse still, even if that happens,
businesses could end up getting wrong
information
about
their
customers/products/business and taking
incorrect decisions, even leading to dire
consequences such as the 2007 Financial
Crisis.
Such hugeness and complexity in data
generally stems from Mergers &
Acquisitions and even plain organic growth
of companies becoming large institutions.
Merger and Acquisition Activities
Mergers and Acquisitions are one of the
prime reasons why businesses have disparate
systems as silos and unintegrated data.
Mergers and acquisitions are among the
biggest challenges for enterprises and their IT
organizations to navigate.
M&As result in success by consolidating
operations and inventory as well as sharing
and integrating designs and leveraging use of
common data, people, processes and
operations. If enterprises fail to inherit the
data from both the companies, synergies are
rendered vulnerable and the integration
process is dragged. This in itself may threaten
the success of the merger or acquisition.
© 2016 Nomura Research Institute Financial Technologies India Pvt. Ltd. All rights reserved.
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Data Rationalization: Why, What and How?
Data Rationalization: What is it?
- Steps for Rationalizing Data
Data Rationalization forms the backbone of effective Data Governance. Rationalized data is
accurate, complete, relevant, and trustworthy and remains consistent across locations, channels
and services in an enterprise.
Data Rationalization helps provide a common dictionary to the enterprise. By identifying common
data entities, and how these relate to other pieces of data, MDM solutions become better at
accommodating the needs of all the systems which require the master/reference data.
To be able to effectively locate, classify, reuse, and manage enterprise data assets, it is necessary
to be able to form a comprehensive inventory. Big businesses become humongous in their volumes
of data because of their diverse lines of business, applications and technologies. This ungoverned
data is not only difficult to work with but drives huge costs for the businesses. Visibility into all
forms of data becomes the first major step towards bringing them all together. Whether the data is
held in structured or unstructured formats, it has to be curated and brought upon a common
platform.
Once the infrastructure outlook of the business has changed to being data-centric, the overhead of
the company reduces manifold. But, there should be a repository of semantic domains (e.g.
business names, definitions, and relationships) embedded within our database models that can be
reused for centralized operations and efficient use. Data-centric infrastructure should be
sustainable and profitable to the company. Businesses shouldn’t be spending a huge chunk on
maintaining data in the governed form. Normalized, integrated and rationalized data is the key to
sustainability. It empowers businesses with enhanced applications, newer insights into their
customer behaviour and product/service development while reducing costs.
© 2016 Nomura Research Institute Financial Technologies India Pvt. Ltd. All rights reserved.
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Data Rationalization: Why, What and How?
Understanding the Process of Data Rationalization
Master data rationalization is a multistep, iterative process that involves the discovery, profiling,
cleaning, classification, attribute enrichment and integration of master data.
Key to this process is the proper classification of the item master record based on the business’
data dictionary. Most systems use some sort of taxonomy to classify items. However, for use
throughout the enterprise and with external partners, organizations should select a taxonomy that
delivers depth and breadth, such as UNSPSC (the United Nations Standard Products and Services
Code), and that allows granular visibility of the item. [2]
Steps of Data Rationalization

Step 1: Discovery & Profiling
Master data rationalization begins with the recognition of all the structured and
unstructured sources of the enterprise’s data and their metadata. This involves the
involvement of data stewards or people responsible for maintaining data in all lines of
business. Data has to be extracted from all internal systems and any third party or external
systems as well. This data has to be stored in a database and their scope and range is
recognised.

Step 2: Cleansing
Once profiled and aggregated, the data is subjected to an initial screening to identify
duplicate records. Businesses should write rules to identify exact matches and probable
matches for LEIs, country names and other attributes (e.g. Client name). But rule-based
processing will generally be inadequate to manage the volume of data. This process would
require SMEs to identify and eradicate the redundancy.

