Due diligence is the complex process of assessing a potential investment and ensuring it is a sound one. It involves the gathering, classifying, and analyzing of large volumes of data.
Given its very nature, it’s the perfect field for data analytics, which can speed processes up and assess the quality and reliability of data.
Due Diligence at a Glance
First, let’s look at the steps involved in due diligence to give you a clearer overview of the impact data analytics can have:
- Data gathering: This used to be a lengthy and tedious process. It involves the accumulation of data from all stakeholders across numerous fields, from finances to sales, from marketing to legal. Data gathering is usually the most complex and potentially most difficult part of due diligence.
- Data consolidation: This step involves the storing and classifying of all data gathered. Poorly organized data is notoriously difficult to analyze. Consolidation should be executed as well as possible, with the use of various tools and a human element involved to ensure all data is labeled correctly and allotted its proper place and importance.
- Data analysis: The analysis and comparison of data aims to determine potential risks and yields. This is where data analytics and AI can truly help and make the process more efficient and cost-effective.
- Reporting: The final step entails drafting a document that summarizes all the key findings and suggestions.
Now, let’s explore how data analytics can improve this process.
Speed Up Traditional Due Diligence Processes
The biggest challenge of due diligence used to be extracting reliable data in a timely manner from a multitude of sources. There is only so much a human being can read and analyze in the course of a workday.
With the help of data analytics solutions and APIs, data can be accumulated and even classified and organized in a fraction of the time. Missing data or incorrectly formatted data can be discovered more quickly, and issues with data quality can also be highlighted before the analysis itself begins.
Data analysis tools support the creation of standardized data models, which can then be used to analyze data on vendors, contracts, customers, or inventory. They can also aid the analysis of revenues or EBITDA and help make future projections.
It is important to note that a measure of human oversight should still be retained. If data is incorrectly formatted or labeled, the data analytics tool may utilize it incorrectly, which is why it’s key to double-check your data and its sources before moving on to the analysis stage.
Help Challenge Assumptions
In order to save time and money in the traditional due diligence process, data quality was assumed based on the source and various external or internal factors. Due diligence companies can thus miss or overlook threats or potential by assuming a certain piece of data is correct or incorrect.
Data analytics tools allow for a certain amount of forensic investigation. They can be used to audit and verify statements and even projections. They will rarely overlook a threat or an opportunity because they have the time and the power to verify, double-check, and critically analyze both the data itself and its source.
Improve Integration Processes
We often forget that the data gathered in the diligence phase can be leveraged to make the acquisition process smoother and less disruptive.
When you adopt a data-driven approach to company integration and merger, you can better optimize processes and activities. All the data you have gathered already should be utilized to this end.
Take note of the key issues and difficulties that have been underlined in the due diligence report. Come up with a plan to overcome them even before you finalize the paperwork. Coming in prepared can increase revenues and even boost staff morale.
Challenges and Risks of Using Data Analytics in Due Diligence
Now that you are aware of all the benefits of using data analytics in due diligence, you also need to understand what the challenges and potential risks are:
- Data privacy and security: Compliance with data privacy and security laws and regulations is essential in the due diligence process. You need to understand the specific requirements of the country the company and you reside in and ensure you tick all the necessary boxes. This might mean you need to familiarize yourself with the General Data Protection Regulation (if you’re operating in the EU) or the California Consumer Privacy Act.
- Algorithm biases and unfinished algorithms: Algorithms are not above perpetuating biases. Whether they have been acquired inadvertently or not, you need to be aware of them and the havoc they can cause. When working with AI or any other algorithm-based program, make sure there are checks and balances in place that will help you avoid making a costly mistake.
- Data quality: Also note that poor or incomplete data can skew the results of data analysis. If the company you are interested in has done a poor job of keeping their records organized and filed correctly, this may not be something you can predict or tackle yourself. Remember to analyze the quality of the data from a human perspective and to use human logic when looking at reports and data.
Given all of these facts, make sure to find experienced service providers who know how to do due diligence properly. That way they will be well aware of the potential pitfalls and know how to navigate them. The smallest of mistakes or misinterpretations can cost a lot.
The high-speed development and adoption of AI data analytics software certainly provides an opportunity to save both time and money on the due diligence process. However, AI is not all-knowing, and it is certainly capable of making a mistake. Don’t forget to vet the humans you entrust it to as well.
Due diligence can significantly be improved and sped up with the help of data analytics and AI-based software solutions. However, take all of these developments with a grain of salt and don’t forget to employ a human pair of eyes and ears, too. Together, they can deliver the best results.