The Deployment of Big Data Analytics Technologies in Law Firms in Ireland and the Potential Impact on the Future Delivery of Legal Services: Risks and Solutions

Date01 January 2020
e Deployment of Big Data Analytics
Technologies in Law Firms in Ireland and the
Potential Impact on the Future Delivery of
LegalServices: Risks and Solutions
e debate on articial intelligence (‘AI’) and what it means for the future of
the legal profession, in particular the day-to-day work of solicitors, has gained
increasing momentum in recent years. is article does not attempt to oer any
authoritative pronouncements on this broad issue in general. Rather, the focus
of this paper relates to a branch of AI known as Big Data analytics and, more
specically, to what extent it can be used to the advantage of law rms across
Ireland, along with considering the potential impact this could have on the future
delivery of legal services. While it can be argued that law rms must adapt to
technological change or risk being le behind, the deployment of legal analytics
technologies is not without its own risks. us, the aim of this article is to open a
discourse and interrogate the assumptions embe dded in Big Data by addressing the
practical issues that should rst be considered by solicitors before using such tools.
is analysis will comprise four parts; the rst part will commence by setting out
the characteristics of Big Data and Big Data analytics technolog ies. e article will
then move on to examining the rationale behind using legal analytics tools in law
rms in the second part, which will include a discussion of current analytics tools
in use across various jurisdictions today and their respective advantages. e third
part will focus on the limitations and risks arising from the deployment of Big Data
analytics technologies in law rms, with the fourth and nal part oering potential
solutions to alleviate these problems insofar as possible. is article will conclude
that, while Big Data analytics technologies can be used by solicitors as a welcome
guide in case preparation, over-reliance should be avoided, thereby stressing the
crucial continuing need for human judgment with all legal analytics technologies.
Big Data and Big Data Analytics
Big Data, which is oen linked to, among other things, the Internet of ings,
machine learning, 3D printing and virtual reality, is one of the main drivers of
* LL.B.(Ling. Germ.)(Dubl.), LL.M.(EULISP)(LUH), Trainee Solicitor at Mason Hayes & Curran.
46  
change in information technology today as it continues to move into new elds
and disciplines.1 However, Big Data, like Big Data analytics, does not lend itself
to one operative denition. It is for this reason that the importance of examining
the fundamental principles of both Big Data and Big Data analytics is crucial,
emphasising that while predominant technologies may change over time, the
general principles of data science are unlikely to be altered in the coming decades.2
Consequently, a brief discussion of the essential underlying and operational
characteristics of Big Data and Big Data analytics will follow in order to introduce
the topic and to set the general context of the paper.
Big Data
Big Data refers to the ‘massive quantities of information produced by and about
people, things and their interactions’, or ‘digital traces le by people’.3 Although
typically associated with modern technologies, Big Data is not an entirely
new concept. Indeed, industry, government and academia have long produced
computerised datasets, with national censuses being one such example.4 However,
compiling census data is a closely controlled process.5 Censuses are conducted once
every ve years, ask a limited number of questions within conned local areas and
are typically inexible in nature in that once a census is set, questions cannot be
added or removed.6 Big Data, on the other hand, is generated continuously and
seeks to be exhaustive and ne-grained in scope, as well as scalable and exible in its
production.7 us, Big Data is not stagnant8 and is recorded in or near real-time.9
Everyday examples of Big Data production include digital CCTV and social media
However, while the volume and variety of data available today may have
outstripped the capacity of manual analysis,11 the sole dening characteristic of Big
1 Richard Kemp, ‘Legal Aspects of Managing Big Data’ (2014) 30 Computer Law & Security
Review 482, [3].
2 Foster Provost and Tom Fawcett, ‘Data Science and its Relationship to Big Data and Data Driven
Decision Making’ (2013) 1(1) Big Data 51, 58.
3 Danah Boyd and Kate Crawford, ‘Critical uestions for Big Data’ (2012) 15(5) Information,
Communication and Society 662, 662.
4 Caroline Compton, Fleur Johns and Wayne Wobcke, ‘e Promise and Problems of Including “Big
Data” in Ocial Government Statistics’ e Conversation (Australia, 11 November 2018) 1,1.
5 Rob Kitchin, ‘Big Data, New Epistemologies and Paradigm Shis’ (2014) 1(1) Big Data and
Society 1, 2.
6 ibid.
7 ibid.
8 Tom Kulik, ‘A Kick in the Assets: e Big Deal about Data & IP’ Above the Law (United States, 20
November 2017) 1,1.
9 Kitchin (n 5) 1.
10 ibid 2.
11 Provost, Fawcett (n 2) 51.
e Deployment of Big Data Analytics Technologies in Law Firms in Ireland 47
Data, despite many trite proclamations, is not the sheer volume of data involved.12
In fact, Big Data is described by commentators as a poor term; datasets which
once required supercomputers due to their size can now be analysed on standard
desktop computers.13 e reason for this is due to increases in computing power,
as described by Moore’s law, in addition to decreases in data storage costs, as laid
down in Kryder’s law.14 On the contrary, Big Data comprises several other factors;
drawing on extensive engagement with literature, Kitchin outlines the fundamental
characteristics of Big Data as:
Huge in volume, consisting of terabytes or petabytes of data; high in velocity,
being created in or near real-time; diverse in variety, being structured
and unstructured in nature ; exhaustive in scope, striving to capture entire
populations or systems; ne-grained in resolution and uniquely indexical in
identication; relational in nature containing common elds that enable the
conjoining of dierent datasets; exible, holding the traits of extensionality,
meaning new elds can be easily added, and scalability, meaning rapidly
expandable in size.15
Big Data is therefore known for its ‘ve Vs’, namely volume, velocity, variety, veracity
and value.16 Further to this, Boyd and Crawford describe Big Data as a ‘cultural,
technological and scholarly phenomenon that rests on the interplay of te chnolo gy
… and analysis’.17 is leads on to the next topic of discussion, namely Big Data
analytics and prediction.
Big Data Analytics
Big Data is only valuable if it can be meaningfully analysed. We can begin to make
sense of Big Data through the use of special analytics tools and platforms known as
Big Data analytics. is process relates to the systematic extraction and examination
or analysis of the masses of data at hand in order to discover new insights that were
previously inaccessible from bulk data.18 is is made possible today due to modern
high-powered computation,19 with algorithms that can extract and assemble large-
scale patterns in human behaviour for analysis.20 us, a capacity to search, agg regate
12 Kitchin (n 5) 1.
13 Boyd, Crawford (n 3) 663.
14 Daniel Martin Katz, ‘uantitative Leg al Prediction – or – How I Learned to Stop Worrying and
Start Preparing for the Data Driven Future of the Legal Services Industry’ (2013) 62 Emory Law
Journal 909, 913–914.
15 Kitchin (n 5) 1.
16 Kalbandi Ishwar and J Anuradha, ‘A Brief Introduction on Big Data 5Vs Characteristics and
Hadoop Technolog y’ (2015) 48 Procedia Computer Science 319, 320.
17 Boyd, Crawford (n 3) 663.
18 Anne Tucker and Charlotte Alexander, ‘ Why we’re Training the Next Generation of Lawyers in
Big Data’ e Conversation ( Uni ted States, 2 October 2018) 1,1.
19 Kitchin (n 5) 2.
20 Boyd, Crawford (n 3) 664.

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