Yes, text, words, wording, phrases, clauses, subject matter, context, which are all found in the mountains of documents your business uses, and which you use to manage your business relationships.

Are your documents compliant with SFDR, Taxonomy, GAAP, and Anti-bribery legislation? What about your third party documents ? What about investor communication documents or websites are telling ? Any inconsistency ?

The fact is you don’t know, so when you see numbers without the conditions attached to them, you aren’t even able to make an educated guess. It’s as if you could only see the profit numbers in your P&L, and the rest of the cost information is hidden.

The black swan events of Lehman Brothers, JP Morgan, and Covid-19 have shown us that the old strategies no longer work, and the cost of not knowing is growing exponentially.

Today, however, ignoring this gap in your BI landscape is no longer sustainable. Macro trends in regulations, economies and technologies demand higher visibility, agility, and innovation, driving an increasingly critical and frequent need to access qualitative data from words in documents. You will expect to have the same view and insights of your qualitative data in your BI system as you do of your quantitative data. This is the last frontier for BI.

Not knowing has a cost, and it’s growing. And new digitally-savvy companies will disrupt and compete with more knowledge than you.

Until now, BI users are primarily in sales, marketing, and finance functions because they can cross-reference data. With BI for text, you will be able to expand the scope of BI Analytics to new roles and departments, particularly support functions – Finance, Audit, Compliance, Risk, HR, & M & M&A – that have been left behind in the digital transformation journey and currently perform investigations manually. BI for text will allow companies to make fundamental changes and transform their business operations to be the intelligence engine of the organization – a new Intelligent Operations at the heart of the enterprise. A new paradigm that also resolves the paradox of needing faster innovation and agility to compete, with the demands of more control and regulation.

Some forward-thinking firms have identified the challenges we’ve seen above. And some have tried to tackle the qualitative data challenge, but few have succeeded as the manual way is still the primary way. Some companies have been attempting to take an ‘Outside-In’ approach by applying a hypothesis to a mass of documents (e.g. Pandemic clause) which would work perfectly if all documents were typical, had standard wording and practices. But in the real world, this doesn’t exist. Wording is different in different places, and most documents are unique in their make-up or ‘genome’. When documents are genuinely unique (which is the case with business documents in large groups), this approach cannot deliver on the accuracy and reliability you need and requires extensive training not fit for fast decisions and actions. It fails specifically and spectacularly as you can be precisely wrong in a very specific decision you might need to make. A generalization could hide that very specific costly insight you need.

A few bold companies, including Document Genome, have taken a different route – an ‘Inside-out’ approach – where we don’t start with any assumptions on the make-up of the documents, but literally ‘sequence the genome’ of the documents being analyzed and derive patterns from the actual data. By design, this is faster, more accurate, more exhaustive and in the end, totally traceable. And what’s more, this can work for any document type, on any use case, in most languages and without machine training.

And this new generation of solutions embraces the best of the latest infrastructure technologies with SaaS cloud computing services and Kubernetes for deployment in a private cloud, for example. These future-proof solutions ensure they can scale while also addressing high demands on data privacy, security, ease of maintenance and micro-services.