Step 3: Classification
Classification is a step of paramount importance. With the data dictionary as the
classification standard, all records have to be identified and classified correctly. Here the
most critical element is that the businesses have to lay down their taxonomy holistically in
a way which covers all their involvements exhaustively. Then again, the best practice
would be to use widely adopted taxonomies such as UNSPSC, NATO, or eClass which
shall improve the performance over legacy or proprietary taxonomies and to append any
customised taxonomy unto it. This step shall give the best results if it uses a tool which has
a built-in taxonomy manager grown over the years.
© 2016 Nomura Research Institute Financial Technologies India Pvt. Ltd. All rights reserved.
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Data Rationalization: Why, What and How?

Step 4: Mapping & Lineage
Classification takes all records and puts them under some predefined hoods. As important
as this step is, it is equally important to know how are the records related and how have
they flown across systems in time. Data mapping bridges the relationship between all
attributes as has been defined in the databases and also should be able to auto-map all the
implicit relationships. This ensures that the relations between records are as they should be
and exhibit coherence. The data life cycle, called data lineage, includes information about
the data's origins and where it moves over time and describes what happens to data as it
goes through diverse processes. It simplifies tracing errors back to their sources and
reduces the many risks associated with managing data, such as security, privacy and
intentional and accidental exposure of sensitive data.

Step 5: Integration
Once the records have been cleansed, enriched, mapped and its lineage established, they
undergo a second round of duplicate identification. All redundancy is removed, this time
through manual intervention by SMEs even with more precision. Any anomalies are
remediated.
After these operations are conducted, businesses should be able to cut down on the number of
databases and eventually searching and reporting should be faster and in an organised and efficient
way.
Steps for Rationalizing Data
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Data Rationalization: Why, What and How?
Ingraining Rationalization in Business reality
- Real Life Examples
Some real life examples shall clarify how master data management helps to serve customer better
as well as helps businesses to improve revenue opportunities and increases goodwill.
Problems
Solutions
Benefits
360o view of Customer:
Enhanced Customer
Experience:
Retail Bank:
Customer Profiling &
Predictive Analysis:
To categorise customers based on
their
behaviours
and
target
appropriate customers with different
products and services.
Profile all databases to know where data
about customer resides. Eliminate
redundancy by destroying duplicate
data. Strengthen customer profile by
attribute enrichment for address, phone
numbers, SSN (PAN) numbers etc.
Create golden source of truth and
generate & distribute data in
standardised formats. Create profiles
and apply automated business rules to
achieve efficient targeting.
Introduction of new opportunities in
the form of services like: Offering
credit cards to customer based on
their savings account.
Enhanced customer satisfaction
Healthcare Company:
Outcome-based treatment:
Aggregated Patient Data
Patient data must be aggregated
from unstructured sources, the data
must be kept private, secure and
HIPAA compliant.
Patient data was discovered and
gathered from unstructured sources,
linked through years of records and
aggregated. All critical data and LEIs
were identified and they were
segregated and secured. This data was
used to recognize patterns between
patient demographics and geographies.
Targeting patients with
appropriate treatment
Recognition of geographies where
healthcare and hygiene was
neglected
Discovery of diseases which were
common and rare.
Predictive analysis on
demographics and diseases.
© 2016 Nomura Research Institute Financial Technologies India Pvt. Ltd. All rights reserved.
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Data Rationalization: Why, What and How?
How to achieve data ecosystem rationalization? [1]
-Global IDs: Data Rationalization Suite
“Global IDs data rationalization is like pulling back the curtains and getting the giant panoramic
view of a vista you’d only seen before in pieces. Only when you can see and appreciate the data
landscape in its entirety can you begin to make thoughtful and intelligent decision based on what
the data can do for your business”
-
Arka Mukherjee, Founder and CEO
The Remedy
Data Rationalization allows organizations to simplify their data landscape, systematically
eliminating redundant databases and significantly reducing data management costs. It creates a
path toward greater efficiencies and lower costs through:



