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.

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. 

DIY Analytics: Beyond Excel

Reposted with permission from AALL Spectrum, Volume 26, Number 2 (November/December 2021), pgs 12-15.

By Erik Adams, Manager of Library Digital Initiatives, Sidley Austin LLP; Martin Korn, Director of Research and Knowledge Services, Sheppard, Mullin, Richter & Hampton LLP; and Casandra Laskowski, Head of Research, Data & Instruction, University of Arizona College of Law Library

Tips and tools for mastering the basics of statistics and analytics to create your own data project.

Analytics is using math and computers to mine data for insights and knowledge. Many tools are now available that make it possible to do analytics with little more than a basic knowledge of statistics, some data, a personal computer, and the right software. You don’t have to know how to calculate the standard of deviation or have an advanced degree in computer science to do your own analytics. It is not necessary to run surveys to gather data. This article discusses some basic concepts in statistics, where to find data, and which tools to use for manipulating that data. It also makes some recommendations for librarians and legal information professionals on how to get involved in data projects.

But first, what’s wrong with Microsoft Excel? Once you really get serious about analytics, you will encounter a variety of speed bumps that are handled better with other products. Excel has limits on the amount and kinds of data it can import and manipulate. Other products make dealing with large and complex data comparatively easy. Excel’s formulas and macro language are not as expressive or sophisticated as that found in R or Python, which both allow for more options. Similarly, OpenRefine, Power BI, and Tableau make it possible to automate a lot of the drudgery of data preparation and cleanup. Excel may be the de facto product people use to manage and share tabular data, but that does not mean it is the best tool for the job. ere are things that it is very good at, but there are many tasks that are better done with other tools. You could use a hammer to drive in a bolt, but a wrench will do the job better. Similarly, you can do analytics with Excel, but you will be more efficient using other programs.

This article was developed from a program at the 2021 American Association of Law Libraries Virtual Conference. The session had a companion workbook that is still available for download (visit bit.ly/ND21DIYworkbook). The workbook provides a walkthrough of different kinds of analytics, using a fictional data set.

How Data Analytics Can Change the Way Law Firms Do Business

Reposted with permission from AALL Spectrum, Volume 26, Number 2 (November/December 2021), pgs 16-19.

The latest issue of AALL Spectrum, published by the American Association of Law Libraries (AALL), focuses on the increasing use of data analytics in the legal world, and the role information professionals play in making data accessible and beneficial.  Information professionals’ current roles involve helping people gain insight from the data available via various internal and external sources by integrating the data and presenting it in digestible and meaningful formats.  The Spectrum issue examines the use of analytics from different perspectives, including how to employ a DIY approach to analytics; how analytics can help firms innovate, and how best to implement analytics to help ensure adoption and continued use.

By Lisa Mayo, Director of Data Analytics, Ballard Spahr LLP

A recent Law.com article by Dan Clark highlighted a startling finding: “General counsel are increasingly looking for law firms that can collect and deliver data so corporations can improve their decision-making about risks and spending. But they are often frustrated when outside counsel can’t meet these expectations, according to in-house sources.” (Read the article at bit.ly/ND21law.) The article made the dire prediction that if law firms cannot offer digitized data to their clients, they “will likely lose out to their more cutting-edge competition.” Legal service providers are not alone in their need to employ data analytics. Every business, regardless of industry, requires a framework and methodology to quickly interpret data from
multiple sources in order to make sound business decisions.

At Ballard Spahr LLP, data and analytics are on the forefront of much of our modern technology offerings. Unlike many firms, our data and analytics function sits inside our Client Value and Innovation department, where we have some latitude with a research and development budget and the directive to “fail fast” if we determine a proof-of-concept did not meet our needs. Our data management mission statement says in part that we “contribute to the firm’s strategic goals by using innovative technologies, a variety of flexible and adaptive data sources, artificial intelligence/machine learning, and ongoing data literacy education to help redefine the Firm’s internal performance objectives and accountability drivers and transform how the Firm delivers legal services to its clients.” Just 48 words but loaded with meaning and purpose, both for now and in the foreseeable future.

The following are some of the ways Ballard Spahr is using data analytics to better serve its clients:

• INNOVATIVE TECHNOLOGIES– We are using best-in-class data and analytics tools for data preparation, security, dashboard technology, and automation. We are also leveraging big data tools for data analysis and transformation.
• A VARIETY OF FLEXIBLE AND ADAPTIVE DATA SOURCES– Each evening, our automated processes look for new litigation, updates to federal campaign contributions, new federal, state, and local legislation, and municipality data sources. We can also modify our big data analyses to exclude or include client data based on the business need.
• ARTIFICIAL INTELLIGENCE/ MACHINE LEARNING (AI/ML) – Tied closely to our data literacy initiative, we are using AI/ML to translate pages of financial data into meaningful text with observations and actionable recommendations; we can also train ML models to find patterns, trends, and make predictions in any variety of datasets.
• ONGOING DATA LITERACY EDUCATION – Global research and advisory company Gartner classifies data literacy as a “core competency” that entails being able to “read, write, and communicate data ‘in context’ including . . . the ability to describe the use case application and resulting value.” Our data literacy initiative involves training our users to understand the impact of effective-dated information versus period in time data; using filters to exclude anomalous data; and understanding the key financial drivers related to profitability. As Gartner’s recent 2021 Data Analytics Summit mentioned, “Data literacy is the ‘How’ of a data-driven organization; it is the most important skill for the twenty-first century—period!”

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!