Product Data Management during Mergers and Acquisitions

Adrian Carr | April 6, 2023 | 6 minute read

See how you can turn trusted data into a competitive advantage

Get in touch

Product Data Management during Mergers and Acquisitions

Master Data Management Blog by Stibo Systems logo
| 6 minute read
April 06 2023
Product Data Management during Mergers and Acquisitions ➤
12:28

Data management during mergers is a challenge to CIOs and IT leaders. They have to manage a doubled IT infrastructure and merge product data into a single product classification system.

Merging product data is important to reap the benefits of a business merger, including brand consistency, better customer experiences and operational efficiency.

A master data management system can help merge product data via built-in data governance capabilities and enable a number of improved business outcomes by ensuring clean and consistent product data. This involves handling diverse data sources, ensuring data validation, and maintaining high data quality.

The goals and expectations of a merger and acquisition (M&A) are almost always the same: cut costs, increase efficiency, shorten time to value, automate manual processes, enhance business intelligence, etc. Ultimately, a merger is supposed to provide value to shareholders, who expect quick wins. However, history is littered with failed mergers such as Microsoft and Nokia, Google and Motorola and Time Warner and AOL. Why do mergers fail so often, or at least not succeed as easily as expected? The high expectations of an M&A seem proportionate to the pitfalls that threaten its success.

There are several challenges. In his Forbes article, Why Corporate Acquisitions Fail and How to Avoid It, Richard Kestenbaum mentions culture as the most significant: “When you buy a company and change the culture, you are making war on what has made the company successful.”

You can also add fear of layoffs, as well as the differences in systems and processes. This blog focuses on the IT challenge of managing product data during a merger.

 

 

Data management during merger is CIO/IT leader responsibility

The overall responsibility of successful data management during a merger lies with the CIO, who oversees delivering the expected synergy of unified systems, where new and improved systems are expected to disclose redundancies of processes and data and thus promise some low-hanging cost reductions.

The CIO will know to provide some realism here: opportunities, as well as problems, will occur when different data systems are combined as part of a business merger. There is a huge potential in merging databases, but there are pitfalls that lie just beneath the surface waiting to swallow both money and resources.

Merging systems and databases is a serious IT challenge that can be hard to communicate to stakeholders. CIOs rarely get credit for a successful merger, but they are likely to take the fall if the incorporation of IT systems fails or blows the budget.

Of course, the merging companies can choose to operate as separate entities with different brands and assortments. Then there will be fewer data issues, but often stakeholders are keen to see the merger make a significant impact, either as a continued brand or a new, unified brand that changes the landscape of their trade by the expansion.

Data management during mergers

Mergers means dealing with data silos and a heterogenous data landscape

From an IT perspective the typical M&A situation regarding product data will look like this:

  • Multiple sets of assortments and product data

  • Different ERP systems

  • Different warehouse systems with different SKUs

  • A variety of different apps and systems transporting and maintaining product data

Data exists in disparate systems and different formats. Mergers effectively double the infrastructure and the amount of master data you have to manage and maintain, as well as the cost of doing so. This goes for product data, but also for other data domains like customers, suppliers, employees, locations and assets.

There is a strong need to aggregate and consolidate data for the sake of efficiency, for the post-merger long run and also to provide quick wins.

You have two choices: merge your systems or connect your systems. Well, three choices, to be precise, as a combination could be the case.

 

Two approaches to data management during mergers and acquisitions:

1) Merging systems

The likelihood is, upon the merger, you’ll end up with a lot of redundant systems and master data duplication. Because of this, you will want to carry across relevant information into your primary systems before retiring the redundant ones.

As part of moving data across systems, you need to remove inconsistencies such as duplication and inaccuracies.

Once moved, you’ll have all the data you need without incurring the costs of managing a bloated infrastructure estate.

2) Connecting systems

The alternative approach of connecting systems reduces the time it takes to use information from the existing and incoming organization and then having the data fed through a master data management system. This approach leads to standardizing and converging master data and application platforms, such as ERP, CRM and supply chain, as well as eliminating duplication and complexity. This ensures consistent data sets and a single source of truth across the organization.

Which approach to data management during mergers and acquisitions should you choose?

Which approach you choose depends on the business case and the data model. In some cases, a combination of merging and connecting systems is the best solution.

The merging companies must endeavor to bring the combined product data together under a single product classification system.

This is important for establishing correct references and traceability: all products brought in from different systems must be classified in the same way. This often involves integrating various data formats and maintaining consistent naming conventions across different types of data.

Generally, the below seven steps form a best practice to achieve this approach.