Decommissioning databases with obsolete, duplicate, non-critical or otherwise unused
information
Rationalizing databases that have similar information but are critical to the business
Protecting and monitoring critical databases that contain core business information
The Data Rationalization Solution Suite (DRSS) is a comprehensive suite of applications that
allows organizations to rationalize their core databases in a systematic way.
In order to create a foundation for rationalization, DRSS performs four core activities




Data Discovery
Data Profiling
Data Quality
Master Data Integration
Once these activities are complete, candidates for decommissioning and rationalization are
identified. A program is initiated to systematically reduce cost by reducing the number of databases
that need to be maintained.
Integrate disparate data to create “golden copy”
In information systems design and theory single source of truth (SSOT), also known as single
point of truth (SPOT) or golden copy refers to the practice of discovering, linking, aggregating
and storing information in such a way that every data element is stored exactly once (e.g., in no
more than a single row of a single table). Any possible linkages to this data element (possibly in
other areas of the relational schema) are by reference only. Because all other locations of the data
just refer back to the primary "source of truth" location, updates to the data element in the primary
location propagate to the entire system without the possibility of a duplicate value somewhere
being forgotten. [3]
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Data Rationalization: Why, What and How?
Deployment of an SSOT architecture is becoming increasingly important in enterprise settings
where incorrectly linked duplicate or de-normalized data elements (a direct consequence of
intentional or unintentional de-normalization of any explicit data model) poses a risk for retrieval
of outdated, and therefore incorrect, information. [3]
The Data Rationalization Solution Suite (DRSS) was specifically created to help financial
services organizations govern and rationalize market data. The software can scan and monitor
~300 types of exchange and non-exchange data feeds to understand the level of redundancy
across these data feeds. Duplicative market data feeds become potential candidates for
rationalization.
Product Suite
The Global IDs Product Suite contains 30 layers of product functionalities to
address the diversity and complexity of corporate data landscapes.
Data
Discovery
Data
Profiling
Data
Recognition
Data Lineage
Data
Comparison
Data
Classification
Data
Mapping
Data Rationalization: Iterative Process
Data Rationalization: Iterative process
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Data Rationalization: Why, What and How?
Bring to light all that has been dashed in dark corners: Data Discovery
“Getting value out of big data is more than just slicing and dicing billions of records. It requires
discovering what you have and getting the data ready for analysis to use without boundaries”
-
Peter Schlampp, Vice President of Products, Platfora
The foremost and one of the most complex steps of the process of data management remains
answering these questions:
 What data is available?
 How are the data sources structured?
 What are the characteristics of these data sources?
Data discovery does just that. It helps uncover the architectures and the metadata of data sources
and discover the semantics of a data element in data sets. The metadata objects in the data store
help applications to make sense of the data.
Metadata is a means to foster integration of diverse applications, a way to cull and relate
information from data silos, a challenge currently faced by electronic records. Managing metadata
is the direction of the near future, particularly as content management, records management, and
e-discovery systems converge and consolidate. Deciding what metadata to keep depends on the
needs of a diverse set of interested parties in legal, compliance, records management, information
technology, and business functions. [4]
Features
Benefits
Once all metadata is available, the choice of what data is important to us and where it resides
becomes clear. Having access to metadata means there is an understanding of the expanse of the
data ecosystem, what data sources have been used, what data is in use and what lies redundant.
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Data Rationalization: Why, What and How?
What does the data say? : Data Profiling
Once all the data sources have been discovered, we can form ideas about the data landscape, and
can now choose to know about the data and analyze its quality.
Data Profiling is a systematic analysis of the content of a data source (Ralph Kimball).
Data profiling elucidates what sort of data is stored, what data is related to it and what are the
specific sources where they reside. It thus helps build a relationship, a mapping. Analysis of this
data gives answers to questions like
 Is the data of sufficient quality to support the business purpose(s) for which it is being
used?
 Are any specific issues within the data decreasing its suitability for these business
purposes? [5]
Create & Execute
Plan & Design
ETL
Data Cleansing
Profile Data
Sources
Analyze Findings
Design systems
Review & Manage
Define Audit
Proceudres
Implement Jobs
Report
Data
Profiling
Data Profiling ensures:





