This white paper discusses the importance of data integration in mergers and acquisitions. It outlines a data excellence framework to enable smooth integration of acquired companies. The framework defines business rules, data quality dimensions, and roles to ensure critical business rules are followed and optimal data quality is achieved during integration. Following this framework is crucial to fully realize the expected benefits of an acquisition by integrating data correctly according to business needs.
The article is intended as a quick overview of what effective master data management means in today’s business context in terms of risks, challenges and opportunities for companies and decision makers. The article is structured in two main areas, which cover in turn the importance of an effective master data
management implementation and the methodology to get there.
James J Okarimia
Managing Partner
Aligning Finance, Risk and Data Analytics in Meeting the Requirements of Emerging Regulations
Banks must meet more (and more varied) regulations today than ever. The sheer scale and scope of banking regulations, including Dodd-Frank, Basel III and IFRS, pose challenges to all financial institutions, from the smallest bank to the largest financial services enterprise.
The article is intended as a quick overview of what effective master data management means in today’s business context in terms of risks, challenges and opportunities for companies and decision makers. The article is structured in two main areas, which cover in turn the importance of an effective master data
management implementation and the methodology to get there.
James J Okarimia
Managing Partner
Aligning Finance, Risk and Data Analytics in Meeting the Requirements of Emerging Regulations
Banks must meet more (and more varied) regulations today than ever. The sheer scale and scope of banking regulations, including Dodd-Frank, Basel III and IFRS, pose challenges to all financial institutions, from the smallest bank to the largest financial services enterprise.
The Data Governance Annual Conference and International Data Quality Conference in San Diego was very good. I recommend this conference for business and IT persons responsible for data quality and data governenance. There will be a similar event in Orlando, December 2010. This is the presentation I delivered to a grateful audience.
Enterprise Information Management Strategy - a proven approachSam Thomsett
Access a proven approach to Enterprise Information Management Strategy - providing a framework for Digital Transformation - by a leader in Information Management Consulting - Entity Group
Data governance involves setting up procedures and regulations to enable the smooth sharing, managing, and availability of data.
The idea is to prevent an overlap of resources. When you have data governance procedures you experience faster decision-making processes while moving data from just a company’s by-product to a critical asset within the organization. Check out this and know how to build a strong Governance framework for your organization
Small and medium enterprise business solutions using data visualizationjournalBEEI
The small and medium enterprise (SME) companies optimize performance using different automated systems to highlight the operations concerns. However, lack of efficient visualization in reporting results in slow feedbacks, difficulties in extracting root cause, and minimal corrective actions. To complicate matters, the data heterogeneity has intensely increased, and it is produced in a fast manner making it unmanageable if the traditional methods of analytics are applied. Hence, we propose the use of a dashboard that can summarize the operational events using real-time data based on the data visualization approach. This proposed solution summarizes the raw data, which allows the user to make informed decisions that can give a positive impact on business performance. An interactive intelligent dashboard for SME (iid-SME) is developed to tackle issues such as measurement of cases completed, the duration of time needed to solve a case, the individual performance of handling cases and other tasks as a proof of concept. From the result, the implementation of the iid-SME approach simplifies the conveyance of the message and helps the SME personnel to make decisions. With the positive feedback obtained, it is envisaged that such a solution can be further employed for SME improvement for better profit and decision making.
Leading enterprise-scale big data business outcomesGuy Pearce
A talk specially prepared for McMaster University. There is more benefit to thinking about big data as a paradigm rather than as a technology, as it helps shape these projects in the context of resolving some of the enterprise's greatest challenges, including its competitive positioning. This approach integrates the operating model, the business model and the strategy in the solution, which improves the ability of the project to actually deliver its intended value. I support this position with a case study that created audited financial value for a major global bank.
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
Data Quality Management: Cleaner Data, Better Reportingaccenture
In this new Accenture Finance & Risk presentation we explore a process to investigate, prioritize and resolve data quality issues, key to creating a more efficient and accurate reporting environment. View our presentation to learn more.