 

Merge product data

 

Seven steps to successful data management during a merger

1. Set up a data management team

Set up a data team headed by the chief information officer (CIO) or chief data officer (CDO) of the acquiring company. This team should consist of people from both organizations and must have sufficient expert knowledge of IT and data issues to be able to see both opportunities and threats. The team must also have enough leverage to elevate the issues to the executive level because timely communication can be critical for the success of a merger.

The data team then agrees with the vision and objectives of the data management approach before laying out a data strategy, including a budget and a timeline.

2. Perform a data management audit

The data team performs an audit that includes all of the data stakeholders to describe the current data landscape and the future data model. The audit is required because it will give the team an idea of which business areas and departments are involved and a picture of the complexity of the data model. The audit will also offer multiple perspectives on the data merger and help with the buy-in on the project because departments are invited to participate. Questions to address during the audit are:

  • Where are product data and assets located (systems, apps, files)?

  • How does data traverse these different systems (data flows)?

  • Who owns data?

  • Who manages data?

3. Agree on a data classification system

Based on the result of the audit, the data team agrees on a product data classification system. The classification system reflects the new company’s sources, data quality, formats and hierarchies.

The classification system will form the basis of matching and linking product data and provide the guideline for how to migrate product data.

4. Choose a master data management system

It will be a lot easier and quicker to manage the data migration if you use a master data management system, which identifies, links, acquires and synchronizes product data from different sources — both internal and external. An effective data integration process is crucial here.

Depending on the complexity of the data model and the vision of the data strategy, the master data management system can be implemented in one of four different methods:

  • Consolidation. Data is owned by the legacy systems (ERPs, inventory management systems, etc.) where it stays during the merger. The master data management system then aggregates and consolidates data into a so-called golden record, where data is cleansed and synchronized. The beauty of this method is that there is no need to discontinue legacy systems, which can make merging data a quicker process.
  • Centralization. If the data model from the audit discloses a disorganized set of product data that cannot be aggregated in a meaningful way, the master data management system can take over the role as the primary platform of record, from where data is then pushed out to the operational systems (retailer systems, marketing apps, point of sale, etc.). If one of the organizations was managing product data in the form of Excel spreadsheets, then targeting a centralized approach would be worthwhile.
  • Coexistence. The master data management forms part of a two-way system that secures a single version of the truth. Whenever a siloed system is updated with new information, the master data management system will be updated, and vice versa. Regardless of where system data is pulled from, it will always be the single version of truth. Compared with the consolidation method, the coexistence method synchronizes and consolidates data, but at the same time allows the legacy systems to be fully operable.
  • Registry. Within this implementation method, the master data management system allows an organization to keep data segregated, which helps support regulatory requirements. This is especially common in the healthcare industry, where you need to comply with the standard 21 CFR Part 11.

5. Agree on data governance procedures

Across the new organization, there will now be a need for new data governance policies and procedures to secure accountability, instill accurate reporting, manage compliance and ensure transparency.

 

 

6. Plan the migration of all data

The migration of all data is the summit of your efforts and preparations. It should include a methodology check as well as a capability check for hardware, software and resources. The migration requires a data security plan to avoid data breaches and a recovery plan to restore the previous state; and finally, it should include a go-live plan that describes who and when to do what. Most importantly, test and then test again until the number of errors is acceptable – because you will most likely not eliminate them all.

7. Use a master data management system to operate the process going forward

The master data management system will prove its worth in the transition phase. However, going forward it will continue to be a valuable tool as it deduplicates data and helps control how records are created, updated and approved via configurable workflows.

You can use the master data management system to ensure an efficient onboarding of supplier data and distribute master data to all stakeholders. The organization will continue to benefit from the removal of data barriers between departments and partners.

The firm grip of master data that is secured by a master data management system will even make it easier to maintain regulatory compliance and data governance through clear audit trails and record histories.

Main points of managing product data during mergers

Data management during mergers and acquisitions pose a challenge to the IT leadership. A business merger resulting in a doubled IT infrastructure will inevitably exacerbate any existing data silo problem. It’s paramount to merge data as well.

Master data management can help unify product data and also serve as a valuable tool in the post-merger era, where it will enable you to reap the benefits of the merger in the long run.

Unifying product data helps Rensa Family, a leading wholesaler for the ventilation, heating, and sanitary industry in the Netherlands, to collaborate across brands.


Master Data Management Blog by Stibo Systems logo

Adrian Carr is the CEO of Stibo Systems. For more than 35 years he has been passionate about empowering companies with data transparency to improve the customer experience, drive innovation and growth and create an essential foundation for digital transformation.

