Something revolutionary is happening to the world’s documents. For five thousand years, they have been used to preserve and share knowledge, but over the last century the number of documents has increased dramatically, as has their size and complexity. While technology has facilitated that growth, until now it has not provided us with the tools to quickly read and analyse, the increasingly dense information these documents contain. This has led to severe inefficiencies due to the sheer work overload, duplication of efforts, and delays in decision making. Many industries find it hard to keep up with the document overload, an issue which impacts both the quality of work and the bottom line.
The solution to this problem is a single approach to quickly, intelligently and efficiently analyse and share the dense data contained in documents. This solution has only recently been introduced to the market through innovative Document Analysis Platforms. These platforms add layers of shareable depth to text based documents. These new Big Data layers create Deep Documents which rewrite the rules of documentation enabling us to easily understand, analyse and share huge numbers of documents at a very rapid pace.
In the early stages of the internet, the web revolved around finding and consuming information. The next stage saw an explosion in the creation of user generated content through social networks, as well as a multitude of blogging platforms and individually uploaded content. The stage we are in now revolves around making the vast resource of web data readily available for deeper analysis and interaction.
Adding algorithmic advances and technological improvements to the concepts of Social/Professional Networks, Wisdom of the Crowd, and Cloud Computing has enabled the creation of Document Analysis Platforms and the related Deep Documents:
- Social And Professional Networks – The explosion of social networks has given users an overarching framework in which to operate, interact and disseminate information, with the inherent value of the services offered on the networks increasing exponentially with the number of users or organisations. These network effects also come into play in relation to Document Analysis Platforms which are relevant to narrower interest groups, enabling the creation and reuse of content across a network or organisation at a rapid pace. An added benefit is that users can be quickly authenticated on these networks, providing a better way to ascertain the reliability and quality of the content.
- Wisdom of the Crowd – Similar in spirit to crowd-based collaborative platforms such as Wikipedia and Stack Overflow that rely on empowering people to interact on one single platform, Document Analysis Platforms can track insights from multiple documents and then make those insights available to our professional networks. This powerful ability makes the analysis of similar documents, whether by us or by our network, significantly easier and quicker.
- Cloud Computing – Considering the high complexity levels of Deep Documents, sharing storage and computational power with other enterprises allows for much cheaper delivery, as well as the flexibility to easily scale up without large upfront costs. As companies have become more comfortable with the notion of storing critical data on remotely-located servers, the barriers to sharing the analysis of Deep Documents have been removed.
- Algorithmic Advances – AI and basic machine learning technologies have become much more cost-effective and prevalent to the point where they are now provided online “as a service”, sometimes even as an open source service. The first Narrow AI applications in the professional services field are beginning to emerge, augmenting knowledge workers’ abilities and helping them in routine tasks, rather than fully automating their work. These applications, including Document Analysis Platforms, use AI to provide greater insight and accumulated experience, whilst retaining the judgments of sophisticated knowledge workers.
Documents are getting increasingly complex but fortunately, so are our means of analysing and understanding them. We have arrived at a stage where companies must evolve their methods of interacting with their documents or risk being crushed under their weight. In the coming weeks, we intend to put our theory into practice with a new software offering that will make document analysis fast, easy and precise. Follow us to keep up to date with the upcoming announcement.
Eli Luzac is the CEO and founder of TagDox, the world’s first Document Analysis Platform, which enables firms and individuals to quickly and easily analyse documents in a collaborative, semi-automated and structured manner.
Tony Angel is the former global co-chairman of DLA Piper, Executive Director of Standard & Poor’s EMEA and Managing Partner of Linklaters.