Why improving process efficiency always starts with quality data
Running shoes, world records and sh*t sausages
People have been running for centuries. From the dawn of humankind we’ve been running.
First we ran to catch our prey and we ran from our enemies but that was about it. There was some running in the 19th century but running as a pastime was only really required as we adopted a more sedentary lifestyle.
The running craze of the 1960s which Nike cashed in on, propelled running and its slower paced sibling, jogging, into the mainstream and grew it to the $40B and growing industry it is today.
“But running is running right?”. “We can’t really improve the process, surely it’s just practice that makes us stronger, fitter and faster?”. Wrong! Just ask the two giants of the sports shoe industry, Nike and Adidas, both of whom spend millions of dollars a year on research and development. Beyond training and sports nutrition, the big strides (sorry I couldn’t resist) in process improvement have come from running shoe construction and material.
Take the case of Ethiopia’s Tigist Assefa who last weekend in Berlin broke the marathon record by over 2 minutes! Assefa completed the 26 miles in just 2hr 11 min 53 secs. So was it down to her years of training and marathon running experience? Hardly, she only raced her first marathon last year. The Adizero Adios Pro Evo 1’s however were making their debut!
What’s this got to do with data?
When Adidas looked at developing a running shoe to beat the Nike Alphafly, which Eliud Kipchoge wore when he broke the then mens marathon world record in 2022, they started with the data. In Adidas’ own words: “Throughout the development process, data was gathered from elite athletes during in-camp training and testing in Kenya and in the Adidas labs at the HQ in Herzogenaurach, Germany.”
So if something with so much history and as seemingly straightforward as the process of running can be improved by starting with the data, perhaps the same can be said of your legal processes.
Legal process improvement
Whether it’s a client facing department looking to improve their service offering and respond to client requests for increased collaboration, a claims handling team needing to streamline decision making in order to handle higher volumes or an internal ‘business of law’ process that currently relies on emails, post-it notes and xls files; understanding the data that drives these processes is an essential first step.
The problem with many automation projects is that not enough regard is given to the efficiency of the process nor the quality of the data that drives the output.
Consider this client onboarding process I recently discussed with a firm, and I’ll simplify for conciseness;
1. Client contacts via phone, email, website.
2. Partner reviews the phone note, email, website form and checks existing PMS for existing client/conflict
3. Partner creates task for file opening team to create a new client/matter record in PMS
4. Client Onboarding Team create new client/matter and advise Partner via email of the new client ref. Number
5. Partner checks new client/matter in PMS for completeness of data required for initial engagement letter.
6. Partner advises Client Onboarding Team via email of missing data
7. Client Onboarding Team contact prospective client via email to request missing data
8. Prospective client provides missing data
9. Client Onboarding Team update PMS and advise Partner via email
10. Partner drafts client engagement letter and sends it to prospective client
I could go on…
Some would argue that automating this 10 step process would bring benefits to the firm and they’d be right. Perhaps it would remove some of the emails which may be contributing to the time it takes to onboard new clients. Maybe it would reduce the number of prospective clients that seem to abandon the process and never get to the client engagement letter. (Spoiler alert; they’ve gone elsewhere where the onboarding process was less painful).
Alternatively, if we look at the data that’s essential to the task of sending the client an engagement letter, we can map out a more efficient process containing just 3 steps! Furthermore the new process can not only speed up the entire process but also reduce the risk of inaccurate data being recorded. Without first considering the data we would simply be automating a poor process or as one Legal Designer recently commented on LinkedIn: “if you feed rubbish ingredients into the tech sausage machine, you just make sh*t sausages more quickly.”
So with LegalGeek arriving in London this week, let’s celebrate our legal data geeks and make sure we start by focussing on quality ingredients before we create a recipe for terrible sausages!