The document describes how a company implemented a modern data management approach to support a multi-billion dollar merger between two large food service companies. They consolidated master data from both companies' systems in 5 months to support business goals. After the merger was blocked, Company A incorporated the customer segmentation and category management applications into their strategies, and realized rationalization benefits from the advanced MDM platform. Company B is re-evaluating their MDM strategy without the merger.

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2. Abstract
MDM for a single view of customer, products and other entities. But how can IT teams
cope with big data sources and increasing volume, variety and velocity, while
managing veracity. More critically, how can frontline business teams get the analytics
and data‐driven applications they need to achieve their goals. This lunchtime session
will describe how all this was achieved as part of a multi‐billion dollar merger, and
some of the major and side benefits from the project

3. Going Beyond Traditional MDM to Deliver the
next generation of Modern Data Management
Neil Cowburn, CEO
Robert Quinn, Practice Lead
October 5th, 2015

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Agenda
1. About iMiDiA
2. The Challenges of M&A
3. Case Study
4. Modern Data Management

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About iMiDiA
iMiDiA is a unique services and solutions company. We take the core
principles of Enterprise Data Management, integrate Cloud computing,
and move organizations to the next generation of Modern Data
Management.
During the previous 18 months iMiDiA has proven the benefits of Modern
Data Management during the implementation of a multi-billion dollar
merger and today we will highlight how organizations can benefit from
this approach.

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Challenges of M&A – M&A Operations in the ‘Dark Ages’
In this era of “business agility” and “enablement” M&A operates as it
has for last 20 years—especially when it comes to the technology.
 People are still doing things the way they’ve always done and are
comfortable with…perhaps it’s how they originally established their
expertise in this area.
 To that end, they’re still using legacy systems that can’t do what
we’d want them to… the technologies that would do everything we
wanted didn't exist before.
 As a result of not changing with the times, they’re bogged down by
the same old issues; complexity, high cost, and inefficiency.
 M&A still relies on the use of spreadsheets that are used by tens or
even hundreds of accountants.

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Challenges of M&A – Key Business Challenges & Impediments
PEOPLE
Who survives
post-merger?
“Change” is not easily
received or achieved.
PROCESS
What are the agreed
common processes?
What about our
‘secret sauce?’
How can we be more
efficient now
and later?
TECHNOLOGY
Which technology is
better?
Data must be kept
separate
for competitive and
legal reasons.
Combined assets
must be available
post-merger.

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Challenges of M&A – Technology Challenges
 Using spreadsheets actually blocks progress before, during and
especially after the merger.
 Information gathered to justify the merger is difficult to leverage
and validate
 The effort to collect the information is substantial and costly
It is time to consider a modern approach, which enables you to
leverage and obtain additional value from data.
 Today’s technology allows you to;
 Reduce collection costs, track changes and better control access
 Repurpose pre-merger analysis for post-merger competitive
advantage
 Increase the volume and variety of the data analyzed

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▪ Sales quota and territory realignment
▪ Product Substitution and Category Management
▪ Vendor spend and contract re-negotiations
▪ Redundant distribution centers, warehouses and office space
▪ Post-merger consolidated reporting and historical comparisons
▪ Master Data scope; Entity overlap and cross-entity relationships:
- Customers and Accounts
- Products and Services
- Locations
- Vendors and Contracts
Challenges of M&A – Business Linked Data Challenges

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PEOPLE
Complete re-organization
from top to bottom takes
several years during
which time competitors
take advantage of
the chaos
PROCESS
Attempting to
standardize on a common
business process during a
merger is impossible with
all the change about to
take place
TECHNOLOGY
CIO/CTO request the
build out of technology
roadmap with tendency
to expand current costly
investments.
LEGAL
Everyone has to wait
for legal approval
to start Post Merger
Integration.
Typical Approach to M&A
PRE-MERGER POST MERGER

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PEOPLE
Leverage both
organizations resources as
much as possible. Partner
highly experienced data
management professionals
with in-house business
and IT experts.
PROCESS
Follow the principle of
Best of Both. Harmonize
core repeatable
processes. Build ‘secret
sauce’ into data driven
applications to eliminate
competition.
TECHNOLOGY
Data driven approach uses
the merger a catalyst for a
new innovative technology
layer. Data driven
applications utilize the data
that supported the merger
benefits case.
LEGAL
Addressed via Cloud
and start Pre-Merger
integration.
A New Data Driven Approach
PRE-MERGER POST MERGER

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Case Study
Background: In December 2013, the two largest Food Service
companies in the US announced plans to merge. The combined
company would be expected to have sales of $65bn. Annual synergies
of $600m would be achieved.
The Challenge: iMiDiA was charged with integrating both companies’
data management strategies, data and technologies as quickly as
possible in support of the synergy targets.

