Law Librarians are Data Specialists

Reposted with permission from AALL Spectrum, Volume 26, Number 2 (November/December 2021), pgs 42-43.

By Diana Koppang, Director of Research & Competitive Intelligence, Neal, Gerber & Eisenberg LLP

To continue to lead, librarians must build on their existing expertise by gaining data science fluency and proficiency with new data-driven tools.

In the 2021 AALL State of the Profession report, 52 percent of private law library respondents stated that they did not have an AI/Machine Learning Initiative and had no plans to start one. I may have been among those 52 percent (honestly, I can’t remember that far back). If so, then I too fell into the common habit of downplaying my technical expertise as a librarian. We must stop doing that. 

Law librarians have been among the lead users of artificial intelligence (AI) and machine learning technology in law firms since the advent of this technology in law ‑rms. Early machine learning in legal tech appeared in legal research platforms and e-discovery software. It’s only recently been expanding into the fields of process optimization, contract review clause analytics, and other knowledge management solutions. So, because librarians are often not part of those new initiatives (even though we likely should be) we think we are not promoting advanced technology within our organizations. But we have been promoting it—and at times necessarily pointing out the flaws in developing tech. 

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Ask a Director: Implementing Data Analytics

Reposted with permission from AALL Spectrum, Volume 26, Number 2 (November/December 2021), pgs 28-29.

By Patricia Barbone, Director of Library Services, Hughes Hubbard & Reed LLP and John Digilio, Firmwide Director of Library Services, Sidley Austin LLP

Last week, we posted an article from AALL Spectrum on How Data Analytics Can Change the Way Law Firms Do Business, as well as an article highlighting how some law librarians have made use of internal and external data repositories to launch their own data projects, DIY Analytics:  Beyond Excel.   This week’s posts from AALL Spectrum complement those articles.  One illustrates how law library directors and their teams are currently implementing analytics solutions.  The others encourage law librarians to further embrace their data scientist skills and to look into the “black box” of technology, so they can understand and present data and analytics in ways that will best benefit their firms and organizations. 

PATRICIA BARBONE

For the past three to four years, as analytics research tools have proliferated, we have familiarized our lawyers and legal staff with the concept of legal analytics by introducing them to the data analytics features in our existing legal research products. We currently subscribe to many products for litigation and transactional research that contain analytics tools. Some of the most popular products include Bloomberg Law Litigation Analytics, Lexis Context, Lex Machina, and Westlaw Litigation Analytics. For transactional lawyers, we frequently instruct them to use Bloomberg Deal Analytics, Lexis’s Intelligize, and LexisNexis Market Standards.

Analytics training for lawyers has been gradually taking place over the past few years as these tools have increasingly become an integral part of the research platform. Originally, when a database had a data analytics component, it was highlighted in training if it illustrated a typical legal problem that our lawyers were trying to solve but was cumbersome to tackle using traditional research techniques. In our current general orientation, we let lawyers know that analytics research tools may help them get to a better understanding of the legal issue, a better assessment of the strategy, or a better way to retrieve relevant precedents. ­The results will be presented in a tabular or graphical format that provides a different perspective than a list of case citations. We highlight a couple of tools when they begin, but we don’t overstress them because we find a relevant use case is needed for lawyers to fully appreciate the power of analytics. ­Therefore, we showcase the data analytics tools all year round, not just as part of the onboarding process.

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10 Ways Data Science Can Help Law Librarians

Reposted with permission from AALL Spectrum, Volume 25, Number 5 (May/June 2021), pgs. 16-19.

By Sarah Lin, Information Architect & Digital Librarian at RStudio, PBC

As law librarians, many of us scrutinize the data we have access to with Excel and out-of-the-box visualization tools. Whether that data is from docket activity, research databases, websites, or online catalogs, what we have can generally be described as “usage data.” But what one skill set would allow us to do so much more with that data, to better understand and communicate what our users are doing and what they need? Enter, data science.

Broadly speaking, data science brings opportunities to work more quickly and easily with data. It provides better reporting formats by incorporating outside data from various sources, and can even turn text into data that can be displayed visually. Even though legal information isn’t always associated with data, science, or data science, data science skills enable law librarians to do their jobs with greater efficiency. With data science skills, we are able to show new value for our teams and organizations, so it is definitely worth the time invested.

Even in a year when time has been both condensed and stretched (when many of us picked up new hobbies, such as baking), learning to code for just one use case, such as replacing Excel as a data analysis tool, doesn’t make sense. Luckily, data science skills are useful for more than just data manipulation, and learning to code allows you to provide many more use cases than just creating better data visualizations for management. Cooking is a useful metaphor for data science: while it’s completely possible to eat take-out, frozen food, box mixes, and cereal for dinner, you can actually create healthier meals with the right tools, enhanced cooking skills, and a better understanding of ingredients. For example, pre-cut vegetables are available in grocery stores, but a chef ’s knife and some practice allow you to customize any meal you make as well as lower costs. Similarly, while you can do your job with Excel and a commercial tool such as Tableau or PowerBI, learning to do data science opens a window of opportunities to new and improved skills that do more than just create improved graphics for reports or budget projections.

The following 10 data science skills and techniques, along with descriptions of the amazing deliverables that are associated with them, are listed in a progressive skill-building sequence, and they will provide you with a fully stocked data science kitchen. Keep in mind that the examples in this article focus on the R programming language, even though data science can also be done in Python (which has similar and sometimes compatible resources for you to use). The power of data science using R or Python comes from the powerful skills and techniques they enable you to use to transform how you work with data in your day to-day job. It’s time to graduate from Excel and start cooking with gas!

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