Taking on Data Analytics

Reposted with permission from AALL Spectrum, Volume 26, Number 2 (November/December 2021), pgs 46-48.

By Miram Childs, Director, Law Library of Louisiana Supreme Court; Andre Davison, Research & Information Operations Implementation Manager, Orrick LLP and Scott Vanderlin, Student Services Librarian, University of Chicago D’Angelo Law Library

Data is everywhere. Many law librarians’ job responsibilities increasingly require them to understand and handle data. What advice, recommendations, or tips do you have to help legal information professionals improve their data skills?

ANDRE: Fifteen-plus years ago, mathematician Clive Humby made headlines when he declared that “data is the new oil.” His metaphor explained that “just like oil, in its rawest form, data is almost useless. But when it is refined, it can be turned into something much more valuable.” Firm law librarians have the unique skills and tools to refine and transform data to perform analytics to support the practice and business of law. Data can seem intimidating, but I will offer recommendations that helped me become more acclimated to using and understanding data analytics. 

VOLUNTEER FOR PROJECTS

At my previous firm, our new CIO created a project to revise our budget reporting process. We were previously utilizing an Excel spreadsheet to track our annual budget. He asked me to lead a project where our goal was to transform the invoice data we were collecting into insights we could use in our budget report. In this project, I learned to utilize tools such as Microsoft SharePoint and Power BI to transform a considerable amount of data into a digestible format for our finance committee. I was able to take some courses to help familiarize myself with the products. My willingness to volunteer to lead that project helped me learn new methods and processes to transform large amounts of data into actionable insights.

UTILIZE AALL RESOURCES

­The American Association of Law Libraries (AALL) has produced informative, educational content from our membership to help improve our data skills. An article and webinar presentation by former law firm technical services librarian Sarah Lin provided a comprehensive introduction to data science for law librarians. Th­e webinar “Introduction to Data Science for Law Librarians” listed skills that make up data science, described how data science intersects with law librarianship, and introduced vocabulary and terminology to effectively discuss analytics and data science with internal stakeholders. (Watch the webinar at bit.ly/AALL92420.) She followed the webinar up with an AALL Spectrum article, “10 Ways Data Science Can Help Law Librarians,” that provides use cases and opportunities that allow you to utilize data science to improve efficiency at your organization. (Read the article at bit.ly/MJ21data.) AALL Spectrum and AALL’s eLearning platform have articles and programs that discuss data, data science, and analytics. I encourage you to read the articles, view the webinars, and reach out to the professionals responsible for the content. ­e wonderful thing about our profession is that law librarians are always willing to share knowledge.

PRACTICE MAKES PERFECT

To get better at handling data, you need to practice, practice, practice. When I was younger, a “Practice Makes Perfect” commercial campaign ran during afternoon cartoons. I can still hear the song in my head, and that theme has stuck with me in my professional career. We must continue to practice analyzing data to get the right insights to help our organizations. A great example that stuck with me was the program “Deep Dive: ­The Federal and State Court Analytics Market—Should the Buyer Beware? What’s on the Horizon?” from the 2019 AALL Annual Meeting in Washington, DC. ­The program was an in-depth session that consisted of three panels. ­The first panel consisted of a group of law librarians that presented a controlled comparison test of major federal analytics products that addressed scope, functionality, and usability. ­The session provided a methodology to testing these litigation analytics platforms while also helping law librarians understand the full capabilities of the products. Our team modeled the test with use-case scenarios from my firm to evaluate litigation platforms that we were considering purchasing. Our findings gave our firm valuable insights to make a considerable investment in litigation analytics. Without the practice exercise, we would not have had the insights to make the best decision for our firm. 

