Data Analytics For Mergers and Acquisitions: A Brief Guide

Technological advances continue to accelerate the rapid growth of many data sets, with institutions generating a lot of data daily.

Some everyday activities that produce big data for organizations include customer interactions, communication on various digital platforms, financial transactions, barcode scanning, digital files, and documents, among others. 

In its raw and unstructured form, such data can be challenging to understand or use, rendering it invaluable to an organization.

Data analytics refers to scientific techniques and processes that analyze raw data and convert it into information. It enables organizations to make strategic business decisions for efficiency and optimal business performance. 

As organizations strive to thrive in fluid business environments characterized by cutthroat competition, the need for mergers and acquisitions continues to grow. We at DealRoom help dozens of companies organize their M&A process and here we'll explain how data analytics help business to grow.

In brief, mergers occur when two separate entities combine to create a new joint organization, while acquisitions refer to the takeover of one entity by another.

Mergers and acquisitions (M&A) are motivated by the need for product and service diversification, economies of scale, and increased financial capability, among other reasons. 

Whereas some decisions can be based on intuition, others must be backed by data. M&A are major organizational decisions that need proper data management.

During M&A, data analytics empowers dealers with crucial information for every aspect of the real deal's lifecycle.

For instance, well-managed data allows deal makers to quickly check the financial health of a company they're targeting to buy. 

The term data analytics can instill a sense of dread if you don't like statistics. This mustn't be the case today, given the variety of software tools that allow you to easily export data or pull information from an app without any need for technical skills.

Today, we have businesses dedicated to providing solutions that enable you to sync information between apps seamlessly. For instance, you can effortlessly do salesforce google sheets integration from the comfort of your office.

Types of data analytics for M&A

Four main categories of data analytics facilitate M&A. Powered by specialized systems and software to facilitate data analysis; these four categories of data analytics include: 

1. Descriptive Analytics

This is the most commonly used data in business. Descriptive analytics summarizes past data, usually on dashboards that provide historical information.

Business applications of descriptive analysis include KPI (Key Performance Indicator) dashboards, monthly revenue reports, and sales lead overviews.

For instance, descriptive analytics would show dealers how a targeted company's stock performed on the market within a specified period. 

2. Diagnostic Analytics

This type of analytics helps to explain the factors that influenced an occurrence. Diagnostic analysis gathers cues and insights from descriptive analytics to further establish the causes of specific outcomes.

For instance, descriptive analytics would help highlight the factors affecting your target company's stock market performance during a period. Dealers use diagnostic analysis to create connections between data and business trends. 

3. Predictive Analytics

This helps to forecast possible future outcomes. Predictive analytics rely on statistical modeling to make accurate and logical predictions about possible future events.

During M&A, predictive analytics can help assess a target organization's future risks. This is enabled through sales forecasting, team member productivity, and success forecasting.

Predictive analytics help dealers identify profitable M&A deals and go the entire cycle or end poor deals. 

4. Prescriptive Analytics

This is considered the frontier of data analytics. It combines state-of-the-art technology with insights from the three analytics types described above.

Predictive analytics utilizes costly technology and data practices that require considerable investment resources.

Artificial Intelligence (AI) systems are examples of prescriptive tools whose analytics engines have been shown to speed up M&A deals while lowering costs and reducing risk.

For instance, during M&A, dealers can use AI technologies to peruse thousands of business documents swiftly. The information generated can point out potential problem areas, helping dealers to evade poor acquisitions. 

Data analytics help to reduce the amount of time spent during M&A.

Big data also helps dealers during the early stages of M&A to identify any risks or opportunities posed by the targeted company, enabling early-stage decision-making about the deal's viability. 

Benefits from using data analytics during M&A

By using data analytics during M&A projects dealers and businesses can benefit in the ways outlined below.

1. High-Quality Information 

Data analytics provide dealers with structured data sets as information, enabling them to visualize and test crucial aspects of the M&A project.

The lack of quality data analytics software can cause dealers to depend on low-quality data and information. This often leads to misinformation that is time-consuming and costly during M&A projects.

Data analytics enables dealers to make quality decisions based on rich data availed within a shorter duration that helps save time and costs. 

2. Navigating Loads Of Data At High Speed 

The amount of data available to any business today serves no purpose if it's not analyzed effectively to generate meaningful information.

During M&A, time is of the essence, yet dealers must go through volumes of data to make good decisions.

Data analytics classifies tons of disorganized market and business data, helping dealers with vital information concerning the M&A. 

3. Identifying The Ideal Target 

During M&A, data analytics provides organizations with statistics that enable you to see the bigger picture regarding the deal's impact on a company's strategic position.

Data analytics can help you to visualize the direction of the new entity to be formed, including its probability of success or failure.

For instance, predictive analytics can help you to forecast how markets will react to the new business, informing you whether your target enhances the deal's viability. 

4. Smooth Post-Deal Integration 

Data analytics also help to accelerate integration processes between the M&A organizations after the deal is closed.

M&A usually brings cultural and personality clashes to the fore as employees from different organizations work to gel and adapt to a new culture that may be significantly different for acquired companies.

Data analytics can speed up the transition process by reducing the time spent on power struggles and personality clashes.

For instance, HR analytics can expose challenges caused by skill gaps in the managerial abilities of new bosses. This information can help the development of leadership training programs for the new managerial team. 

In conclusion 

Execution speed is a crucial indicator of successful M&A deals.

Today, businesses generate a lot of raw data that isn't as useful in an amorphous form. Information overload can lead to M&A deals that become costly financial mistakes. 

Data analytics increases your chances of executing successful M&A deals by availing multiple quality data sets already processed into high-quality information.

Many companies offer data analytics services, so you don't need technical qualifications to conduct the next M&A deal or inform a critical business decision.

Acquiring data integration software will automatically turn you into a statistician and increase your chances of making accurate data-driven decisions.