Page 145 - 2019 - Leaders in Legal Business (q)
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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
programming is undoubtedly required to command machine-learning algorithms.
Your firm will need to choose software that can carry out the analyses. Those algorithms
exceed the capabilities of Excel, but many other choices exist. This author relies on the open-
source R programming language, which has been optimized for statistical analyses and data
visualization. Another open-source choice would be Python. Many commercial packages jostle in
the market, including SPSS, Tableau, SAS, and Mathematica.
130
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
programming is undoubtedly required to command machine-learning algorithms.
Your firm will need to choose software that can carry out the analyses. Those algorithms
exceed the capabilities of Excel, but many other choices exist. This author relies on the open-
source R programming language, which has been optimized for statistical analyses and data
visualization. Another open-source choice would be Python. Many commercial packages jostle in
the market, including SPSS, Tableau, SAS, and Mathematica.
130