MIRIAM: “I’m a librarian, Jim, not a data scientist!” (Apologies to Dr. Leonard “Bones” McCoy.) When I graduated from library school in the early ‘90s, my main goal was to get a serials cataloging job. At the time, I would sometimes hear about librarians getting jobs as “webmasters.” ­is provoked a bit of jealousy because I wondered how they got those jobs with an MLIS and not a computer science degree. I started hearing terms like “metadata,” “Dublin Core,” and “XML.” I finally realized that, as a cataloger, I was dealing with metadata—MARC records—on a daily basis. Metadata was just the new, sexier term. Later, I started hearing terms like “big data,” “coding,” “data analytics,” and “data visualization.” Just like before, I was skeptical that these skills had much to do with my job in Technical Services, until I became a law library director. In the role of director, I need to understand how artificial intelligence (AI) works to interpret and retrieve the information I seek, so that I can understand the results when I provide information to users. I started placing big data, coding, and analytics in the context of AI, machine learning, and the neural network being­ utilized by Westlaw, LexisNexis, Fastcase, and newcomer Casetext. I realized that being ignorant of AI would have a signi­ficant negative impact on my effectiveness as a leader. Meanwhile, schools of library and information science started changing their names (basically dropping “library”) and offering technical classes that would have been unheard of in my day. I began to seek ways to close my knowledge gap and learn more about big data and analytics, and I have found that there are resources to help that don’t require returning to school. Library Carpentry (bit.ly/ND21carpentry) offers free, self-paced courses for those working in library and information-related roles. Their lessons include UNIX shell, OpenRe­fine, Git, webscraping, and Python. Massive Open Online Course platforms (MOOCs) such as Udemy (bit.ly/ND21udemy) and Coursera (bit.ly/ND21coursera) offer a plethora of technical classes, though not all of them are free. Even Amazon offers some free data skills and coding classes (bit.ly/ND21aws). The idea behind these self-paced courses is to give everyone the opportunity to learn a new skill, perhaps go on to become certi­fied, and hopefully ­find a rewarding career. Law librarians can take advantage of these opportunities. You will likely need to use some personal time to complete these courses, and you may not get a pay increase. However, the increase in value that you will bring to your institution will be worth it. Lastly, I’d recommend Amy Affelt’s book Th­e Accidental Data Scientist, which helped me understand that I’m a librarian AND a data scientist.

SCOTT: Thanks, Nate Silver. We were all doing just ­ne using our own eyeballs to tell us how the world worked before you peeked up from your baseball cards, yawned, and used your robots to call an election. Next thing I know, Sam Hinkie is over here explaining how important losing is. Now I have to moneyball my toast in the morning because apparently, I have been buttering it wrong my whole life. Data is the pits.

But also, data is awesome. There, I said it. Understanding advanced analytics and having an ability to harness data has become an integral part of jobs in most industries. And since “data” is fundamentally “information,” it is no wonder that we, as legal information professionals, have increasingly been asked to fi­nd, interpret, and explain data as part of our jobs.

For those who are intimidated by this growing facet within our ­field, it is important to remember that there is a giant gulf between knowing absolutely nothing about data analytics and using R or Stata to perform complex data analysis. Any of us can know enough to be dangerous (or at least to get called a dork behind our backs at a party) without turning into a full-on quant.

My advice would be to start with a program that nearly everyone has access to and interacts with semi-regularly—Excel (or Google Sheets if you prefer). If you have been intimidated in the past by anything more complicated than rows and columns, make it a goal of yours to learn how to competently use basic formulas to add, subtract, or otherwise manipulate data from different cells. Once you have mastered formulas, move on to pivot tables. Once you have pivot tables down, try creating some basic charts or graphs of data from a spreadsheet. Excel is a program that we all interact with more than we realize, but too few take the time to truly understand how powerful it can be if used effectively.

There are countless tools that you can use to brush up on your Excel skills, ranging from paid courses to free resources. A wonderful place to start if you have access through your employer or otherwise is LinkedIn Learning (formerly Lynda.com). LinkedIn Learning provides dozens of quality training videos that will quickly turn you into an Excel pro. If you do not have access to LinkedIn Learning, regular old YouTube can be an incredible resource for ­finding Excel tutorials.

Start small, and before you know it, you will be more familiar with data than you ever thought you would be. And if you still hate the concept, send all complaints to @NateSilver538 on Twitter.

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