Entis is a data science company. We use advanced data technology to help our clients by generating investable insights for thematic investing.
Investors often focus on specific investment themes like sustainable development, disruptive technologies, healthcare, life sciences or education. Entis helps investors to identify which companies fit within a certain theme and provides them with investable insights that are fully optimized for a specific investment theme.
Entis helps asset owners, private equity firms, venture capital firms, pension funds, and other investors to fully understand what companies fit best into their investment themes. We also work with R&D and M&A departments of large enterprises.
We have developed an advanced data processing factory where we apply Artificial Intelligence, Natural Language Processing, and Machine Learning technologies to process large amounts of textual information from company reports, websites, analyst reports, patents, and so on. You could compare Entis’ data factory to an automated conveyor belt that subsequently collects, combines, extracts, and analyses data to produce the investable insights that help our clients to decide which companies to invest in.
- Artificial Intelligence = AI is a widely used term to describe technologies that mimic cognitive functions associated with the human mind, such as learning and problem solving. So when we use the term AI, we mean a system or software program that is able to learn or solve problems by processing data intelligently.
- Machine Learning = ML is a type of AI technology that makes it possible to automatically detect patterns in large amounts of (e.g. textual) data that are extremely hard to find using human skills or conventional computer systems. Well-known examples of ML technologies are neural networks and deep learning systems.
- Natural Language Processing = NLP is technology to clean, simplify, analyze, and extract computer understandable information from textual data like books, technology briefs, patents, and webpages.
Let’s say that as an investor, you wish to invest in a company. If you want to learn about the ins and outs of this company, there is a huge amount of information available, including annual reports, patents, the company website, the websites of competitors, news articles, and so on. To form a deep understanding of what companies are doing would require digesting thousands of pages of text. So the key information that will give you an advantage over other investors might be very hard to find.
Since text data is so hard to process using human analysts, investors focus on information that can be swiftly analyzed using computers, namely information in numbers. There are a lot of numbers available to investors, such as price information, financials, industry codes. All this data can be fed into computers to compare performances, do statistics, and to do this quickly. But the problem with these numbers is that all investors have access to the exact same data. So you can only win by being microseconds faster or by using extremely smart models. Or by being lucky.
There is another issue at play here as well, as these numbers only cover a tiny part of all the information out there. What if you could have access to all that deeply hidden company information in the form of text? Information that is not easily available to the market. Easy to digest and relevant information on corporate strategies, technology developments, product information, and risk profiles. What if you could have this not only for one company, but for all listed companies – every day and based upon the latest information? Deeply relevant information overlooked by the market, yet available to you in a format that seamlessly feeds into your current systems and processes.
Using technologies like Artificial Intelligence, Machine Learning, and Natural Language Processing, we are able to generate actionable insights from textual information. If you combine those insights with the results of your numerical analyses, you are able to deeply understand a company – how it performs right now, but also its opportunities and challenges when going forward.
That is what makes Entis unique. We are able to generate a thematic fit score for each and every company with a detailed analysis that explains why the company belongs to a specific theme. As an investor, this provides you with an investable insight!
Data quality is extremely important in the financial industry. Investors need to be absolutely sure that we provide the correct information for each company.
In order to reliably produce high-quality investable insights we have developed our Entis data factory. The data factory produces insights with a steady frequency and controls the quality in the data production process using standardized and automated procedures. Using our data factory, we are able to produce high-quality data scoring thousands of companies on many themes on a daily basis.
You can compare our data factory to a manufacturing plant where a conveyor belt moves all available textual data through a series of steps to produce the end product. It is important to understand that the application of Machine Learning and Natural Language Processing technologies is just one step of many that together form a data factory. In a data factory, raw uncleaned data arrives from many sources and is subsequently cleaned, transformed, linked to the right companies, and analyzed using NLP and ML algorithms. The end result of all these steps is then delivered to the client. As in a car factory, we have implemented a great many quality checks to ensure we deliver reliable, accurate, and consistent investable insights to our clients.
When a client needs investable insights into a specific investment theme, we start with modeling the theme in great detail. The starting point might, for example, be a list of keywords. Or a large taxonomy, as was the case when we started our work on the Sustainable Development Goals developed by the United Nations.
To model the theme, we analyze text data from a large number of sources – company websites, product information, research reports, scientific publications, analyst reports, patents, and so on – to give meaning to the initial keywords and concepts. Using our AI technologies, we then discover what type of text information is relevant in the theme’s context. We are therefore not dependent on human expertise to come up with an exact definition of the theme. Instead, we use the textual data to make sure we do not miss anything relevant.
With the resulting model, we can score companies on all relevant thematic topics. We transform text into numbers and are able to compare and rank companies. We thus generate insights for each company. That means the investor fully understands how each company fits within the chosen theme and is able to compare how companies relatively perform to the chosen theme.
We also profile each and every company and make them comparable in such a way that the investor is able to fully assess the potential of each company and the opportunities and challenges they are facing. The end result is a continuous stream of actionable insights that seamlessly integrate with the systems and processes of an investor.
First of all, we are a technology company. We are data scientists and use data science technology to help our clients to gain actionable and investable insights into themes, groups of companies, and individual companies.
Our technology is highly focused on analyzing the huge amount of textual information ‘out there’ that analysts and investors are generally not able to process with the speed and depth required to stay on top of what is going on in the world. To be able to read all relevant textual information on companies, industries, technologies and other developments, analyst firms would have to hire an army of highly-skilled business analysts who would have to spend years to read the thousands of pages of textual information in thousands of industry segments, technology fields, regional areas, and the likes. That is simply impossible. They are also not able to utilize advanced technologies like Entis has implemented in its data factory.
Our approach is unique in allowing clients to quickly build custom-made portfolios. Based on the client’s vision on the portfolio, Entis shapes the definition of the theme to capture the client’s ideas and convictions. Using our data factory, we automatically generate thematic classifications and scores for thousands of companies. With this approach, we provide clients with the essential data to build thematic portfolios.
With our AI enabled data factory, we are able to collect enormous amounts of textual information and analyze all this information in a very short amount of time. That is why we are very different from your typical analyst firm. Our insights provide a novel and deep understanding of the current and future performance of companies and investment themes. Not only that – we also ensure that investors are able to use all this information in a very actionable way.