This document discusses the limitations of big data and predictive analytics in the legal field due to a lack of comprehensive data. Specifically, it notes that there is a scarcity of legal data compared to other industries due to smaller populations and less litigation. While data visualization can help understand large datasets, big data analytics in law requires careful consideration of bias, errors, and statistical significance. The document recommends using confidence intervals and inferential statistics when analyzing legal big data quantitatively. It also states that predictive analytics remain elusive for the legal profession due to data being siloed and not properly aggregated.

1. Big Data/Analytics for Small Firms
Omar Ha-Redeye
Nov. 8, 2016

2. The Big Data “Problem”
 Artificial Intelligence directly connected to big
data
 especially for predictive analytics
 Doesn’t mean that big data is useless for
lawyers
 it’s just not as robust as we would like for it to
be (yet)

3.  we have a scarcity of data in the legal
industry
 Smaller population -> less cases
 Less litigious society -> less lawsuits
 Less trials -> less written decisions
 See The Big Data Problem for AI in Law, Sept. 11, 2016:
http://www.slaw.ca/2016/09/11/the-big-data-problem-
for-ai-in-law/

4. Current Limitations in Legal
Data Analytics
 a rush to use Big Data can result in
‘overlooking a number of important
quantitative issues
 bias in data sample
 measurement error
 questions of statistical significance

5. Why is this a Problem?
 Data visualization can be an extremely
helpful tool to understand and
comprehend large amounts of data
 Concern of falsely visualizing patterns which don’t
actually exist
 without proper consideration Big Data can
be reduced to ‘quite useless or worse’
 Erroneous and misleading conclusions
 False cognitive biases that accepts small sample
sizes as a representation of the whole

6. The Proper Way to Deal with
Big Data in Law
 Solution: use a ‘confidence interval to gauge the
margin of error for any data sample and
subsequent value
 If Big Legal Data is used quantitatively, cannot be
done without use of inferential statistics
 Similar to legal argument without case law or rules of
precedent
 lacks meaningful point of reference, authority
 Even then, results should not be accepted without
further enquiry
 Robert J. Parnell,When Big Legal Data Isn’t Big Enough – Limitations in Legal Data Analytics, Sept. 26,
2016: https://settlementanalytics.com/2016/09/when-big-legal-data-isnt-big-enough-limitations-in-legal-
data-analytics/

7. How Do we Level Up?
 In civil litigation, where much of the research in AI is
being developed, the majority of the useful data is held
privately
 Information exists in silos and is not shared
 Governed by confidentiality agreements
 Protected as proprietary
 Not properly maintained or aggregated
 Either the information is shared, or 3rd party vendors
will aggregate with anonymization

8. Until Then, What is Big Data
Good For?
 Doing what we do now, just doing it better
 More efficiently
 More thoroughly
 More effectively
 Allows small and solo practices to research or prepare on
the same scale as larger firms
 Predictive analytics are still elusive
 Unlikely to feature in practice prominently any time soon

9. Contact
 omar@fleetstreetlaw.com
 @omarharedeye
 https://ca.linkedin.com/in/torontolawyer