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Practice Innovations —; Managing in a changing legal environment
Gray Rule
October 2015 | VOLUME 16, NUMBER 4
Gray Rule
Why Lawyers Should Care about Predictive Analytics
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IN THIS ISSUE:
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»Why Lawyers Should Care about Predictive Analytics
»Managing the Intelligent Global Expansion of Research Services at Squire Patton Boggs LLP
»(Probably) No Longer Asking Which Comes First – Project Management or Process Improvement
»Making Project Management Work Better: Earned Value Management for Law Firms
»A Day in the Life of a Pricing Professional
»Measure Better to Manage Better—Part 2
»Back to Contents

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Why Lawyers Should Care about Predictive AnalyticsJohn Hokkanen, Risk Programmer/Analyst Contractor at First Hawaiian Bank, Honolulu, HI
We are now in the era of inexpensive data. Amazing data repositories exist, and technology can automate the capture of real-time data. Instead of simply generating reports that summarize data, businesses now develop algorithms, capable of predicting things including the future.

Corporate America is trending towards analytical decision-making. This style of management means that one collects detailed data and analyzes it to support all decisions. In the past, that has involved historical data and metrics. Organizations create reports (e.g., billed hours, active projects) and reporting metrics (e.g., profits per partner). Having vast, diverse data warehouses allows the asking of interesting questions like "Do associates with laptops bill more hours than associates without laptops?" The answer to these sorts of questions informs decision makers and helps to ensure profitable outcomes, like whether or not to issue laptops.

We are now in the era of inexpensive data. Amazing data repositories exist (e.g., data.gov, financial data, social media), and technology can automate the capture of real-time data. Consequently, we can collect and polish tremendous data assets, store the data inexpensively, and crunch the data with more powerful computers running analytical software. Instead of simply generating reports that summarize data, businesses now develop algorithms that help see into the future. For one business, that may be adjusting the price of an airline route on a particular day to maximize yield, and for another business, it might be determining which consumers may be most receptive to a direct mail offer to minimize cost. Seeing the future, even if it is very blurry, has obvious advantages to having perfect hindsight. One can iterate and develop a better and better vision. It is safe to say, though beyond the scope of this article, that a giant wave of productivity, value, and new opportunities will come from the predictive exploitation of data.

For a law firm, the benefits of developing and using predictive analytics now rather than years from now are both strategic and tactical. Many of the most important benefits are strategic; for example, if one accepts that some of the most valuable client opportunities of the not-so-distant future lie with companies that exploit predictive analytics, then it is obvious why understanding these technologies will be a huge asset. These technologies may affect multiple areas of law—intellectual property, privacy, labor—and a firm may benefit by being able to talk the talk and market its expertise.

Building expertise slowly in a key technology means a firm can painlessly and wisely assimilate the technology it uses on "low hanging fruit" projects. For example, one might develop a dashboard of key leading metrics for managing a big case or understanding opportunities in an industry. The point is that a real benefit arises from the increasing capacity of the firm to use these tools rather than the specific ROI that one obtains from the first project. Learning what not to do is often the basis of best practices.

We learn the details of how to make these projects happen by doing, thus giving a strategic capability to those who have done them. For example, let's take a problem that a lot of large firms have: hiring and retention. What data and processes would it take to try to reduce the cost of hiring and increase the length of employment? What data does the firm have that might yield insight into that question? Does the data need to be summarized or transformed? A relationship observed appears to make no sense; is it possible that something hidden is occurring that explains the relationship? Why does a combination of data elements act together as a leading indicator? What data is missing and how can the firm acquire the information? Finally, how good are the predictions? How can they be used and how can they be improved? As the organization's assets and skills evolve, it can tackle bigger, more valuable questions. Prescience is a valuable strategic tool, but it is earned.

In summary, for law firm leaders with vision, predictive analytics is a prescient technology that should not be ignored. There are numerous areas where these sorts of approaches might be applied for immediate gain and to add dollars to the bottom line. A few examples will illustrate the point.

Marketing

Retailers want to predict who will be receptive to marketing, and they run predictions and tests. This allows them to optimize the selection of whom they approach and how they approach them. Would any of your practice groups want to know the same thing? How do you fund and assess your marketing efforts? This is the science of marketing.

Mining Social Feeds

Lots of clients and potential clients are on social media. What might one learn from social data? This could be anything from identifying infringement on existing clients' intellectual property or recognizing an entrepreneur who has the next hot thing and needs corporate representation.

Labor/Human Resources

How might you use these technologies to generate insights and provide guidance on recruiting, staffing levels, technology utilization, and administrative costs? How might one's knowledge of these allow you to provide guidance to other companies on privacy and labor issues?

Long Term Client Relationships

A litigation firm that handles (or coordinates) a large number of routine claims may point the data-mining engine at all of these cases. It is hard to know what insights might be obtained, especially if one could obtain the corporate records of the nonsuits. Which variables predict the filing of a lawsuit? Could this guide corporate policy on how to handle a situation? Which factors influence a settlement?

Industry Dashboards

Which key leading indicators predict volumes for your various practices? Do you have an analytical prediction method? If you tried to develop such leading indicators, what might you learn? What do you know about the interconnectedness of the people networks known by your firm?

You should not be embarrassed if some of these are difficult questions to answer. You might even conclude that your future business is based mostly on the personality and networks of key partners. If so, then maybe the next tier of partners might benefit not by simply replicating partners with exceptional marketing skills but by supporting some forays into new approaches fueled by predictive analytics.

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