Discover Blogs by Topic

  • MDM strategy
  • Retail and distribution
  • Data governance
  • See more
  • Customer and party data
  • Manufacturing
  • Product data and PIM
  • Data quality
  • AI and machine learning
  • Supplier data
  • CPG
  • Financial services
  • Sustainability
  • GDPR
  • Location data
  • PDX Syndication
  • Customer Experience
  • Product Experience Data Cloud
  • Cloud
  • Microsoft Azure
  • Product Onboarding

Gen Z: Seeking Excitement Beyond Amazon

12/11/24

A Modern Guide to Data Quality Monitoring: Best Practices

12/10/24

CDP and MDM: Complementary Forces for Enhancing Customer Experiences

12/10/24

Using Machine Learning and MDM CBAM for Sustainability Compliance

12/3/24

How to Implement Master Data Management: Steps and Challenges

11/26/24

AAPEX and SEMA: The Automotive Aftermarket Industry’s Mega-Showcase

11/25/24

5 Key Trends in Product Experience Management

11/20/24

Building the Future of Construction with AI and MDM

11/19/24

Solving Retail Data Fragmentation: The Key to Consistent Customer Journeys

11/14/24

Live Shopping: How to Leverage Product Information for Maximum Impact

10/22/24

Why Data Accuracy Matters for CPG Brands

10/16/24

Why Choose a Cloud-Based Data Solution: On-Premise vs. Cloud

10/15/24

How to Use Customer Data Modeling

10/10/24

Navigating Change: Engaging Business Users in Successful Change Management

9/20/24

What is Digital Asset Management?

9/11/24

How to Improve Your Data Management

9/3/24

The Future of Master Data Management: Trends in 2023-2025

9/1/24

Digital Transformation in the CPG Industry

8/30/24

5 CPG Industry Trends and Opportunities for 2024-2025

8/29/24

What is the difference between CPG and FMCG?

8/27/24

Responsible AI relies on data governance

8/27/24

6 Features of an Effective Master Data Management Solution

8/15/24

Great Data Minds: The Unsung Heros Behind Effective Data Management

8/13/24

A Data Monetization Strategy - Get More Value from Your Master Data

8/6/24

Introducing the Master Data Management Maturity Model

8/4/24

What is Augmented Data Management? (ADM)

7/31/24

Data Migration to SAP S/4HANA ERP - The Fast and Safe Approach with MDM

7/30/24

GDPR Data Governance and Data Protection, a Match Made in Heaven?

7/17/24

The Difference Between Master Data and Metadata

5/26/24

Master Data Management Roles and Responsibilities

5/20/24

8 Best Practices for Customer Master Data Management

5/16/24

What Is Master Data Governance – And Why Do You Need It?

5/12/24

Guide: Deliver flawless rich content experiences with master data governance

4/11/24

Risks of Using LLMs in Your Business – What Does OWASP Have to Say?

4/10/24

Guide: How to comply with industry standards using master data governance

4/9/24

Digital Product Passports - A Data Management Challenge

4/8/24

Guide: Get enterprise data enrichment right with master data governance

4/2/24

Guide: Getting enterprise data modelling right with master data governance

4/2/24

Guide: Improving your data quality with master data governance

4/2/24

Data Governance Trends 2024

1/30/24

NRF 2024 Recap: In the AI era, better data can make all the difference

1/19/24

Building Supply Chain Resilience: Strategies & Examples

12/19/23

How Master Data Management Can Enhance Your ERP Solution

12/14/23

Shedding Light on Climate Accountability and Traceability in Retail

11/29/23

What is Smart Manufacturing and Why Does it Matter?

10/11/23

Future Proof Your Retail Business with Composable Commerce

10/9/23

5 Common Reasons Why Manufacturers Fail at Digital Transformation

10/5/23

How to Digitally Transform a Restaurant Chain

9/29/23

Three Benefits of Moving to Headless Commerce and the Role of a Modern PIM

9/14/23

12 Steps to a Successful Omnichannel and Unified Commerce

7/6/23

CGF Global Summit 2023: Unlock Sustainable Growth With Collaboration and Innovation

7/5/23

Navigating the Current Challenges of Supply Chain Management

6/28/23

Product Data Management during Mergers and Acquisitions

4/6/23

A Complete Master Data Management Glossary

3/14/23

4 Ways to Reduce Ecommerce Returns

3/8/23

Asset Data Governance is Central for Asset Management

3/1/23

4 Common Master Data Management Implementation Styles

2/21/23

How to Leverage Internet of Things with Master Data Management

2/14/23

Manufacturing Trends and Insights in 2023-2025

2/14/23

Sustainability in Retail Needs Governed Data

2/13/23

NRF 2023: Retail Turns to AI and Automation to Increase Efficiencies

1/20/23

5 Key Manufacturing Challenges in 2023

1/16/23

What is a Golden Customer Record in Master Data Management?

