Data Analytics For Mergers and Acquisitions

Every mergers and acquisitions (M&A) decision is driven by data. In the past, you would not know the real story behind the numbers until after your post-deal transition. But today, thanks to M&A data analytics you can go deep into the data to find out what’s really going on—before it’s too late.​

Choosing your target

Always, the first step of any M&A deal is the determination of a potential target firm. Some everyday activities that generate big data for organizations include customer interactions, communication on various digital platforms, financial transactions, barcode scanning, digital files, and documents, among others. But this raw and unstructured form, such data can be challenging to understand or use, rendering it invaluable to an organization. A sound portfolio strategy identifies the value of potential targets in terms of future revenue streams, disruptive technologies capabilities, customer purchasing power, and a definitive competitive advantage. Data Analytics can allow business leaders to visualize better, allowing for detailed comparisons and due diligence of potential targets.

What is Data Analytics?

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

Types of data analytics for M&A

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

1. Descriptive Analytics

Descriptive analytics, is the most commonly used data in business that summarizes past data, usually on dashboards to provide historical information about the company to be acquired.

KPI (Key Performance Indicator) dashboards, monthly revenue reports, and sales lead overviews are the business applications of descriptive analysis  For instance, descriptive analytics shows dealers or interested investors how a targeted company’s stock has performed on the stock market indexes during a specified period. 

2. Diagnostic Analytics

This type of analytics explains the factors that influenced an occurrence. Diagnostic analysis gathers cues and insights from descriptive analytics to further establish the causes of specific outcomes in business or transactions.For instance, descriptive analytics could throw light on the factors that may affect your target company’s stock market performance during a period. Dealers and prospective investors or buyers use the diagnostic analysis to establish connections between data and determine business trends. 

3. Predictive Analytics

Predictive analytics helps make a forecast on possible future outcomes. It relies upon statistical modeling to make accurate and logical predictions about possible future events.During any M&A deal, predictive analytics can help prospective buyers and investors assess and ascertain the future risks of a target organization. This is enabled through sales forecasting, team member productivity, and success forecasting.

Predictive analytics help dealers, potential investors or buyers identify profitable M&A deals and decide whether to go the entire cycle or end a poor or non-profitable deal. 

4. Prescriptive Analytics

Prescriptive Analytics is considered the frontier of data analytics. It combines state-of-the-art technology with insights from the three analytics types- Descriptive Analytics, Diagnostic Analytics, and Predictive Analytics.

Prescriptive analytics utilizes costly technology and data practices that require considerable investment resources. Artificial Intelligence (AI) system is one of the prime example of prescriptive tools whose analytics engines have proven to speed up M&A deals while lowering costs and reducing risk. For example, during an M&A deal, dealers or potential buyers and investors can use AI technologies to peruse hundreds or thousands of business documents quickly. The information that is generated from it can point out to potential problem areas of the company or business to be acquired, and thus help those interested in investing or acquiring to evade poor acquisitions. Data analytics thus help to minimize the time period of an M&A deal, by identifying any risks or opportunities posed by the targeted company, enabling early-stage, and well-informed decision-making about the deal’s viability. 

Advantages of using data analytics during M&A

By using data analytics during merger and acquisition deals dealers or potential buyers and investors as well as businesses can benefit in innumerable ways:

1. High-Quality Information 

Data analytics facilitates potential buyers get structured data sets as information, enabling them to visualize and evaluate crucial aspects of the M&A deal.

Generally, the lack of quality data analytics can cause potential buyers to depend on low-quality data and information. This often leads to misinformation that is time-consuming, and may lead to wrong decision which may prove fatal during M&A deal.

Data analytics enables dealers to make quality, well-informed decisions based on rich data accessed within a shorter duration of time span that helps save valuable time and costs. 

2. Navigate Large Quantity of Data At High Speed 

There is a large amount of data available to any business today, but if it’s not analyzed effectively and properly to generate meaningful information, then it is useless. During M&A, time is of great importance, so dealers or potential buyers must go through volumes of data to make well-informed decisions.  Data analytics classifies large volumes of disorganized market and business data, helping dealers or potential buyers or investors with vital information concerning the M&A deal. 

3. Ideal Target Identification

During any M&A deal, it is data analytics that provides the organizations with vital statistics that facilitate them to see the larger picture regarding the deal’s impact on a company’s strategic position.

It is data analytics that helps them to visualize the direction of the new entity to be formed, including its probability of success or failure. For instance, predictive analytics can help them to forecast how markets will react to the new business, enabling you to realize whether your target enhances the deal’s viability. 

4. Smoothens Post-Deal Integration Process

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

Generally, M&A brings with it cultural differences and personality clashes to the fore as employees from different organizations begin to work together and adapt to a new culture that may be significantly different for the acquired company. Data analytics enables them to quicken the pace of the transition process by reducing the time spent on power struggles and personality clashes.

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

In conclusion 

The speed of execution of a deal is a very important indicator of the success of the M&A deal.

Generation of a lot of raw data that isn’t as useful in an amorphous form can lead to M&A deals becoming financial risky mistakes.

Data analytics enhances their chances of executing successful M&A deals by availing multiple quality data sets that have already been processed into high-quality information.

Moreover, they don’t need to have technical qualifications or proficiencies to conduct the next M&A deal or inform a critical business decision as there are many companies that offer quality data analytics services. Also acquiring data integration software will automatically turn them into a statistician and increase their chances of making accurate data-driven decisions.