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Project Goals
Ingest and process master data from the current disparate
applications at the two companies
Duplicate detection of master data within the two companies
Consolidated and efficient management of master data
post merger in a single company scenario
Support enrichment to enable the combined business
teams to get more value out of the master data
Syndicate master data to operational applications and
analytic platforms
Support a single point of governance

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M&A Case Study - Timeline
PRE-DAY 1
Start Data Consolidation
5 months (Day 60)
Customer Segmentation
3 months (Day 30)
Customer Matching
7 months (Day 90)
Product Matching
Category Management
POST MERGER
Day X – EDW
Day X –Single MDM/DG
8 months (Day 100+)
Integration to EDW

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1
2
3
4
CONSOLIDATE
Pre Day 1 – Objective: Accelerate & maximize data support of value capture
Combine data for Customer, Item, Location and Vendor to support Day 1 Value Capture
ENRICH
Pre Day 1 – Objective: Accelerate & maximize data support of value capture
Integrate CHD for Customer, and Lotting attributes for Item
SINGLE POINT OF GOVERNANCE
Post Day 1 – Objective: Eliminates redundancies in processes & data entry
Rollout rationalized workflow and process for master data element creation and maintenance
SINGLE MDM
Post Day 1 – Objective: Rationalize system landscape
Consolidate MDM technologies based on harmonized processes
Steps 1 and 2 targeted pre-merger, steps 3 and 4 target post-merger
MDM consolidates Customer, Item, Location and Vendor; including
enrichment (3rd party attributes for Customer, Categorization for Item)
Data Driven Approach
The approach for MDM closes the identified gaps across both companies.

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Pre-Day 1 – MDM Solution Implementation

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Post-Day 1 – MDM Solution Implementation

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A set of data management principles that align to and support Business
objectives
Principles drive decision making (resource allocation, architecture,
technology choices)
Support the changes required by merger while;
 Building new competitive capabilities available post-merger
 Simplifying the technology landscape
Modern Data Management – Business Alignment

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Modern Data Management – Business Alignment
Data Management prioritized to support strategic business
objectives
Today’s Challenges
Missing Organization Insight - No clear solution to
reflect different business views of data across the
organization and data domains
Increased Costs with Duplicate Systems – Multiple
overlapping technologies with inconsistent data exist
across the organization
Decentralized Data – Multiple entry and governance
routes with unclear accountability
Scalability Issues – No quick or clear solutions for
expanding data domains, attributes, and hierarchies
Future State Goals
Single, Comprehensive Master - Transition away
from multiple sources to a single location where
enriched data can be stored
Quality of Data - Proactively detect and correct data
and improve the to optimize the customer experience
Common Definitions - Define and manage consistent
data with different views across the organization
Common Data Provisioning - Accommodate evolving
business needs while supporting acquisitions,
provide additional data functionality, and organization
growth
Data Management Guiding Principles
Data is an Enterprise Asset
Innovation & Agility
Customer Centric Design
Open Standards
Secure
Service OrientedSingle View of Master Data
Agility Layer to build apps quicker
“Rent b4 Buy, Buy b4 Build”
Business Strategy
“Continue acquisition and international growth”

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Modern Data Management – People & Process
People
▪ Partner / Pair (combine internal resources with outside expertise)
▪ Small teams of highly experience data management resources
▪ Internal resources responsible for source system integration
▪ Internal resources develop “platform 2.0” in conjunction with
experienced system integrators
▪ Business users / data experts for “expert sourcing”
Process
▪ Surface data early and often to Business users / data experts
▪ Utilize sampling for rule refinement
▪ Develop materiality metrics to drive prioritization
▪ Utilize collaboration features to minimize meetings
▪ Leverage out of box lineage to improve confidence and debug issues
▪ Maintain “native” attributes for consistency and system co-existence
▪ Create “enterprise” attributes using DQ capabilities and support
enrichment requirements

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 Agile and Flexible
Domain independent; Flexible data model. Can support master and
transactional data as needed
 Scalable
Provides platform for rapid assimilation of future growth
 Usable
Intuitive user interface, business user governance, search, tagging
and powerful relationship visualization.
 Universal
Cross-application / vendor capability supports EA “best of breed”
principle for the near and distant future
 Innovative
SaaS, Big Data infrastructure, “batteries included”; Data Quality,
Consolidation and micro services support
Modern Data Management – Key technology features

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Modern Data Management - Summary
Merger: In June 2015, the FTC ruled against the merger. What does
this mean?
Company A: The two data driven applications for customer
segmentation and category management are being incorporated into
the companies sales and merchandising strategies. In addition MDM
application rationalization can be realized due to the advanced
capabilities of the platform.
Company B: They are re-evaluating their MDM strategy and footprint
at this time. The tenant for this company is being kept alive for future
potential use.

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Q & A
Thank you for attending
See our Whitepaper at:
https://tdwi.org/articles/2015/09/01/mergers-and-modern-data
-management.aspx