series, which can be useful
for those wanting to use
(For more detail on and links to affiliated
businesses, see the sidebar on page 35.)
Technical tools, applications, and software that attorneys can use to mine data,
organize evidence, and increase their profitability can be dizzying. Using the latest
and greatest tool may not be what’s best in
every situation. Firms and corporate counsel
departments should determine the highest
return on investing in a BI tool dependent
upon their size, needs, and access to data.
Along with other factors, a BI tool should
be chosen for its ease of use, value, and the
service it will provide to a non-data-savvy
attorney.
Big Data Price
of Admission
To be blunt, there are few free tickets into
the world of big data. While litigators are
enjoying a decrease in technology-assisted
review pricing, the cost of managing and
analyzing discovery can still be a material
expense in a case. When more in-depth analysis is needed, litigators may need to hire an
expert, at expert hourly rates. However, numerous free external data sources exist for
the ambitious attorney who wants to incorporate Census data or macroeconomic data
into their arguments.
Beyond Excel (sitting at the least sophis-
ticated end of the toolbox spectrum) and R
(freeware programming language requiring
expertise), BI tools lying in the middle of
the sophistication spectrum will cost mon-
ey. Such tools run a range of prices, and
are often priced as software-as-a-service
(“SAAS”), meaning that firm size will affect
the number of software licenses required
and the eventual price of implementing the
tool. There’s also the cost of an analyst or
consultant to help with BI data analytics,
unless this knowledge exists in-house.
Overall, the prudent firm will need to
commit some time to the homework of
pricing analytic tools that match their needs.
Conclusion
The legal industry sits atop one commenta-
tor’s list of industries that need big data—
and need to understand it. 7 Law firms and
attorneys are sitting on a gold mine of BI
data and past case data that can guide de-
cision-making, boost case outcome success-
es, increase profitability and inform pricing
structures, and more efficiently distribute
their human capital. Simultaneously, exter-
nal open-data sources are becoming more
user-friendly to the non-data-savvy, especial-
ly to benefit the legal areas of discrimination
and economic damages.
The term “big data” is already on its
way out, another reflection of the speed of
technology. 8 We will soon become accustomed to our data being “big” and turn
our focus toward predictive and prescriptive analytics, citizen data science, and
analytics marketplaces—all areas to which
the legal field will want to monitor, participate, and contribute.
www.azbar.org/AZAttorney 36 ARIZONA ATTORNEY JANUARY 2016
endnotes
1. R. Bhargav, What is Big Data, and Why Should You Care? Aug. 25,
2015, available at www.datasciencecentral.com/m/blogpost?id=
6448529%3ABlogPost%3A316182
2. See http://quickfacts.census.gov.
3. See, e.g., http://genuineprogress.net; www.census.gov/economic-indicators/; and the Wall Street Journal’s Economic Indicators
Archive.
4. See Ben Kerschberg, Business Intelligence and Legal Matter Management, FORBES, April 26, 2011, available at www.forbes.com/sites/
benkerschberg/2011/04/26/business-intelligence-and-legal-
matter-management/
5. Diann Daniel, How a Global Law Firm Used Business Intelligence to Fix
Customer Billing Woes, Jan. 8, 2008, available at www.cio.com/
article/2437361/business-intelligence/how-a-global-law-firm-used-
business-intelligence-to-fix-customer-billing-woes.html.
6. Rita L. Sallam et al., Magic Quadrant for Business Intelligence and
Analytics Platforms, Feb. 23, 2015, available at https://www.gartner.
com/doc/2989518/magic-quadrant-business-intelligence-analytics.
7. Larry Alton, 5 Industries that Need Big Data, July 24, 2014,
available at http://www.datasciencecentral.com/profiles/blogs/
5-industries-that-need-big-data.
8. William Vorhies, Big Data Falls off the Hype Cycle, Aug. 17, 2015,
available at http://www.datasciencecentral.com/profiles/blogs/
big-data-falls-off-the-hype-cycle.
DATA VISUALIZATION EXAMPLE