For more on regulatory reporting, see presentation on Financial Reporting Robotics: http://bit.ly/2qaLK9y
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
Data and the enterprise mission: putting data at the corecorfinancial
Data matters to Financial Services firms. It is their stock-in-trade, a strategic asset that without an accurate and timely data set they cannot operate effectively, they cannot price risk fully and their capital allocation calls are unlikely to be optimal. Data is the ultimate collateral of these firms. For many, it requires a transformational change in their systems, technology and processes How then do you embed strategic data into your enterprise architecture?
Read 2 minute guide
Governance and Architecture in Data IntegrationAnalytiX DS
AnalytiX™ Mapping Manager™ provides this discipline and rigor through its dedicated data mapping methodology as well as its metadata management processes and powerful patented mapping technology. AnalytiX™ Mapping Manager™ was designed and developed to not only fill the gap of having the ability to manage and version mapping specifications, but to also streamline and improve current process and drive standards around the entire process and across the enterprise for all integration and governance processes.
The Data Governance Annual Conference and International Data Quality Conference in San Diego was very good. I recommend this conference for business and IT persons responsible for data quality and data governenance. There will be a similar event in Orlando, December 2010. This is the presentation I delivered to a grateful audience.
Enterprise Information Management Strategy - a proven approachSam Thomsett
Access a proven approach to Enterprise Information Management Strategy - providing a framework for Digital Transformation - by a leader in Information Management Consulting - Entity Group
Data governance involves setting up procedures and regulations to enable the smooth sharing, managing, and availability of data.
The idea is to prevent an overlap of resources. When you have data governance procedures you experience faster decision-making processes while moving data from just a company’s by-product to a critical asset within the organization. Check out this and know how to build a strong Governance framework for your organization
Small and medium enterprise business solutions using data visualizationjournalBEEI
The small and medium enterprise (SME) companies optimize performance using different automated systems to highlight the operations concerns. However, lack of efficient visualization in reporting results in slow feedbacks, difficulties in extracting root cause, and minimal corrective actions. To complicate matters, the data heterogeneity has intensely increased, and it is produced in a fast manner making it unmanageable if the traditional methods of analytics are applied. Hence, we propose the use of a dashboard that can summarize the operational events using real-time data based on the data visualization approach. This proposed solution summarizes the raw data, which allows the user to make informed decisions that can give a positive impact on business performance. An interactive intelligent dashboard for SME (iid-SME) is developed to tackle issues such as measurement of cases completed, the duration of time needed to solve a case, the individual performance of handling cases and other tasks as a proof of concept. From the result, the implementation of the iid-SME approach simplifies the conveyance of the message and helps the SME personnel to make decisions. With the positive feedback obtained, it is envisaged that such a solution can be further employed for SME improvement for better profit and decision making.
Leading enterprise-scale big data business outcomesGuy Pearce
A talk specially prepared for McMaster University. There is more benefit to thinking about big data as a paradigm rather than as a technology, as it helps shape these projects in the context of resolving some of the enterprise's greatest challenges, including its competitive positioning. This approach integrates the operating model, the business model and the strategy in the solution, which improves the ability of the project to actually deliver its intended value. I support this position with a case study that created audited financial value for a major global bank.
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
Data Quality Management: Cleaner Data, Better Reportingaccenture
In this new Accenture Finance & Risk presentation we explore a process to investigate, prioritize and resolve data quality issues, key to creating a more efficient and accurate reporting environment. View our presentation to learn more.
For more on regulatory reporting, see presentation on Financial Reporting Robotics: http://bit.ly/2qaLK9y
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
Data and the enterprise mission: putting data at the corecorfinancial
Data matters to Financial Services firms. It is their stock-in-trade, a strategic asset that without an accurate and timely data set they cannot operate effectively, they cannot price risk fully and their capital allocation calls are unlikely to be optimal. Data is the ultimate collateral of these firms. For many, it requires a transformational change in their systems, technology and processes How then do you embed strategic data into your enterprise architecture?
Read 2 minute guide
Governance and Architecture in Data IntegrationAnalytiX DS
AnalytiX™ Mapping Manager™ provides this discipline and rigor through its dedicated data mapping methodology as well as its metadata management processes and powerful patented mapping technology. AnalytiX™ Mapping Manager™ was designed and developed to not only fill the gap of having the ability to manage and version mapping specifications, but to also streamline and improve current process and drive standards around the entire process and across the enterprise for all integration and governance processes.
