Getting good quality sausages out of a law firm data sausage machine

(or why data governance helps change behaviour)

A sausage machine has two parts—what goes in and what comes out. We’ve all heard "garbage in, garbage out." This is an excellent shorthand for the idea that the quality of an output is dependent on the quality of the input.

In other words, if the data quality of the authoritative source system is low, the quality of the business intelligence dashboard, AI or knowledge product will also be low.?

That means we need to have a good understanding of what quality means and how that applies to whatever we’re putting in our sausage machine. For data concepts like ‘work type’, ‘role’, ‘industry’, and ‘sector’ - what do they mean, how are they used, where and how do we share the firmwide taxonomies, and do we or don’t we use industry standards (like noslegal , SALI , or even ISO lists)?

This also includes identifying who is accountable and responsible for the data at every stage in its journey down the firm’s data process river. ?This means that teams might be capturing and curating data that they don’t need for their own operational processes but that flow downstream to someone else.?

Having high-quality data, in short, means implementing some level of data governance.

Lawyers don’t prioritise admin

It also means shifting some of the accountability and responsibility for good data entry back onto the people who have the information in their heads—the lawyers themselves. Now, this is the point where most business services folks are going to groan because we've all fought battles or died on the hill trying to get lawyers to do data entry for themselves.?

?

This is for many reasons, the most common of which are:?

• They're usually too busy and see it as an administrative task, so it gets pushed down to the trainee or their PA.
• It gets forgotten, or they do it quickly and pick the easiest option. The easiest option (if you think about lists of sectors or industries) is either the option at the top of the list because it saves them having to scroll or the option that says “other” because then they don't have to think about it.

This perception of data capture as an administrative task means that lawyers aren't prepared to expend effort on doing it properly. So, a massive cultural and organisational behavioural change is a huge part of making the sausage machine work and having good definitions and clarity. Support for this change can only come from the very top of the firm, and you need a lot of very visible, very vocal champions out there helping you bang the drum to make this happen. It's not easy.?

Prioritising good data

Educating the humans

However, I'm seeing more and more firms grasp the nettle of making lawyers fill in forms correctly and promptly. That's not to say that you don't still need to have a quality assurance (QA) wrapper behind them, nor will business services teams disappear or there’s an impact on headcount or processes—these activities still happen.??

What it means is that there is a more robust consequences mechanism on a stronger feedback loop. So, if a lawyer chooses “other”, someone will ask them ‘why’ and challenge them. That someone could be a sector or practice head or another senior role somewhere in the firm. The lawyer will have to justify why they picked “other” rather than scrolling down and picking the correct term, such as “litigation”.

This improved challenge-based process forces lawyers to use mental calories to think about the data they are entering.

For these consequences to be fair, everyone must be given support, training, education, and transparency. The Data Governance team needs to publish your firm’s vocabularies and take every opportunity to teach and share why correct data entry is important.

IT training needs to be updated to reflect these changes. Rather than just saying, ‘This is how you use this system,’ it becomes,?‘This is why making good choices when you do data entry helps you get the most out of this system to produce this task or this data set or this report, whatever you want to do’, so the two things go hand in hand.

This is why creating your data river and defining your fields, vocabulary, and firm’s language is such an important part of good-quality data. Most firms look to their Knowledge, Marketing, and BD teams for clearly defined agreed-upon lists of things like industries, sectors, types of law, and jurisdictions.?Additionally, Finance and HR teams will have lists of offices, billing entities, job titles, management roles, and so on.

So, since firms already have these lists, they also have cottage industries somewhere within the firm. People who are running around behind the lawyers, tidying up all this data. Data governance helps illuminate these hidden silos and helps you educate the lawyers to do improved data entry which means better quality sausages come out of the sausage machine.?

Leveraging the machines

At its most achievable, this means carefully planning the use and reuse of data throughout the firm. This involves creating a clear set of integrations to ensure that lawyers only need to provide the data once. It also means identifying the authoritative systems of record, the systems of reuse, and well-documented business processes and system integration architecture.

Using AI for data entry is a rapidly evolving field that has the potential to significantly streamline and enhance the accuracy of the data entry process. It can automate the tedious and repetitive task of entering data into the firm’s systems, freeing up lawyers and business teams to focus on more complex and strategic tasks.

One key advantage of using AI for data entry is its ability to process large volumes of data quickly and accurately. The algorithms can recognise patterns and learn from them, improving efficiency over time. The more data the AI processes, the better it becomes at its job, potentially surpassing the speed and accuracy of manual data entry. Reducing the likelihood of human error can also improve the quality of the inputted data.

AI-driven data entry solutions can also incorporate natural language processing (NLP) to understand and process unstructured data, such as written text or complex forms, and convert those into a structured format that can be easily digested by the data systems. This capability is particularly useful for law firms since they rely heavily on paperwork, as it allows them to digitise and organise their documents more efficiently.

In short

The journey towards improving data quality and, by extension, the outputs of our sausage machine require more than just one-line mandates from the top. It demands a cultural transformation within the firm that champions the value of meticulous data entry.

This transformation is guided by data governance leads and supported by visible advocates who underscore the importance of quality data and its impact on the firm’s intelligence capabilities and process efficiencies. This can be simplified by leveraging technologies (like AI) to do as much as possible so that humans have confidence that they will only be asked once for a piece of information that lives in their heads.

How Iron Carrot can help

Innovative law firms have big goals for improving the client experience through data innovation.

Through our extensive law firm background, we have developed a unique data governance road-mapping approach to help law firm leaders launch the proper foundation for their data strategy.

If you want to chat confidentially about how Iron Carrot can help your firm with its Data Strategy and Data Governance initiatives, then send me a Direct Message via my Profile , or book a call via the Iron Carrot Limited website.

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