We consolidated two decades of legacy case data onto Databricks and built the access layer that lets teams reach historic records, making it possible to retire the source systems.
As the biggest conveyancing law firm in the UK, Taylor Rose had accumulated over two decades of legal case management platforms, picked up through acquisitions and successive technology changes - some holding records going back to 2000. Now operating from a central Salesforce estate, legacy systems were no longer needed.
This created two problems. Firstly, licence fees, database servers and file servers across the legacy estate accrued millions of pounds a year, largely for data that was only ever being read. The second was access. As a legal firm it needs its history so the data had to stay reachable long after a case was closed. This single requirement was what kept the whole estate alive and the costs locked in.
Underneath both sat a third issue. Twenty years of case data spread across ten separate platforms is twenty years of data nobody can properly ask a question of.
The original brief was narrow but after leading a technical scoping exercise with Taylor Rose, across the legacy estate, we deliberately widened the scope once we understood how the data was actually being used. We mapped which platforms existed, what sat inside each, and how the data was structured.
To avoid leaving users stranded, we dived deeper into the estate to understand what each department needs and how often they need it. This gave us the ability to build a tranched plan rather than a big bang, sequenced by real operational redundancy, rather than by what looked easier to unplug.
ThirdEye built three things; pipelines to move the data, a platform to hold it, and an application that lets users reach it.
Custom integration work built pipelines that pull data out of each legacy platform into Azure, with Databricks sitting on top. Both file data and record data were in scope, along with the metadata around them. Hundreds of terabytes moved into a single consolidated location.
On top of that sits a purpose-built application in Databricks. It gives Taylor Rose teams a way to search and retrieve legacy case records without the source system being online - the piece that makes decommissioning possible at all.
The third element is the decommissioning plan itself, partitioned around what the business can genuinely let go of. Whilst all of the data is already in Databricks, a phase one consisting of only systems that can be let go is underway. Future phases incorporate those additional systems that are currently still live and have users accessing the source platform.
Phase one decommissioning saves Taylor Rose substantial cost on the systems in scope. Critically, legacy data stays available allowing teams to pull a case file from 2003 when a compliance query lands. There’s no longer a source system sitting behind it, and no dependence on people to access it.
Databricks has also become the route for data migrations into Salesforce. Rather than running a traditional source to target migration, data now moves through Databricks, which has made a normally awkward process considerably simpler and given Taylor Rose a reusable path for every migration still to come.
94%
cost saving on legacy case management systems
16x
less infrastructure spend per unit of storage
Almost every established business has this problem. Legacy systems accumulate, the invoices arrive every month, and the data inside them cannot be used for anything. What makes it repeatable is the order of operations. Ingest first, solve access second, then decommission at a pace the business can absorb, delivering a constant stream of financial savings.
Phase two follows this initial phase, and is where the largest savings sit. Two specific systems carry around £1m a year in licence costs between them - a substantial benefit for Taylor Rose to gain from.
After that, twenty years of case data in one place opens up initiatives that were never possible whilst it was scattered. So alongside transformational financial gains, Taylor Rose can properly explore dynamic fee estimation, powered by a complete history of case data.
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