James J Okarimia
Managing Partner
Aligning Finance, Risk and Data Analytics in Meeting the Requirements of Emerging Regulations
Banks must meet more (and more varied) regulations today than ever. The sheer scale and scope of banking regulations, including Dodd-Frank, Basel III and IFRS, pose challenges to all financial institutions, from the smallest bank to the largest financial services enterprise.
A Summary of Top 28 areas covered by EC Proposed Regulation for CRR, CRD IV and Basel III Regulatory Compliance and Implementation of the proposal: A publication by James Jeffrey Okarimia
Partner at RM associates: Partners in Enterprise Risk Managements
James J Okarimia
Managing Partner
Aligning Finance, Risk and Data Analytics in Meeting the Requirements of Emerging Regulations
Banks must meet more (and more varied) regulations today than ever. The sheer scale and scope of banking regulations, including Dodd-Frank, Basel III and IFRS, pose challenges to all financial institutions, from the smallest bank to the largest financial services enterprise.
James J Okarimia
Managing Partner
Aligning Finance, Risk and Data Analytics in Meeting the Requirements of Emerging Regulations
Banks must meet more (and more varied) regulations today than ever. The sheer scale and scope of banking regulations, including Dodd-Frank, Basel III and IFRS, pose challenges to all financial institutions, from the smallest bank to the largest financial services enterprise.
Big & Fast Data: The Democratization of InformationCapgemini
Moving from the Enterprise Data Warehouse to the Business Data Lake
Is it possible that ubiquitous analytics represents the next phase of the information age? New business models are emerging, enabled by big data that business leaders are eager to adopt in order to gain advantage and mitigate disruption from start-ups and parallel industries. The winners are likely to be those that master a cultural shift as well as a technology evolution.
Our view is this will be realized through the alignment of a business-centric big data strategy, combined with democratization of the analytical tools, platforms and data lakes that will enable business stakeholders to create, industrialize and integrate insights into their business processes.
Innovative approaches are needed to free up data from silos whilst encouraging both the sharing and the continuous improvement of insights across the business. While it will be evolution for some, revolution for others; the risk of status quo is not just the loss of opportunity but also a widening gap between business and the internal technology functions.
https://www.capgemini.com/thought-leadership/big-fast-data-the-democratization-of-information
The rise of data - business value and the management imperativesSheriff Shitu
Directing the attention of business managers and strategy executives away from the flood of Big Data marketing unto internal organizational factors important for the success of Data-related initiatives. Such include developing a coherent understanding of the potential of data, assessing the preparedness of the business from a capability perspective, limiting waste by starting small, and understanding the requirements for sustaining these initiatives through strategy, culture, and governance.
The report narrows in on becoming a data-driven company from three dimensions:
• Datafication of internal operations from which useful data can be generated. Such data reveals insights that can be used to save costs or optimize business operations.
• Datafication of external customer engagement and service delivery channels to ensure that sufficient data is generated from which insights about customer behaviour and preferences can be generated.
• Making necessary management changes (data governance, organizational strategy and culture) to nurture and support the adoption of sustainable data-driven initiatives.
Data Governance for EPM Systems with Oracle DRMUS-Analytics
In this training session, data governance guru Greg Briscoe explains how to deploy an enterprise data governance initiative utilizing Oracle's Data Relationship Management (DRM) application.
By aligning technology with business strategy and understanding how the organization must adapt, companies can optimize the impact of their cloud investments. Companies can use four criteria to determine where the cloud can deliver the most value.
Learn more from our Cloud resource center - http://gt-us.co/1BQYYqp
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
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Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. Mergers & Acquisitions : The
Data Dimension
A White Paper
by Dr. Walid el Abed
CEO
January 2011
Copyright Global Data Excellence 2011Copyright Global Data Excellence 2011
3. Preamble
The world is changing more quickly than ever, particularly in the area of multi-channel data flows.