Trust in data
Finding problems in advance
Shorten development time on projects
Improve understanding of data & business knowledge
Design newer services & products
With profiled data, whose semantics are clear, one can build a common data dictionary or a
taxonomy which shall contain definitions of all enterprise wide entities. This forms the foundation
or the base layer of good data governance, which shall result in compliance to any regulation or
standards and inevitable business growth.
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Data Rationalization: Why, What and How?
Is your data good? : Data Quality
“In God we trust, all others must bring data.” W. Edwards Deming.
Now imagine, if all we did was bring bad data. It would be an apocalypse. There is an immense
awareness about data quality these days. But then again, organizations have ended up having bad
quality soiled data, where some verticals of business have good data while others requiring the
same data may end up with bad ones. This scenario is still bad as there is no dependability on the
data. The focus should be on having good dependable data across business lines, across assets and
across all verticals and horizontals of enterprise. The dimensions of data quality can be summed
up in the diagram below:
Is there
Rotten Outdated Trivial
(ROT) data?
Does the data
reflect the
semantics used in
your business?
Validity
Completeness
Is all the
necessary
data
present?
Accuracy
Is data
consistent
across the
enterprise?
Data
Quality
Timeliness
Consistency
Integrity
Is the data
available at
needed times?
Are there copies of
data which say
different stories?
Once the data has been profiled, rules of data quality can be reverse engineered out of the data
landscape. This structure of the data is then tested against the dimensions of good quality data. The
ownership of the data is established as the same data could be unimportant to some people while
they may be extremely crucial for some others. This is generally referred to as Data Stewardship
in the data management world. Any duplication of data is removed and the data is cleansed and
eventually can be presented to the data stewards to ameliorate their quality further.
Data Quality is not a one-time process that shall relieve the headache of the CDOs but a recurrent
one which measures and monitors the quality of data assets and continuously improves the quality
of the data.
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Data Rationalization: Why, What and How?
Fighting the gargantuan: Data Integration
Half the work is done in forms of data discovery, profiling and measuring it against data quality
metrics. What remains to be done is create the single source of truth out of the data. A data
glossary/data dictionary is created, which enables data stewards to build and manage a common
business vocabulary and make it available across an organization. This vocabulary ensures that all
data assets are recognised and classified under proper semantics, which provides association
between technical metadata and business context. While automation through business rules can
help govern the classified data, manual intervention is required as all business rules can’t be
simulated. Local data stewards, who understand their businesses clearly and comprehensively have
to work along with the tool to eliminate duplicate data, do away with unused sources and data,
eradicate redundancy and store relevant information in such a way that it facilitates all lines of
business and operations which require it.
The benefits of this process are multitudinous. A 360 view on all business entities is one of the
most notable ones. Once the information of which data is important, where is that data, how is it
related to other important data and what is the lineage of the data is obtained, the complexity
decreases manifold and tracking data becomes easy and simpler, the access time is reduced and
any reporting is faster and brings out coherent reliable results. An enterprise wide knowledge of
the data not only improves business but also makes it stress-situation ready.
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Data Rationalization: Why, What and How?
Conclusion
Rationalizing data on an enterprise scale is a herculean task and will always cause pain to
businesses, data officers and risk officers. Hence, beginning at the root cause level becomes
extremely crucial. Unless these fundamental issues that have been highlighted in this paper are
dealt with, all the other tasks that face the business will be impossible to tackle or, at best, any
results derived out of them will be dysfunctional.
In other words, discovering and understanding your data landscape, building a strong infrastructure
and cleaning and organizing data are necessary conditions to effectively manage, govern,
understand, and analyse information assets, realize significant time savings and minimize the
amount of real analysis so often performed when changes or new applications are required and
ultimately increase the ROI or the value of the business.
Global IDs Uniqueness [1]
In contrast to traditional manual approaches that focus
on reducing costs in silos, Global IDs software reverseengineers the data ecosystem to identify candidates for
database rationalization. Since data ecosystems are
large and complex, reducing costs from these
environments has a significant ROI.
This perspective allows organizations to see their
enterprise data in a holistic manner, allowing visibility
into the way in which business is conducted across the
enterprise.
The Global IDs machine centric approach to master
data management creates a foundation for firms to
manage their data assets. Through automated
discovery, data profiling, quality analysis and metadata documentation we allow companies to
create transparency, enhance accuracy and reduce the resources required to manage data assets.
Their machine-centric approach to data governance is much more cost-effective than traditional
approaches. It is:
1. Automated (greater than 90%)
2. High speed
3. Continuously evolving (through increased awareness of the data landscape)
Some of the world's largest organizations have used this approach to bring transparency and
visibility into complex data landscapes.
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Data Rationalization: Why, What and How?
References & Further Readings

