NLP Meets M&A: Enhanced Insight, Analytics, and Decision-Making
The term may not be very widely used, but natural language processing (NLP) is part and parcel of our everyday lives – in the form of popular digital assistants like Siri, Alexa, or Google Home. And NLP isn’t confined to the private sphere; it also plays an increasingly important role in honing businesses’ competitive edge. One area where this tech holds considerable promise is M&A.
The Untapped Potential of Unstructured Data
Before looking at the application of NLP in M&A, let’s think about the more general challenges presented by unstructured data. Of course, companies hold much of their data in structured form in their enterprise software. But a greater percentage of corporate data is stored exclusively in a vast array of unstructured formats, such as e‑mail, text documents, social media, and voice recordings, to name just a few.
Tapping into this potentially valuable information would enable businesses to more efficiently resolve and even prevent many issues. But generating insight from unstructured data is far from easy and requires complex, time-consuming data analytics processes.
This is where natural language processing comes in. NLP is a real game changer when it comes to helping enterprises analyze vast volumes of unstructured data and extract value from them.
A Brief Introduction to NLP
So, what exactly is NLP? Built on disruptive technologies, such as artificial intelligence (AI) and machine learning (ML) – which I covered in an earlier M&A blog –, NLP is essentially about enabling computers to replicate human understanding of text and spoken word.
NLP marries computational linguistics with statistical ML and deep-learning models. And it’s the interplay of these technologies that enables computers to process human language, whether in the form of text or voice data, and comprehend its full meaning.
Businesses are already reaping the benefits of NLP through solutions like voice-operated GPS systems, digital assistants, speech-to-text dictation software, and customer-service chatbots. The tech is also playing a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.
NLP + Unstructured Data = Speed Gains
NLP’s ability to automatically understand unstructured data sources and extract relevant information from them offers major advantages in M&A. A recent Accenture Strategy survey found that analysts can reap 50-60% time-savings by deploying NLP to identify M&A targets and shortlist potential candidates.
Let’s say an analyst team has drawn up a list of targets running into the thousands. By deploying NLP in conjunction with weighted criteria, the experts can filter out all targets that don’t fulfill the relevant criteria, generating a shortlist in double-quick time.
From First Impressions Right Through to the Small Print
What’s more, NLP allows M&A teams to form an initial impression of a company more rapidly. Any information linked to an industry, enterprise, or economic activity can be used as source data. Processing this unstructured data manually would take great deal of time and effort. By contrast, NLP provides rapid insight, delivering a basis for investment decisions.
NLP also allows M&A teams to spot otherwise difficult-to-identify legal hazards. For example, the tech can pinpoint defined “red-flag” terms (for example, relating to liability) in vast volumes of unstructured legal documents. These terms can be automatically identified, highlighted, and shared with the Legal team for closer scrutiny.
The Upsides: Greater Insight, Faster Processing, Lower Costs
NLP offers a whole host of benefits when deployed in M&A. When it comes to resource exploration, the tech enables you to extract information from a wide range of historical documents, including reports, and notes.
As already mentioned, another big advantage is time-efficiency. With NLP, you can process volumes of data that would otherwise require days or weeks of manual analysis in a matter of mere seconds or minutes.
And last but not least, the tech promises cash savings. Manually analyzing unstructured data not only takes time; it also calls for human resources, racking up sizeable overheads. Here, NLP offers a cost-efficient alternative, cutting costs and freeing up staff for value-adding tasks.
The Downsides: Complexity, Reliability, and Training Issues
While these benefits are appealing, NLP also has its drawbacks. For one thing, developing an effective solution of this kind is a complex undertaking. Most companies lack the highly specialized knowledge needed to create the required algorithms, putting them at risk of having to rely on external providers.
As digital assistants clearly show, NLP has made great advances in recent years. However, like any tech it’s not 100% reliable. There’s always the danger of NLP misunderstanding unstructured data – for example, because of misspellings, ambiguous terms, or other factors that humans are more likely to pick up on.
Finally, there’s the all-important question of training. Any NLP model is only as good as its training data. If you feed poor or questionable data into the system, it will inevitably learn inefficiently and may even learn things that are just plain wrong. And the dangers of letting a badly trained NLP solution loose on transaction-critical data are not hard to imagine.
Want to Find out More?
All of that being said, NLP holds undoubted promise for businesses, both in their day-to-day operations and in the specific area of mergers and acquisitions. If you’re interested in digging deeper into NLP in M&A, feel free to reach out to me. I’ll be more than happy to discuss this fascinating tech and its potential applications in your M&A initiatives.
And, as ever, if you have your own success (or horror) stories involving NLP, please share them in the comments section.