The shift from the industrial age to the information age and the current financial and economical
challenges are already impacting our behavior and, as a result, we are demanding more intelligent
data to drive our actions or decisions. The survival of any enterprise will depend on its agility, which
is fundamental to ensure sustainability and growth. We strongly believe that without data
integration, data quality, and data governance, enterprise agility cannot be achieved. In a society
that is consuming more and more data, data quality and data governance become fundamental to
support value creation and enable growth. Therefore, it is imperative to define frameworks, models,
methodologies, tools, and technological platforms to ensure the right level of data compliance and
confidentiality. Although a technological platform enables information and data sharing, it is critical
to protect proprietary strategies (outside the firewall) to increase trust among all business partners.
Moreover, with improved data management, the total cost of ownership of the data can be cut still
further.
In a data-driven world, it is the enterprise’s responsibility to execute such frameworks to preserve,
improve, and sustain the global value chain and economy.
In this white paper, we will focus on mergers and acquisitions enablement through the
implementation of a data excellence framework supported by a unified platform to accelerate
delivery of the transaction’s benefits.
3Mergers & Acquisitions : The Data Dimension
The Data-Driven Approach to Enable Organizational
Flexibility
Over time, the market forces the enterprise to deal with different imperatives to remain competitive
and to continue to grow. The following list includes some of the business imperatives and goals that
will be required over the next five years to create value and adapt the business model to the future
value chain. In all cases, the delivery of just-in-time and governed data quality is a critical factor in
achieving such business goals in a rapidly changing economy.
• Introduce new products to market in 30 days
• Divest a company in six weeks
• Merge a newly acquired company in eight weeks
• Reach operational excellence in one year
• Transform the business in two years
• Reduce the number of applications by 50 percent
• Decrease the number of Enterprise Resource Planning (ERP) systems by 75 percent
• Reduce the number of vendors by 60 percent
Furthermore, as the enterprise moves from a reactive strategy focused on implementation to a
proactive strategy focused on sustainability and leverage, some “what if” scenarios naturally arise,
such as the following examples:
The definition of a
data strategy
must be
coordinated and
owned by a data
management
team that
operates as a
business cross-
functional team.
4. • Acquisition of a new business
• Divestment of an existing business (in whole or in part)
• Internal reorganization
»» Creation of a new global/regional business
»» Decomposition of an existing global/regional business
»» Consolidation of sales forces
»» Move of production from one factory to another
»» Development due to external factors such as legal and fiscal change
• Scale of shared service implementation
To execute these business objectives, a comprehensive global data strategy is critical and must be
part of the overall enterprise strategy. The definition of a data strategy must be coordinated and
owned by a data management team that operates as a business cross-functional team. To ensure
business sustainability and growth, the enterprise information system needs to be able to support
data process changes in a focused, fast, and flexible manner (the front end) and must include the
swift re-alignment of data and data hierarchies to reflect internally or externally driven changes to
the business context. Also the introduction and scale of shared services will need to be enabled and
even accelerated to provide a slim, cost-efficient, and service-driven back end. Business
modernization, operational efficiency, and successful mergers and acquisitions all rely on accessible,
timely, high quality data.
The objectives of this white paper are to highlight the different data considerations for the
acquisition of a new business. It illustrates the data strategy, framework, methodology, and platform
necessary to:
4
Give visibility of the impact of organizational changes on the enterprises
systems and processes in order to manage the changes associated with an
acquisition.
Enable the enterprise to smoothly integrate business changes for each change
scenario
Leverage the enterprise information system as an enabler for business
flexibility and shared services implementation
Mergers and acquisitions involve vast and complex processes from a data
integrationperspective.Toprovideapracticalviewofthechallengesassociated
with an acquisition, we will base this work on a real-life scenario. Then we will
illustrate the challenges that the enterprise will have to face and finally will
propose a framework and best practices to enable a smoother integration of
the acquired organization.
Mergers and
acquisitions involve
vast and complex
processes from a
data integration
perspective.
Mergers & Acquisitions : The Data Dimension
MergersandAcquisitions’High-LevelConsiderations
In this section, we will highlight some key parameters and questions that need to be considered and
answered before signing the contract. These decisive points may highly influence the business case.
