7 Questions on The Future of Data Driven M&A Markets with Anders Lundgren

Finding good investments!

The hunt for alternative investment returns has continued to intensify. In these competitive times the challenge is to find the truly attractive companies at attractive valuations, also ones off the general radar screen. To find “the needle in the haystack”. As there are millions of private companies in each region, a well-structured sourcing process also using efficient and intelligent tools to find relevant companies is more important than ever.

We have experienced an increased trend to search for investment opportunities outside the limelight in each market. We also see a trend of smaller buy and builds, as competition is so high and consequently valuations are pushed up, especially in all tech related sectors.

We see a trend in the effort to spot and reach out to interesting, fast-growing companies 1-3 years before they have grown into the specific GP investment criteria, to build an early relationship. This means for us that we are now focusing more on ‘trending’ and ‘key indicators’ tools, to facilitate our customers to spot these companies easier and earlier.

We see private equity and venture capital becoming more data-driven and structured in their deal sourcing process. This normally means using value add deal sourcing tools and good data. We have worked closely with our customers since the start, they are the best source for smart ideas and product development that we have. It’s a win-win and nice co-operation and they get a better platform from us.

We also see players building their own sourcing platforms, where we co-operate with them as a data provider and for enriching data.

I think the tech possibilities is central now! New technology is not around the corner but here and now and adding real value to our platform and for our customers. Today, enriching traditional, structured data with additional layers of alternative, unstructured data make the data more relevant, This is even more important for private companies where the availability of public data is lower and public reporting is lagging in some markets by up to 24 months.

New machine learning technology can not only be used in enriching data, but is working very efficiently in comparing company data and finding truly similar companies with advanced trained algorithms. In many cases where traditional sector codes and descriptions do not really help at all. And it learns and improves.

So, is it all about AI and machine learning? No, far from it. Good and qualitative data is still the most important of all, so are traditional search methods and technology, where ML is just adding new possibilities and a new dimension in my view.

Too unstructured data is rarely useful for data driven M&A, even if the data itself is massive. It’s about finding relevant, selected data and then being able to “connect the dots” in a smart way, otherwise the data is easily useless to enrich structured company data. This could be, for example, targeted web scraped data of a more detailed business or product focus, business keywords or as simple as a relevant up-to-date employee count.

I am coming back to the marriage of traditional tech and traditional data with new tech and alternative data. We often hear people in general terms talking about ‘AI in near future’ now as been done for the past 5 years. But the fact is that in our sector, machine leaning is adding value here and now and is already an integral part of the sourcing process.

Machine learning and NLP (Natural language processing) algorithms being part of the M&A deal sourcing process for private companies will be taken as a given in the near future.

Tough question. This is answered better by our customers within M&A and PE than by us. Each GP has their own investment strategy and approach to differentiate themselves off course.

From our perspective as a data and platform provider, I believe data driven deal sourcing tools add value in the indicative sourcing process, the first 70-80% to target the long lists and make sure no opportunities are missed.

I would say that in the strategy and innovation of what and where to search, as well as the more qualitative last 20-30% of the sourcing process down to singling out the target short lists, is performed by our customers. No ML algorithm or platform replaces that part of the process, at least not today.

Very exciting future!

It honestly feels like it is already here and now. Again, we see the following:

→ Highly competitive investment market drives demand for a structured sourcing process.

→ Key to have efficient intelligent, sourcing tools to triangulate the universe of attractive investment opportunities.

→ True value-add data, enriching structured data with selected, alternative data layers. What these data layers may off course differ for different players.

→ Combination of traditional technology with advanced ML and NLP technology.

Our mission is to facilitate the deal origination process for private company targets, from an aggregated sector analysis down to long or short lists and detailed individual company data. We aim to make sure our customers do not miss out on any potential companies or targets in the screening process due to bad data.

Our role is to add the best value possible in the 80-20 sourcing analysis of relevant companies. The last 20-30 per cent, the real qualitative work of selecting the short lists or target companies lies with our customers and not by us or any tool or algorithm. This is also important for us to understand and what role we play as a data and platform provider in the sourcing process.

Our detailed private company data coverage, covering more than 70 million European private companies today, also helps our clients in any phase to quickly get the relevant details of a company, ranging from business focus, detailed ownership data, key people and peoples’ networks, group structures and detailed financial data.