1/9/23

Innovation in Retail

1/4/23

Life Cycle Assessment Scoring for Food Products

11/21/22

Retail of the Future

11/14/22

Omnichannel Strategies for Retail

11/7/22

Hyper-Personalized Customer Experiences Need Multidomain MDM

11/5/22

What is Omnichannel Retailing and What is the Role of Data Management?

10/25/22

Most Common ISO Standards in the Manufacturing Industry

10/18/22

How to Get Started with Master Data Management: 5 Steps to Consider

10/17/22

What is Supply Chain Analytics and Why It's Important

10/12/22

What is Data Quality and Why It's Important

10/12/22

An Introductory Guide: What is Data Intelligence?

10/1/22

Revolutionizing Manufacturing: 5 Must-Have SaaS Systems for Success

9/15/22

An Introductory Guide to Supplier Compliance

9/7/22

What is Application Data Management and How Does It Differ From MDM?

8/29/22

Digital Transformation in the Manufacturing Industry

8/25/22

Master Data Management Framework: Get Set for Success

8/17/22

Discover the Value of Your Data: Master Data Management KPIs & Metrics

8/15/22

Supplier Self-Service: Everything You Need to Know

6/15/22

Omnichannel vs. Multichannel: What’s the Difference?

6/14/22

Create a Culture of Data Transparency - Begin with a Solid Foundation

6/10/22

The 5 Biggest Retail Trends for 2023-2025

5/31/22

What is a Location Intelligence?

5/31/22

Omnichannel Customer Experience: The Ultimate Guide

5/30/22

Location Analytics – All You Need to Know

5/26/22

Omnichannel Commerce: Creating a Seamless Shopping Experience

5/24/22

Top 4 Data Management Trends in the Insurance Industry

5/11/22

What is Supply Chain Visibility and Why It's Important

5/1/22

The Ultimate Guide to Data Transparency

4/21/22

How Manufacturers Can Shift to Product as a Service Offerings

4/20/22

How to Check Your Enterprise Data Foundation

4/16/22

An Introductory Guide to Manufacturing Compliance

4/14/22

Multidomain MDM vs. Multiple Domain MDM

3/31/22

Making Master Data Accessible: What is Data as a Service (DaaS)?

3/29/22

How to Build a Successful Data Governance Strategy

3/23/22

What is Unified Commerce? Key Advantages & Best Practices

3/22/22

How to Choose the Right Data Quality Tool?

3/22/22

What is a data domain? Meaning & examples

3/21/22

6 Best Practices for Data Governance

3/17/22

5 Advantages of a Master Data Management System

3/16/22

A Unified Customer View: What Is It and Why You Need It

3/9/22

Supply Chain Challenges in the CPG Industry

2/24/22

The Best Data Governance Tools You Need to Know About

2/17/22

Top 5 Most Common Data Quality Issues

2/14/22

What Is Synthetic Data and Why It Needs Master Data Management

2/10/22

What is Cloud Master Data Management?

2/8/22

How to Implement Data Governance

2/7/22

Build vs. Buy Master Data Management Software

1/28/22

Why is Data Governance Important?

1/27/22

Five Reasons Your Data Governance Initiative Could Fail

1/24/22

How to Turn Your Data Silos Into Zones of Insight

1/21/22

How to Improve Supplier Experience Management

1/16/22

​​How to Improve Supplier Onboarding

1/16/22

How to Enable a Single Source of Truth with Master Data Management

1/13/22

What is a Data Quality Framework?

1/11/22

How to Measure the ROI of Master Data Management

1/11/22

What is Manufacturing-as-a-Service (MaaS)?

1/7/22

The Ultimate Guide to Building a Data Governance Framework

1/4/22

Master Data Management Tools - and Why You Need Them

12/20/21

The Dynamic Duo of Data Security and Data Governance

12/20/21

How to Choose the Right Supplier Management Solution

12/20/21

How Data Transparency Enables Sustainable Retailing

12/6/21

What is Supplier Performance Management?

12/1/21

What is Party Data? All You Need to Know About Party Data Management

11/28/21

What is Data Compliance? An Introductory Guide

11/18/21

How to Create a Marketing Center of Excellence

11/14/21

The Complete Guide: How to Get a 360° Customer View

11/7/21

How Location Data Adds Value to Master Data Projects

10/29/21

How Marketers Should Prepare for the 2023 Holiday Shopping Season

10/26/21

What is Supplier Lifecycle Management?