5. 5
• What similarities exist in terms of scale, timing, and business model?
»» Size, geography, and existing landscape
»» Early notice gives improved planning opportunity
• What are the drivers influencing early versus late integration?
»» Business benefits versus political factors, change management
»» Early savings on infrastructure/license agreements
• What needs to be done in the first 100 days?
»» Early financials consolidation
»» Back-office implementation as shared services
• What will be the chosen integration approach?
»» Adopt versus adapt strategy
»» Timing constraint
The effort and focus of these high-level considerations depend on timescales of execution that can
vary from three months to three years.
The complexity of integrating a new acquisition, as illustrated later in the example, applies to any
business. From a data perspective, any organization (financial services, manufacturing, or
government, for example) faces the same challenges when integrating a new acquisition. The
integration of the acquired business data must take into account legal and financial constraints of
the acquirer. The acquisition must also contain the acquirer’s best practices, data standards, and
business rules to ensure that data quality key performance indicators (KPIs) thresholds are achieved
before integration to guarantee business continuity and reduce business risk.
TheBusinessExample
A multinational with $25 billion revenue decided to grow an emerging business by acquiring a
leading company with $3 billion revenue based on a mix of product and services. The acquisition is
consistent with the business’s objective to become the worldwide leading company in its particular
market.
Mergers & Acquisitions : The Data Dimension
The Context
The acquired company has more than 50 different products and services to integrate into the
acquirer’s systems and processes. The products and services are spread throughout 30 countries
and supplied by three factories with a number of co-manufacturers and diverse business partners.
The contract states that the acquirer is only allowed to use the brands of the acquired company for
12 months after acquisition, which implies that all acquired products and services have to be
rebranded or discontinued within one year after signature. Such contract terms result in additional
business complexity and tight timelines.
6. 6
The acquiring and acquired companies have different business processes, different financial and
legal constraints, and different data management practices:
• The initial assessments reveal that data quality is poor—duplicate and obsolete records plus
data inconsistency—and there is no formalization of best practices. In addition, no common
data integration platform exists.
• A complex order to cash and financial process is not harmonized across the different
countries.
• A very complex business model involves multiple layers of business partners.
• Each unit has its own master data systems using local languages in different countries. There
are no common data standards or common approaches to sharing data.
Moreover, there are specific legal requirements for some countries to consider. Finally, the resource
constraint adds to the complexity due to conflicting planning e.g. technical resources are already
fully occupied with other migration activities (in this example) and business resources will not be
available to validate the migrated data.
This real world business acquisition example should be kept in mind as the framework for
implementation is introduced.
The Planning Overview
A high-level planning overview shows the volumes of master data to be integrated over a project
timeline.
Figure 1. High level Company Integration project timeline.
Mergers & Acquisitions : The Data Dimension
7. 7
Potential impacts and issues to be considered
during the Data Integration Process
Data is generally the forgotten dimension in acquisitions; once the
acquisition contract is signed, it is assumed that data integration will
happen as if by magic. The business people assume that the IT people will
manage the data conversion from the acquired company to the current
systems. IT assumes that the business knows and understands the data-
related business rules that need to be part of the conversion process.
Figure 2 highlights some potential business impacts due to data not
correctly converted according to the appropriate business rules.
Rule Scope DQ
Dimensions
KPI Business
impact
Business
benefits
All banks
must have
the ISO
country code
in the fifth
and sixth
digits of the
swift code
All active
bank records
Accuracy 86% 14% could
equate to
millions of
dollars in
delayed
payments
Flawless
execution
(agility)
Increase trust
Any transaction
requiring a
system swift
code will fail.
Rework cost,
Days Sales
Outstanding
cash flow, bad
debt provision
All
transactions
between
countries
must include
a transfer
pricing code
within the
record
All transfer
pricing
records
Complete- ness 86% 14% of
transfer
pricing
records
cannot be
processed
Incorrect
transfer
pricing
leading to
incorrect cost
of goods,
leading to
incorrect
taxes
Inaccurate
financial
reporting
The step-by-step
approach of the
data excellence
framework is
critical for the
successful
integration of the
acquired company
to fully leverage
the
expected value of
the acquisition.
