Don’t Acquire a Company Until You Evaluate its Data Quality

Mergers & acquisitions happen when companies believe they are more valuable together than when operating separately. The companies join workforces, systems, infrastructure, and data to become a new, more powerful, more valuable, more effective entity. That is only until they realized they overlooked or underestimated the key issues with data, IT infrastructure & integration plans. In fact, most merger and acquisition plans fail miserably because of data integration challenges.

Save yourself the devastating cost of a failed merger. Do not acquire a company until you evaluate its data infrastructure and adherence to quality.

Why the Emphasis on Data Quality?

As of 2019, the value of global M&A deals stands at 3.9tr USD. Ironically, another report concludes that up to 90% of mergers fail to meet their goals.

While there are many factors to this failure (with most of it being subjective to a company’s culture, budget, infrastructure etc.), we’ve seen the lack of data due diligence as the most common reason of migration failure.

The problem with data quality isn’t new. However, as the world moves towards harnessing big data to make important decisions, it’s imperative for companies to understand the risks of neglecting data quality. Most companies focus on data quality as a post-migration concern, but, this approach can significantly slow down the M&A process; if not turning it into a failure entirely.

Apart from a failed merger, there are other typical challenges encountered when there is a negligible emphasis on data quality. These are:

  1. Ballooning Costs Due to Poor Data: Post-migration, companies often end up spending millions of dollars in hiring data scientists or analysts and specialists to fix data quality problems. $250+ is spent each year just to hire and retain talent for data quality management. Add in other expenses caused by missed goals, fragmented data and the threat of penalties, violations etc. and you’ve got millions of dollars in lost revenue. The sheer diversity & complexity of data infrastructure is enough to cause the downfall of a business in terms of costs, reputation & lost opportunities.
  2. Duplicate Data as a Critical Challenge: While dirty or messy data is a challenge, it is not as dangerous as duplicate data. In fact, duplicate data is the most concerning data quality challenge. Duplicates can lead to an incorrect perception of business performance with stakeholders, trading partners, customers, and suppliers. Apart from flawed perception and analytics, duplicate payments can cost companies between 0.5% and 0.1% of annual invoice payments. This translates into 1 million USD in duplicate payments in a company with 1 billion USD in payables.
  3. The Problem with Disparate Data Sources: Organizations hardly have a single or unified source of truth when it comes to data – specifically customer data which in the case of M&As is gold. Disparate data sources vary not just in terms of formatting or structure, but also in terms of information. For example, sales and marketing may be storing two different sets of customer information, leading to a fragmented understanding of the audience. Disparate data sources are the leading causes of duplicate data and it takes a mammoth amount of effort to resolve. You will need to consolidate data from every source, weed out deeply hidden duplicates, clean data, restructure it, and adjust it according to the new system. This alone takes months if not years of effort. Companies often wrongly assume that it takes just 6 months to clean data, but in essence, it takes years of wasted effort. Data quality issues become a persistent problem – one that companies spend millions of dollars in rectifying.
  4. Data Security & Compliance: It’s important to check whether the company you want to acquire practices safe data ethics or whether they are in compliance with local and global data laws. Unless you have performed the necessary audit across business processes and compliance with data laws, you risk penalties and legal suites.
  5. Reviewing the Company’s Data Management Processes: How does the company capture manage and maintain data? What are the data standards employed and how do they perceive data quality? There’s a lot you can learn when speaking to organization leaders on their understanding of data quality. It’s quite likely that an organization not aware or aligned with data quality will pass it on to IT and IT will probably discuss all the grand ambitions they have with cloud infrastructure, data lakes, new ERP systems, and other fancy jargons – they just won’t have a plan for resolving data quality issues. In our experience with Fortune 500 companies, almost all of them hold the IT department responsible for data issues. Whether it’s customer data or business user data; the extraction & transformation of data for use has always been pushed to IT. Unfortunately, the IT department is not the true owner of customer data, neither are they aware of the intended purpose of the data. If a company cannot give you the right answer to their data quality management practices, you might want to ensure data issues are resolved, ‘before’ the merger.