Data Rationalization in the capital Markets Sector
https://www.capgemini.com/resource-file-access/resource/pdf/data_rationalization_in_the_capital_markets_sector.pdf

Agile Data Rationalization for Operational Intelligence
http://www.insideanalysis.com/wp-content/uploads/2013/04/PhasicBriefingRoomOpIntel.pdf

Achieving Successful Applications Rationalization Initiative
http://www.dbta.com/Editorial/Trends-and-Applications/Seven-Steps-for-a-Successful-Applications-RationalizationInitiative-81750.aspx

Managing Company’s Data Portfolio Using Data Rationalization
http://www.information-management.com/specialreports/20030311/6456-1.html
Citations


[1] Data Rationalization : http://www.globalids.com/data-rationalization-software
[2] Item Master Data Rationalization: http://www.zycus.com/resource-centre/resources/whitepapermeta-group.pdf


[3] Single source of truth: https://en.wikipedia.org/wiki/Single_source_of_truth
[4]Examining Metadata: http://content.arma.org/IMM/SeptOct2009/IMM0909examiningmetadataitsroleinediscovery.aspx

[5] Data Profiling: https://datasourceconsulting.com/data-profiling/
Authors
Priyanka Mandal
Associate Software Engineer - IT Consulting
NRI FinTech India Pvt. Ltd.
Office : +91-33-6604-1000
Email : [email protected]
Agomoni Sarkar
Associate Software Engineer - IT Consulting
NRI FinTech India Pvt. Ltd.
Office : +91-33-6604-1000
Email : [email protected]
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Data Rationalization: Why, What and How?
About Us
Nomura Research Institute Ltd.
Nomura Research Institute (NRI), founded in 1965, is a leading provider of consulting & system solutions.
Headquartered in Tokyo, Japan, NRI has a presence in all the major financial centers around the world,
providing various services in the areas of management & system consulting, system integration, IT
management, and IT solutions for the financial, manufacturing, and service industries. With more than
5,000 employees worldwide, NRI is able to leverage its global consulting business to deliver innovative,
cross-asset, front-end financial IT solutions for investment banks, asset managers, banks and insurance
providers in the global market. For more information, visit www.nri.com
Nomura Research Institute Financial Technologies India Pvt. Ltd.
Founded in 2001 and acquired by NRI in 2012, Nomura Research Institute Financial Technologies India
Pvt. Ltd (NRI FinTech) is a wholly owned subsidiary of NRI. For more information,
visit www.nrifintech.com
Global IDs
Global IDs was founded in 2001 by Dr. Arka Mukherjee, a data management industry expert with extensive
experience in master data management and data warehousing. By predicting the data deluge facing Fortune
500 companies, the Global IDs team was able to address the specific challenges of complex data
environments almost 10 years before the advent of Big Data. We are passionate about data design and
information management and take great pride in building software that solves complex problems for the
world's most demanding institutions.
Based in Princeton, NJ, Global IDs provides software for enterprise information management (EIM). Over
the last 10 years, Global IDs has provided Data Management Software products to the world’s largest
companies. For more information, visit www.globalids.com
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not to be construed as providing advice, recommendations, endorsements, representations or warranties of any kind whatsoever.
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