Mergers & Acquisitions : The Data Dimension
8. 8
Rule Scope DQ
Dimensions
KPI Business
impact
Business
benefits
Finished
good
materials
with the
same
barcode
printed on
the case
must have
the same
parent
product code
All active
finished
products
records
Accuracy 90,9% 9.1% Finished
goods
records are
wrongly
classified for
revenue/cost
calculations
Reallocation of
revenue to
correct bucket
Improved
decision
making
Table 1. Business Rules, Data Quality Dimensions, Business Benefits
For each area of the business, key business rules need to be identified and applied to avoid any
potential business impact during data conversion. A very structured methodology and
framework must be followed to ensure that all critical business rules in all functional areas are
considered while the data is converted. Data integration is critical to the success of the
acquisition, and if the data is not consistent with the business rules, achieving the business case
of any acquisition may be compromised.
TheDataExcellenceFramework
The data excellence framework encompasses comprehensive common processes and best
practices with a common methodology understood by the enterprise at all levels. The long-term
vision of the data excellence framework is to enable the enterprise to shift to a new paradigm:
“Data quality culture is embedded as second nature” across the organization for business
success.
The step-by-step approach of the data excellence framework is critical for the successful
integration of the acquired company to fully leverage the expected value of the acquisition. The
framework’s mission is to enable the enterprise to be trusted, intelligent, and agile by using the
highest levels of data quality to empower its business partners, employees, and stakeholders.
This business-rules driven framework ensures successful and optimal data integration through
the delivery of data quality KPI metrics, the management of the exceptions, and the inclusion of
the appropriate threshold that allows all business processes to execute flawlessly after the
integration.
Mergers & Acquisitions : The Data Dimension
9. 9
The data excellence framework defines the business rules as rules that data should comply with to
execute business processes properly. Each business rule is associated with one of the following
dimensions:
• Accuracy
• Completeness
• Consistency
• Non Duplicate
• Non Obsolete
For example, data consistency is a business rule typically owned by the CIO because he or she needs
to ensure that all data for reporting and operational purposes is consistent across the systems. It is
crucial to adopt a practical approach to data quality and to focus on the critical business rules rather
than seeking 100 percent quality, which may not be achievable within the acquisition project
timelines. In a traditional data conversion/data integration initiative, an optimal level of data quality
needs to be targeted to avoid delays in the acquisition integration. Figure 2 depicts a realistic view
of the perceived effort between data integration effort and data quality effort in the data
management life cycle as part of an acquisition process.
Figure 2. Perceived effort associated with Data Governance over time
In a traditional data
conversion/data
integration initiative,
an optimal level of
data quality needs to
be targeted to avoid
delays in
the acquisition
integration.
Mergers & Acquisitions : The Data Dimension
A key component of the data excellence framework is to provide a simple and straightforward
process to govern data quality business rules, to show the results to enable corrections, and to link
the data quality KPIs to the business impact for optimal prioritization (figure 3).
10. 10
Figure 3. Data Governance process linked to business KPI’s
In addition, the data excellence framework defines the organizational roles needed during the
conversion. The roles necessary for effective data governance achieved through data Stewardship
include the data accountable, the data steward, and the data responsible roles supported by a data
excellence group, enabled by a data excellence framework (see Figure 4).
Figure 4 : Roles and responsibilities within the Data
Excellence Framework
The roles necessary
for effective
data governance
achieved through
data Stewardship
include the data
accountable, the
data steward,
and the data
responsible roles.
Mergers & Acquisitions : The Data Dimension
11. 11
Who Cares Most?
The willingness and commitment of senior management is critical to the acquisition success. To get
their buy-in, be sure to highlight the business impact and the benefits of process and business rules
ultimately owned by them.
• CFO (risk, compliance, reporting)
»» Reliable controls and reporting based on trusted data and transparency
• COO (operational efficiency, flawless execution)
»» Agility and operational efficiency based on trusted intelligence
• CIO (cost effectiveness, business enabler)
»» Cost effectiveness based on foundation services
»» Enabler for the business to achieve its goals
Attributes of the Data Excellence Framework
The framework facilitates the execution of multiple data integration projects within the same
timescales —for example, parallel projects in different legal entities of the acquired organization.