How Do You Assess Data Quality Matters Before a Merger

Too often, organizations spend significant human resources on fixing data quality problems after a merger. Then additional changes are implemented without resolving the data-quality problems. Organizations, especially active acquirers need to implement a data quality assessment process before acquiring companies.

Here’s a simplified, but effective list of questions you can pose to determine the level of seriousness the organization has towards data quality issues.

  • What is the ratio of data to errors? This can be found by dividing the total number of errors by the total number of items.
  • How many errors arise as you convert information into a different format?
  • What is the severity of duplicates? Are unique IDs well-maintained?
  • Does the company have large data sets stored in disparate sources?
  • What data gathering, storage, the transformation process is practiced by the company?
  • What are the data strategies and technologies used by the company?
  • How do you identify the potential and future value associated with merging data sets?
  • What are the significant challenges that will be encountered in the merging of data?
  • Is it possible to create a golden, consolidated or single view of customers?
  • What tools and solutions will be required to make the transfer or consolidation of data a success?

How to Merge Purge Data to Create Golden Records 

To know more, see how we’ve helped businesses combine data from multiple sources to create the perfect record. 

While these questions may feel overly simplified, it helps with the preliminary assessment of the company’s approach to data.

It is critical that the data is, ‘fit for the purpose.’ Neglecting this crucial aspect can lead to expensive litigations.

How Does Data Ladder Help Companies in an M&A Process?

Data Ladder is a Gartner recognized data quality solutions provider. Our flagship software, DataMatch Enterprise has been used by U.S government institutes and Fortune 500 companies like Deloitte, HP, Coca Cola to resolve data quality issues.

When it comes to M&A processes, we’ve helped dozens of companies meet business goals with data profilingdata matching and data deduplication. In fact, Data Ladder has been recognized as the only solution with the highest data matching accuracy rate at 95%. IBM and SAS stand at 80 and 85% respectively.

With our solution, your company can:

  • Determine the overall health of data: Sometimes, during a M&A, both companies realize they have flawed data. So regardless of whether you assess data quality pre or post assessment, you will still need a tool that can profile your data and let you know the overall health of your data columns. With the data profiling feature, you can evaluate the type of errors plaguing your data. For example, data profiling allows you to see how many fields of phone numbers you may have missing or how many name fields have abbreviations instead of proper names. With this info, it’s easier to manage and plan a data cleansing strategy.
  • Dedupe data with data matching between, across and within data sources: Duplicates occur not only within data sources but also between two data sources or across multiple data sources. So if marketing, sales, billing, or customer support store the same customer data in multiple ways, you will need to consolidate them to weed out duplicates and get an accurate view of customer data.
  • Data integration with support for 150+ platforms: Data stored in Oracle, Excel or Hadoop? Need to evaluate third-party data like Facebook or Twitter? Whatever your requirement, Data Ladder has support for over 150+ platforms. This ease of data integration allows companies to work directly on their customer data platforms without having to worry about extraction, transforming and loading data into acceptable formats.
  • On-premises solution for better safety and control: Most data quality solutions work on the cloud while others require you to upload data on to their platform. Data Ladder has an on-premises solution that lets you resolve data issues on your own servers or cloud platform. Once you purchase the license, you have complete control over how you want to use the solution to your benefit.
  • Following a complete data quality framework: DataMatch Enterprise is designed to allow users to run their data through a data quality framework comprising of 8 critical stages:

This whole framework allows companies to take their data through a complete cleansing, sorting, and deduping process and finally creating a master record that they can use for their intended goals.

To Conclude:

We’ll keep it short. Any time you plan on acquiring or merging with a company, perform a data quality assessment. You don’t want bad data to become a roadblock to greatness.

How best in class fuzzy matching solutions work: Combining established and proprietary algorithms


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