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Machine learning.
At this time, the most sophisticated data analytics that can help partners resides in a
branch of artificial intelligence known as machine learning. The term encompasses a range of
methods by which software chews its way through mounds of data and detects patterns. In one
broad category, supervised learning, someone has to classify enough of the instances so that the
computer can figure out a pattern. In another category of machine learning, unsupervised, the
computer “does its own thing,” so to speak. The output can be a classification, or a regression, or
other kinds of results. These tools include neural nets, support vector machines, deep learning,
and Bayesian tools, among many others. This field is currently a hot spring of innovation.
IV. WHAT DO YOU NEED TO DO TO INCORPORATE DATA ANALYTICS MORE INTO YOUR
DELIBERATIONS?
1. Champion.
Your firm needs a partner who is influential and exudes enthusiasm to push the initiative.
Ideally, the champion will proselytize for data analytics and secure funding. Sad, but true; you
will need to ante up to find out whether and how your firm can take advantage of machine
learning.
The champion ought to be persuasive, eager to learn something about new computational
tools, and adept at conveying a vision of how the firm should take advantage of the evolving
capabilities of data analytics for legal management.
The champion will need to handle objections skillfully. Data can actually be feared as
conspiring against the humanistic values of the partnership. Many partners in law firms shy away
from data analytics because the findings invite divisive comparisons. All data discriminates.
Moreover, many partners don’t really want their clients thinking about performance metrics and
costs.
At this early stage of law firms exploring predictive analytics, it is very important for
someone influential to explain what the benefits are and how the firm can achieve those benefits.
The domain of data, software, statistics, programming, and algorithms will be mostly unfamiliar
within your firm, and explanations will be welcome. A partners’ off-site conference is a good
opportunity to raise awareness and attract supporters.
If IT, a practice group, HR, marketing, and a champion all have roles in a machine
learning initiative, it will likely either bog down or take far too much time and money. Each
group has a different interest. Someone needs to coordinate meetings, decisions, and timelines.
That project management role might fall to a junior person, or the champion might take it on.
2. Programming and IT Support.
Your firm will also need programming, perhaps from a consultant or an employee.
Programmers and consultants aren’t cheap, but they are crucial. Also crucial is that any coding
be work for hire, heavily commented so that someone else can follow the steps and logic, and
adhere to the tenets of reproducible research.
People who have not written code for a computer to run probably don’t realize how
difficult it is to code well. It is challenging to get a computer to do what you want it to do. This
hurdle becomes greater as the sophistication of the programming increases, and sophisticated
149
At this time, the most sophisticated data analytics that can help partners resides in a
branch of artificial intelligence known as machine learning. The term encompasses a range of
methods by which software chews its way through mounds of data and detects patterns. In one
broad category, supervised learning, someone has to classify enough of the instances so that the
computer can figure out a pattern. In another category of machine learning, unsupervised, the
computer “does its own thing,” so to speak. The output can be a classification, or a regression, or
other kinds of results. These tools include neural nets, support vector machines, deep learning,
and Bayesian tools, among many others. This field is currently a hot spring of innovation.
IV. WHAT DO YOU NEED TO DO TO INCORPORATE DATA ANALYTICS MORE INTO YOUR
DELIBERATIONS?
1. Champion.
Your firm needs a partner who is influential and exudes enthusiasm to push the initiative.
Ideally, the champion will proselytize for data analytics and secure funding. Sad, but true; you
will need to ante up to find out whether and how your firm can take advantage of machine
learning.
The champion ought to be persuasive, eager to learn something about new computational
tools, and adept at conveying a vision of how the firm should take advantage of the evolving
capabilities of data analytics for legal management.
The champion will need to handle objections skillfully. Data can actually be feared as
conspiring against the humanistic values of the partnership. Many partners in law firms shy away
from data analytics because the findings invite divisive comparisons. All data discriminates.
Moreover, many partners don’t really want their clients thinking about performance metrics and
costs.
At this early stage of law firms exploring predictive analytics, it is very important for
someone influential to explain what the benefits are and how the firm can achieve those benefits.
The domain of data, software, statistics, programming, and algorithms will be mostly unfamiliar
within your firm, and explanations will be welcome. A partners’ off-site conference is a good
opportunity to raise awareness and attract supporters.
If IT, a practice group, HR, marketing, and a champion all have roles in a machine
learning initiative, it will likely either bog down or take far too much time and money. Each
group has a different interest. Someone needs to coordinate meetings, decisions, and timelines.
That project management role might fall to a junior person, or the champion might take it on.
2. Programming and IT Support.
Your firm will also need programming, perhaps from a consultant or an employee.
Programmers and consultants aren’t cheap, but they are crucial. Also crucial is that any coding
be work for hire, heavily commented so that someone else can follow the steps and logic, and
adhere to the tenets of reproducible research.
People who have not written code for a computer to run probably don’t realize how
difficult it is to code well. It is challenging to get a computer to do what you want it to do. This
hurdle becomes greater as the sophistication of the programming increases, and sophisticated
149