The following characteristics are key for a comprehensive framework enabling prompt data
integration of a new acquisition.
• Harmonized business and data management processes
• Standardized data structures, based wherever possible on industry standards (e.g., ISO
country codes)
• Defined best practices
• A standardized data integration platform
• Global agreements with solution providers
• Increasing level of ownership of data and best practices in business functions
• A comprehensive toolkit for managing change
»» Program governance
»» Data conversion solution team
»» Change management process
»» Implementation methodology
The Standard Data Conversion Process
This section will describe a high-level approach to accelerate and guarantee successful data
integration. The methodology, supported by a comprehensive data integration platform, represents
a real competitive advantage, ensures successful and rapid acquisition integration, and enables the
enterprise to “industrialize” the integration and be fully equipped to face any business imperatives
required in any organizational change.
Mergers & Acquisitions : The Data Dimension
12. 12
Here are the standard steps in data conversion:
• Identify the data sources.
• Define the data objects that are in scope for data conversion: The term “data objects”
describes all records that fulfill the same purpose (e.g., customer, supplier, and material
records).
• Prepare the data object structure (DOS): The DOS gathers and organizes information from
the target application point of view and indicates how the target application will use the data
object. There is one DOS per data object.
• Perform the data mapping: Identifying a source for each field present in the DOS.
• Design the transformation: Logic that will allow modifying or deriving a target data field value
from the mapped data field value is based on business and validation rules.
• Test the process.
• Execute and load.
The next two figures illustrate a high-level, end-to-end data conversion process.
Figure 5. Data Conversion Flow
Mergers & Acquisitions : The Data Dimension
13. 13
Figure 6. Data conversion process
ThePlatform
A technical platform (see Figure 7) is critical to accelerate and support the data excellence
framework. This platform must be comprehensive and scalable to support every stage of the data
life cycle where the data excellence framework applies. The platform has also to support multiple
parallel project implementations as follows:
• During the Data Preparation where the platform supports the execution of the framework
against the legacy repository to assess the quality of the data ready to be migrated
• During the Data Conversion phase where the platform supports the execution of the
framework as part of the data validation process before the load in the target repository
• During the Sustain phase where data in the new repository is regularly assessed
• During the Real-Time Entry phase as part of a data capture tool
Figure 7. Technical Platform
A technical platform
(see Figure 7) is
critical to accelerate
and support the
data excellence
framework.
Mergers & Acquisitions : The Data Dimension
14. Conclusions
To measure the success of mergers and acquisitions transactions, it is imperative to set up clear
criteria related to the data dimension. Otherwise, the business case will be incomplete.
In our business example, the acquisition was successful. The integration of the newly acquired
business was achieved within the timelines and with the required data quality levels:
• All the products and services within the project scope were integrated into the new business
processes and systems.
• Units in all countries have gone live and business is following the acquirer’s process, systems,
and best practices.
• All products and services were rebranded.
• Data quality thresholds were achieved.
The integration of the acquired enterprise was only possible thanks to the detailed data excellence
framework and best practices enabled by a comprehensive, robust, and flexible technological
platform and data governance model involving the business and IT organizations.
Our view for the future value chain is that any business strategy will only succeed if it includes a
comprehensive data strategy that can address all business imperatives. A data strategy enabled by
the appropriate frameworks and technology platform will cultivate a “first time right” culture,
resulting in value creation beyond departmental or business unit boundaries.
High-data quality is vital to an organization’s sustainability and growth. Trusted data represents a
real competitive advantage. It is time to manage data as a company asset.
The cultural change is critical for any merger or acquisition. A tremendous effort will be required in
this area to ensure a successful change management. Therefore, introducing and executing a
proven data excellence framework supported by a robust platform is a mandatory accelerator to
deliver an efficient data integration and to support a business driven data governance while the
organization focuses on the cultural challenge.
14 Mergers & Acquisitions : The Data Dimension