The use of big data and predictive analytics in the legal field is a tough challenge for three key reasons. Firstly, the sector is inherently risk averse. In addition to this, the data itself is unstructured and lost in a labyrinth of documents. And probably the toughest - for lawyers who judge everything on proven years of training and experience, Artificial Intelligence is still considered to be in adolescence.
For a leading Australian consumer law firm, with a history of bold and innovative moves, the idea of pursuing legal analytics was not too farfetched. The first step was to prove its feasibility.
About the client
- Leading Australian Consumer Law Firm
- Proud history of providing a voice for Australians who need access to justice
- Over 800 Employees
- $160M+ annual revenue
An inquisitive and analytical leadership team saw the opportunity that advanced data techniques presented for their firm. However, a low level of maturity across existing data components and a capacity-constrained in house data and analytics team, left this opportunity largely untapped.
Using data science techniques to predict case outcomes highlighted the potential for a material improvement in the overall win:loss ratio. In an industry driven by “No win. No fees” this presents a substantial upswing for both the firm and its clients.
Learnings extracted from the use case informed the improvement pathway required to realise this value at scale.
“A pragmatic and practical pathway for our firm to adopt”
Chief Financial Officer
After moving the company from loss-making to profitable over the past four years, the leadership team saw the opportunity that advanced data techniques presented. Inspired by legal companies in the UK who have had their hands forced through reform and US software companies who automate litigation insights, the Chief Financial Officer saw three opportunities for analytics to influence the ultimate organisational goal of “delivering the best outcome for clients in the shortest possible time”:
- Predicting and improving the outcome of a case
- Predicting and improving the duration of a case
- Putting knowledge in the hands of lawyers
What could a material improvement in key value drivers do for your business?
An initial assessment pointed to a low level of maturity across required data components to adopt legal analytics, and the inhouse data and analytics team were already at capacity, which led to the engagement with Ignite.
The use of data science techniques through Ignite’s Proof of Value methodology answered three key questions:
1. What is the opportunity that data science presents?
2. Is the opportunity feasible?
3. Tools to help practitioners act upon their portfolio
Predicting the outcome of a case and helping practitioners analyse their portfolio provides a potential material improvement in the overall win:loss ratio for the client. This in turn becomes the foundation for the required investment in data and analytics.
One of the key outputs of the Proof of Value methodology is an evaluation in business terms. By their nature predictive models are not 100% accurate — understanding and articulating the inherent business risk of adopting these techniques is key.
“For leaders, understanding the risk of adopting models and the risk of not adopting models is vital. We could be the first to adopt and get it wrong, or be the last to adopt and miss the opportunity.”
Chief Financial Officer
From the outset, the focus was to extract learnings from the use case, and inform the improvement pathway required to realise potential value at scale. Ignite’s Analytics Pathway focussed on three key areas:
- The ideal analytics architecture leverages capability that is already available to the client, and incorporates leading capabilities such as Microsoft Power BI. With an existing strategic investment in Salesforce, the potential for bleeding edge capabilities such as Salesforce Einstein was also evaluated and positioned for future horizons.
- The legal industry has a relatively small amount of structured data, which if not captured efficiently, can make the use of advanced data techniques impractical. Furthermore, the industry is rich in unstructured data, which can lose accuracy during translation. An outcome of the Analytics Pathway was to inform how data capture processes at source needed to improve to maximise the potential of ongoing return.
- An area often underestimated by organisations looking to introduce advanced analytics capability is the impact on its people. The Analytics Pathway work assessed the desired outcomes and architecture to provide organisational capability recommendations that mitigate risks anticipated from potential skills shortages, capacity constraints and gaps in governance.
Does your analytics strategy cover more than technology?
Does it inform how data capture processes at source need to improve?
Does it anticipate and mitigate people-related risks?
Many organisations looking to improve their data and analytics capabilities take an “architecture led” approach. They do this by defining a target architecture for each component and then migrating all data elements. This approach helps build out strong foundations but can be costly without any immediate return.
Some organisations opt for a “use case led” approach, which focuses on identifying and filling those data gaps that deliver value upfront. However, this approach has a proven track record of leaving a history of “production prototypes” in its wake, and if poorly selected, the value derived from the use cases could be trivial and jeopardise the need for change.
An eight-week engagement that combined the experimental nature of data science and the diagnostic nature of architecture development successfully brought together the best of both approaches for the client:
- Using the Ignite Proof of Value methodology, we built prototype analytical models to predict the likelihood of case outcome, but more importantly provided learnings to inform what is required to deliver use cases at scale.
- Ignite’s Analytics Pathway future-proofed these learnings, and created an improvement pathway that allows the client to lift their data and analytics capability, whilst focussing on delivering results incrementally and continuously.
“Too often, data architecture is presented as a set of boxes and arrows where purists get stuck arguing the terminology. Executing Proof of Values in parallel makes the impact of data architecture tangible, and brings to life the ongoing pain that Data Scientists would have to go through.”
Harj Chand, Founder - Ignite
This leading consumer law firm has made an opening statement in the case for legal analytics. The use of big data and predictive analytics in the legal field is a relatively new concept, with the legal practitioners ultimately delivering the verdict. But with the rapid advancement of data analysis techniques like Machine Learning, Optical Character Recognition, and Natural Language Processing, it is only a matter of time before any reservations are overturned.
To discuss how analytics could help your business or for further details on our Proof of Value and Analytics Pathway methodologies, reach out to us.
- Data Science
- Proof of Value
- Analytics Pathway