​​Frequently Asked Questions

What makes Datamaran unique?

Datamaran is the only platform to analyze unstructured data on regulatory, strategic, and reputational risks related to the latest economic, environmental, social, and corporate governance (ESG) issues (also referred as non-financial issues) – all in one view.

We’ve combined our diverse expertise – legal, financial, investment, ESG, and data science – with Natural Language Processing (NLP) techniques to extract unstructured data from a variety of sources (corporate reports, regulations, and news), derive meaning from the narrative, and ensure that the data is accurate and complete. The end user is and has always been the focus of our work. That’s why we built Datamaran with a global network of end users – large blue-chip companies, advisory firms, and industry experts.

Who uses Datamaran?

​Datamaran is a multi-user platform that delivers strategic insights via annual web-based subscriptions.

The insights gathered by Datamaran users are applied across multiple departments to identify and monitor risks and opportunities, and to inform corporate strategy. Departments include: ESG/ Sustainability/ Corporate Social Responsibility, Risk, Public Relations/ Communications, Investor Relations, Audit, and Legal.  

Multiple users can immediately share insights via the platform, which is accessible from your browser at any time. Each user has their own login in order to easily customize their analyses on the go.

Its ever-evolving AI – and natural language processing in particular – provides scope, breadth and objectivity for many areas of business.

Where does the data come from?

Datamaran extracts unstructured data from a variety of publicly available sources, including:

  • Corporate annual reports
  • Government websites, regulatory databases, and related regulatory sites
  • News sites
  • Twitter

Our database moves at the speed of your markets, capturing the latest corporate disclosure, regulatory developments, and news to deliver consistent and relevant data. It covers a growing universe of:

  • Over 68,000 corporate reports and filings
  • 7,000+ companies worldwide, based on largest market capitalization
  • 6,000+ regulations that impact corporate disclosure on non-financial or ESG issues
  • 1,000 online news sources 
  • 190 countries
  • 100 non-financial topics

How does the technology work?

We have built a system to process thousands of company reports, using NLP to determine how much a report discusses various non-financial topics, which could potentially be material. This list of topics is based on current reporting frameworks, regulations and the news, and continually updated as new issues arise.

Our platform allows users to look in detail at regulations dealing with these issues, and mentions in news media and on Twitter, to provide a broader picture around each issue: what types of disclosure might be required, and how often the company is being mentioned alongside particular companies in the news.

This data is updated daily so that companies can continuously monitor non-financial issues that are relevant to them and benchmark themselves against industry peers, competitors, customers, suppliers or leaders. This means that corporate decision-makers can ensure that their strategy is based on current, forward-looking and objective data.

What is the Ontology?

The Ontology is a dictionary of topics and related key terms that the engine searches for. It consists of financial, economic, environmental, social, employment and corporate governance topics. As an example, anti-corruption is a topic in our Ontology. In order to give the most complete overview, the engine will also search for a multitude of related key terms, such as corruption and bribery.

The current Ontology searches for 100 different topics consisting of 6,000+ key terms and a combination of their related terms. Our experts built this Ontology by manually annotating a high number of sources (e.g. sustainability reports, financial reports, SEC-filings, regulations and social/online media) and by analyzing which topics appear in financial and sustainability reporting frameworks. Both HTML and PDF sources are analyzed by the Datamaran engine. 

We ensure our Ontology is mapped against the main reporting frameworks and guidelines, including the Global Reporting Initiative, United Nations Global Compact, International Integrated Reporting Council, and Sustainability Accounting Standards Board. Our Ontology, related key terms, and relationships between terms for which the engine searches across the above-mentioned sources, uses techniques such as Natural Language Processing (NLP), semantic analysis, and machine learning; it includes a growing collection of topics.

What does high/medium/low and no mention mean?

High, medium, low and no mention are the emphasis scores that are used to assess the level of emphasis each company puts on a specific topic in their corporate reports. The emphasis takes into account variables, such as, the number of times the topic is mentioned in a sentence (also a number of sentences mentioning it), its location (e.g. CEO letter). 

  • High emphasis topics are found a high number of times in a source and/or in key sections of a source
  • Medium emphasis topics are found a moderate number of times in a source, or rarely but in a key section
  • Low emphasis topics appear, only rarely in a source
  • No mention

Grammar rules are applied for each assessment of emphasis. For instance, if a company states that it does not disclose information on executive compensation – that particular topic is not counted in the analysis.

For each assessment of a topic, the definition provided in the topic description is followed closely. The degree of granularity of a topic can be different: Human Rights can be a more general topic, whereas Forced Labour is a more granular topic that addresses a specific Human Rights issue. 

What is NLP?

NLP stands for Natural Language Processing. It sits at the intersection of artificial intelligence (AI), computational linguistics and computer science. It is the process of making a computer understand the structure and the meaning of language as used by humans.

What’s the difference between machine learning, NLP and AI?

​NLP pursues a set of problems within the field of AI that are to do with understanding language.

AI is the process of teaching a machine to do intelligent things – you give it rules and teach it how to play. Machine learning is the process of making computers learn from past experience and previous examples.

What is the benefit of AI?

Our system can achieve results more quickly than performing the same tasks manually. When completing data analysis tasks, it helps to increase productivity – you are no longer spending the majority of your time on data collection, but on data analysis and interpretation of results for forward-looking decision-making.

What is SaaS?

Datamaran is a Software as a Service (SaaS) – a cloud-based platform offered on the basis of an annual license agreement. Being SaaS means customers trust that Datamaran continuously evolves along their journey and provides the best software service tailored to their corporate needs. SaaS scales automatically and provides continuous value for the users.

What is Datamaran's expertise?

​Our team of legal, industry, and ESG experts worked closely with our data scientists on developing and refining the analytic process behind Datamaran. This process is guided by our proprietary ontology, which is a dictionary of topics and related key terms that cover economic, environmental, social, and corporate governance issues; it includes a growing collection of topics and terms that reflect current and emerging trends in corporate disclosure, industry standards, regulatory initiatives, and public opinion. 

The engine behind Datamaran searches continuously for these topics and terms across a variety of sources – corporate reports, regulations, and news – using techniques such as NLP, semantic analysis, and machine learning.

The most powerful business intelligence tool on the market!

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