10/19/21

What is a Data Mesh? A Simple Introduction

10/15/21

How to Build a Master Data Management Strategy

9/26/21

10 Signs You Need a Master Data Management Platform

9/2/21

What Vendor Data Is and Why It Matters to Manufacturers

8/31/21

3 Reasons High-Quality Supplier Data Can Benefit Any Organization

8/25/21

4 Trends in the Automotive Industry

8/11/21

What is Reference Data and Reference Data Management?

8/9/21

What Obstacles Are Impacting the Global Retail Recovery?

8/2/21

GDPR as a Catalyst for Effective Data Governance

7/25/21

All You Need to Know About Supplier Information Management

7/21/21

5 Tips for Driving a Centralized Data Management Strategy

7/3/21

Welcome to the Decade of Transparency

5/26/21

How to Become a Customer-Obsessed Brand

5/12/21

How to Create a Master Data Management Roadmap in Five Steps

4/27/21

What is a Data Catalog? Definition and Benefits

4/13/21

How to Improve the Retail Customer Experience with Data Management

4/8/21

How to Choose the Right Master Data Management Solution

3/29/21

Business Intelligence and Analytics: What's the Difference?

3/25/21

Spending too much on Big Data? Try Small Data and MDM

3/24/21

What is a Data Lake? Everything You Need to Know

3/21/21

How to Extract More Value from Your Data

3/17/21

Are you making decisions based on bad HCO/HCP information?

2/24/21

Why Master Data Cleansing is Important to CPG Brands

1/20/21

CRM 2.0 – It All Starts With Master Data Management

12/19/20

5 Trends in Telecom that Rely on Transparency of Master Data

12/15/20

10 Data Management Trends in Financial Services

11/19/20

Seasonal Marketing Campaigns: What Is It and Why Is It Important?

11/8/20

What Is a Data Fabric and Why Do You Need It?

10/29/20

Transparent Product Information in Pharmaceutical Manufacturing

10/14/20

How to Improve Back-End Systems Using Master Data Management

9/19/20

8 Benefits of Transparent Product Information for Medical Devices

9/1/20

How Retailers Can Increase Online Sales in 2023

8/23/20

Master Data Management (MDM) & Big Data

8/14/20

Key Benefits of Knowing Your Customers

8/9/20

Women in Master Data: Kelly Amavisca, Ferguson

8/5/20

Customer Data in Corporate Banking Reveal New Opportunities

7/21/20

How to Analyze Customer Data With Customer Master Data Management

7/21/20

How to Improve Your 2023 Black Friday Sales in 5 Steps

7/18/20

4 Ways Product Information Management (PIM) Improves the Customer Experience

7/18/20

How to Estimate the ROI of Your Customer Data

7/1/20

Women in Master Data: Rebecca Chamberlain, M&S

6/24/20

How to Personalise Insurance Solutions with MDM

6/17/20

How to Democratize Your Data

6/3/20

How to Get Buy-In for a Master Data Management Solution

5/25/20

How CPG Brands Manage the Impact of Covid-19 in a Post-Pandemic World

5/18/20

5 Steps to Improve Your Data Syndication

5/7/20

Marketing Data Quality: Why Is It Important and How to Get Started

3/26/20

Panic Buying: Navigating Long-term Implications and Uncertainty

3/24/20

Women in Master Data: Ditte Brix, IMPACT

2/20/20

Get More Value From Your CRM With Customer Master Data Management

2/17/20

Women in Master Data: Nagashree Devadas, Stibo Systems

2/4/20

How to Create Direct-to-Consumer (D2C) Success for CPG Brands

1/3/20

Women in Master Data: Anna Schéle, Ahlsell

10/25/19

Women in Master Data: Morgan Lawrence, Infoverity

9/26/19

Women in Master Data: Sara Friberg, Acando (Part of CGI)

9/13/19

Improving Product Setup Processes Enhances Superior Experiences

8/21/19

How to Improve Your Product's Time to Market With PDX Syndication

7/18/19

8 Tips For Pricing Automation In The Aftermarket

6/1/19

How to Drive Innovation With Master Data Management

3/15/19

Discover PDX Syndication to Launch New Products with Speed

2/27/19

How to Benefit from Product Data Management

2/20/19

What is a Product Backlog and How to Avoid It

2/13/19

How to Get Rid of Customer Duplicates

2/7/19

4 Types of IT Systems That Should Be Sunsetted

1/3/19

How to Reduce Time-to-Market with Master Data Management

10/28/18

How to Start Taking Advantage of Your Data

9/12/18

6 Signs You Have a Potential GDPR Problem

8/16/18

GDPR: The DOs and DON’Ts of Personal Data

6/13/18