Getting to the Root: How Advanced Analytics Can Uncover Cost Drivers for Legal Matters. August 16, 2017

Transcription

1 Getting to the Root: How Advanced Analytics Can Uncover Cost Drivers for Legal Matters August 16, 2017

2 AGENDA Forecasting Legal Outcomes Data Science applied to ediscovery Costs & Staffing Advanced Analytics in Matter Budgeting and Pricing Wrap-up

3 FORECASTING LEGAL OUTCOMES

4 inform strategy clearer understanding of risk mid-stream adjustments WHY FORECAST? precise reserve-setting better source work both internally and externally rigorous optimization based on valuation + pricing

5 Predictive Descriptive Analytics Summarizes past data Conveys where things are Enhances awareness Provides insight into pattern, trend, and distribution First step toward added management and control Descriptive Diagnostic Diagnostic Analytics Gives insight into why things may have happened Connection to likely causation is key Often cutting data to show what happened in a particular scenario to back into reason for occurrence or non-occurrence Predictive Analytics Focuses on determining future outcomes based on prior data/scenarios Used to help anticipate sales, conversion, assess risk, and make other prognostications based upon relationships in prior data

6 everyone naturally wants to play here... Predictive

7 count legal things

8 Examples of Practice Data Transactional Deal size Core industry Cycle time Transaction type Difference between closing date and target date Result after 6 months Result after 2 years Status Substantive issues/ancillary work-streams Litigation Matter type Claim type Jurisdiction Outcome Exposure amount Settlement amount Cycle time Business line Closing status

9 PROGRESS Avg. Litigation Payout by Business Unit Power Oil Water Capital Research Descriptive This is really high compared to the others Diagnostic Why am I paying this much compared to the others? Predictive What should I expect to pay in the future?

10 modeling Base + $2.3k (Manufacturing Defect) $6.3k + $4.2k per case (<= 6 cases) + $3.1k (Yes) + $0.40 per $1 claimed - $7k (Design Defect) - $1.6k per case (7 < cases) - $4.4k (No) $10k Claim Type Filed in Unfriendly Jurisdiction Repeat Plaintiff s Counsel Conservative Appointed Judge Damages Claimed

11 HOW?

12 skills Math & Statistics Knowledge Counting Statistical Modeling Bayesian Inference Machine Learning Natural Language Processing Fluency with Tools R Python Database: SQL (& NoSQL if possible) Data Solutions Architect Legal Industry Context Experience in Private Practice or Corporate Legal Knowledge of Legal Business Problems Familiarity with Industry Technologies Communication & DataViz Consulting Skills Knowledge of Viz Best Practices

13 deep projects

14 creatively engage lawyers

15 take things apart

16 solicit your data model

17 collect the data

18 BUT NOBODY IS COUNTING

19 90% OF THE WORK IS COLLECTION + CLEANUP Less can be more Data exhaust is always preferred, but may not be possible for certain points of the process/facts/scenarios Understand who will collect, tie it back to process Change management issues here - find ways to encourage and reward teams that collect good data Show it

20 sunlight can be the best disinfectant

21 ML to fill gaps Base + $2.3k (Manufacturing Defect) $6.3k + $4.2k per case (<= 6 cases) + $3.1k (Yes) + $0.40 per $1 claimed - $7k (Design Defect) - $1.6k per case (7 < cases) - $4.4k (No) $10k [ ] Claim Type Filed in Unfriendly Jurisdiction Repeat Plaintiff s Counsel Conservative Appointed Judge Damages Claimed

22 generate hypotheses about relationships in relevant data... Very few know that adequate algorithms can be developed without any outcome data at all and with input from information on only a small number of cases. Daniel Kahneman Noise: How to Overcome the High, Hidden Cost of Inconsistent Decision Making, Harvard Business Review, October 2016

23 ... and don t be deterred by imperfect data When I talk about imperfect data, what I mean is, people s notions about the type of data required to produce value from analytics is often way too restrictive. Jim Guszcza You Don t Need Perfect Data for Analytics Wall Street Journal, February 2016

24 FORECASTING E-DISCOVERY COSTS AND STAFFING

25 PLACEHOLDER Lockett Slides TBA

26 FORECASTING PRICE AND COST

27 ClientSync and Compass Drive Data Analytics at McGuireWoods In response to client demand and industry developments, in 2014, McGuireWoods created ClientSync, an attorney-led legal project management program. The need to capture, harvest and analyze data about legal spend quickly emerged as a key priority so the firm simultaneously developed Compass, a proprietary budgeting and tracking tool, which became available (but not mandatory) for all attorneys. Compass derives task-based data from the firm s timekeeping system and tracks it against a budget or fixed fee arrangement.

28 Evolution of Data Driven Policies and Developments xxss September August 2016 January 2017 April ClientSync team worked with attorneys in all 18 departments (both litigation and corporate) to develop McGuireWoods standardized task codes/lists based on legal service type. All new matters are required to use task codes (either MW standardized lists or client-required) which are selected at file opening and are automatically put into Compass (with default budgets). Budgets became required for specific types of matters, including certain alternative fee arrangements. Data Analytics Manager, Tim Golden, joined the ClientSync team to enhance data analytics capabilities.

29 What do Lawyers Want? Amount of time it takes per task to perform legal services Timekeeper information, including leverage Identifiable traits about legal matters (matter profiling) Duration of a matter Location of parties and disputes Type of resolution per jurisdiction Information about opposing counsel

30 Lessons Learned Focus on small improvements, changes and wins Listen to your audience and identify priorities Firm management may surprise you! Be disciplined in your approach but be willing to be flexible Data analytics is an evolving process Above all else, maintain data quality at all costs

31 RECAP