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Structured Data: The fuel of the future

Structured data

The fuel of the future

Law firms have access to volumes of data. Data about themselves, their people, how they practice, metrics regarding utilisation and profitability and also an immense amount of client data. However almost all firms, are data rich but information poor.  

It is hard for law firms to make the most of the data they have at their fingertips, and this is because, much of the data; especially that which pertains to the work completed by a law firm; resides in multiple systems of record, collections of documents and emails, and other unstructured, inaccessible repositories or point solutions.  

Over the years we have seen many firms make tremendous progress in this area. There are firms hiring data scientists into their ranks, boutique consultancies being set up, like my good friend Tom Baldwin at Entegrata, firms attempting to become #datadriven by creating data lakes and really focusing on the present and the future.  

There are also many vendors looking to support law firms in this endeavour too, trying to create structure from a firm’s data debt. In the last 5-10 years these have been predominately the AI tools that have focused on bridging the gap between past work and the future, by creating extraction tools (and the wonder of AI) to take data from previously completed work and create some structure around it for use in the future or for use in live transactions such as due diligence work.

We’re clearly at the point of accelerated technological growth, driven by the abundance of tools available to firms of all sizes and I haven’t even mentioned LLM’s yet. With this technological growth we’re creating even more data silos and even more data to make sense of. I think there is a tremendous amount of strategic thinking that still needs to take place, in order to become #DataDriven. Especially when thinking about combinations of the right tech, with the right people and culture and the right process.  

My question is how can law firms start to address this problem moving forward tactically, whilst thinking about the strategic goals? And ultimately how can they reduce their data debt?  

Part of the answer, is to of course continue to assess and bring structure to the past – this work will inevitably continue, but cannot be done in isolation. You have to think about the present and the future so how do you go about creating an environment, with MORE accessible, structured data for the future. I believe that to reduce your data debt, you must start by aligning your data models with structured processes. In short, creating standards.  

Standards are vital when thinking about data. Ensuring you have an integrated, scalable, and flexible data model, perhaps leveraging something like the SALI model, alongside well thought through, integrated processes, that combine human in the loop workflows and deliver automated outcomes.  

Structured data is a foundational component for use cases to become scalable, whether it be a legal intake and triage system, a full scale CLM or a real estate portfolio and lease review tool. This couldn’t have been more clearly echoed by the panellists and presenters during the first few presentations of Skills.Law conference earlier this year. It was great to listen to the presenters talking about the importance of data standards, the importance of quality of the data, improved interoperability of systems and commonality in data structures and standards, and many references to the fantastic initiatives such as the SALI Alliance and others.  

Law firms have access to a wealth of data. Many have started to think about converting this wealth of data into process friendly, human readable and accessible information that can drive real strategic change, deliver insights, and ultimately increase profitability. As I said, I think structured data is a foundational component for accelerated progress for law firms. 

Autologyx approaches everything from a ‘data centric’ viewpoint (rather than ‘document first’ which is common in legal technology): each platform has a relational object model which can be configured from the ground up to include any number of ‘object classes’ and associated field types. This structured data is then available to show anywhere in the collaboration workspace views, it can be referenced  in tasks, and importantly in the workflow engine to build automation against the information, including APIs which are automatically available against your configured data set, and document automation via a template-builder word plugin. 

Talk to any of the Autologyx team if you’d like to hear about our workflow automation solutions and how we put the data at the heart of your solutions and services.