Fin (Legal) Tech - Law’s Future from Finance’s Past - Professors Daniel Martin Katz + Michael J Bommarito

Fin (Legal) Tech - Law’s Future from Finance’s Past - Professors Daniel Martin Katz + Michael J Bommarito

Fin (Legal) Tech - Law’s Future from Finance’s Past - Professors Daniel Martin Katz + Michael J Bommarito

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Daniel Martin Katz

February 13, 2017
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  1. daniel martin katz blog | ComputationalLegalStudies.com corp | LexPredict.com law’s

    future from finance’s past page | DanielMartinKatz.com michael j bommarito blog | ComputationalLegalStudies.com corp | LexPredict.com page | bommaritollc.com Fin(Legal)Tech edu | chicago kent college of law edu | university of michigan cscs
  2. Today we only want to talk about one thing …

  3. Today we only want to talk about one thing …

    #Arbitrage
  4. But if we are going to talk about #Arbitrage

  5. Then we need to talk about why we *sometimes* 


    miss obvious opportunities
  6. opportunities that have been right under our noses all along

  7. None
  8. we are all the hero of our own story

  9. we are all in our own filter bubble

  10. We must fight rigidity & impact of our own success

  11. Must get exposed to new ideas (most innovation in law

    started elsewhere)
  12. need to develop a

  13. less law centric view of the world

  14. see what the world is understand what it might become

    #arbitrage the precursor to invention
  15. Three Types of Lawyers (as described by paul lippe)

  16. play “whack-a-mole”, reacting to problems by creating fear and friction

    within organizations and the impression that there is a legal risk around every corner. Mediocre Lawyers
  17. can help clients shape (perhaps distort) external perception of risk.

    Merely Clever Lawyers
  18. design systems that balance risk and improve transparency, helping clients

    correctly price risk internally Great Lawyers
  19. None
  20. Lawyer VALUE PROPOSITION (From the Client’s Perspective) (internal or external

    client)
  21. help price risk / help reduce information asymmetries transactional =

  22. litigation = characterize (predict) risk/exposure shift the expected value of

    a lawsuit help price risk / help reduce information asymmetries transactional =
  23. litigation = characterize (predict) risk/exposure shift the expected value of

    a lawsuit compliance = identify + prevent rogue behavior monitor behavior in (near) real time help price risk / help reduce information asymmetries transactional =
  24. litigation = characterize (predict) risk/exposure shift the expected value of

    a lawsuit compliance = identify + prevent rogue behavior monitor behavior in (near) real time help price risk / help reduce information asymmetries transactional = regulatory = help identify (predict) the decisions of regulators / law makers and the risk associated with various outcomes
  25. focusing every individual and every organization on the places where

    they actually provide a return on investment (ROI)
  26. None
  27. the analogy du jour

  28. law = finance ( insurance as well )

  29. law < > finance many elements in law look like

    finance did 25 - 50 years ago (on the long road from Black-Scholes to algorithmic trading)
  30. this is an extension of this prior talk by mike

    bommarito
  31. Dominant Model in Law expert centered pricing of risk

  32. Dominant Model in Law lots of unintentional self insurance rarely

    (if ever) based upon explicit risk models
  33. Cult of one 
 (or very small # of) person(s)

    thinking drives decisions with serious financial consequences
  34. hard to move to more rigorous models given borderline pathological

    numerophobia among lawyers
  35. Claim: fin(tech) offers lessons for many areas in law

  36. thesis statement: the financialization of the law will be an

    important vector of the next decade(s) in law
  37. The Two Major Branches in #FinTech

  38. The Two Major Branches in #FinTech removing socially meaningless frictions

    (from financial processes)
  39. The Two Major Branches in #FinTech removing socially meaningless frictions

    characterizing (pricing) increasingly 
 exotic 
 forms of risk (from financial processes)
  40. #Fin(Legal)Tech application of those ideas and technologies to a wide

    range of law related spheres including litigation, transactional work and compliance.
  41. None
  42. #FinLegalTech

  43. the path of fin(tech) has in part followed developments in

    artificial intelligence
  44. There has been lots of recent interest in applying artificial

    intelligence to law
  45. None
  46. None
  47. data driven AI rules based AI Competing Orientations in Artificial

    Intelligence
  48. expert systems Computational Law Data Driven Rules Based prediction models

    and methods network analytic methods natural language processing self executing law visual law computable codes
  49. we see a decent amount of rules based AI in

    legal industry
  50. that is actually pretty consistent with path of A.I. in

    general
  51. lots of issues with expert systems and/or rules based A.I.

    (without data or an evolutionary dynamic)
  52. None
  53. Ultimately we are trying to learn the rules / dynamics

    that underlie some class of activity
  54. With that understood we want to be able to mimic

    / predict
  55. None
  56. rules based A.I. data driven A.I. 1980’s, 1990’s, Early 2000’s

    >
  57. rules based A.I. data driven A.I. 1980’s, 1990’s, Early 2000’s

    rules based A.I. data driven A.I. 2005 - Present < > ~
  58. None
  59. A.I. State of the Art

  60. A.I. State of the Art purely data centric

  61. A.I. State of the Art purely data centric augment expert

    forecasts w/ data
  62. iterative data < > rules A.I. State of the Art

    purely data centric augment expert forecasts w/ data
  63. None
  64. fin(tech) is commercial field where huge advances have been made

    in science of prediction
  65. is an emerging field where the tools of predictive analytics

    are finally being employed fin(legal)tech
  66. some PUBLIC examples (many more proprietary examples)

  67. None
  68. Here are just a few predictions that we are trying

    to accomplish in law
  69. #Predict Relevant Documents #Predict Case Outcomes Data Driven Legal Underwriting

    Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rouge Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work #Predict Regulatory Outcomes Data Driven Lobbying, etc.
  70. None
  71. There are 3 Known Ways to Predict Something fin(tech) Borrowing

    in part from
  72. Experts, Crowds, Algorithms

  73. example from our own work

  74. predicting the decisions of the Supreme Court of the United

    States #SCOTUS
  75. None
  76. Experts

  77. Columbia Law Review October, 2004 Theodore W. Ruger, Pauline T.

    Kim, Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project:
  78. experts

  79. Case Level Prediction Justice Level Prediction 67.4% experts 58% experts

    From the 68 Included Cases for the 2002-2003 Supreme Court Term
  80. these experts probably overfit

  81. they fit to the noise and not the signal

  82. None
  83. if this were finance this would be trading worse than

    S&P500
  84. #BuffetChallenge

  85. #NoiseTrading

  86. law is full of 
 noise predictors …

  87. we need to evaluate experts and somehow benchmark their expertise

  88. from a pure forecasting standpoint

  89. the best known SCOTUS predictor is

  90. None
  91. the law version of superforecasting

  92. None
  93. Crowds

  94. crowds

  95. None
  96. None
  97. None
  98. https://fantasyscotus.lexpredict.com/case/list/ We can generate Crowd Sourced Predictions

  99. however, not all members of crowd are made equal

  100. we maintain a ‘supercrowd’ which is the top n% of

    predictors up to time t-1
  101. the ‘supercrowd’ outperforms the overall crowd (and also the best

    single player)
  102. as of May 16, 2016

  103. not enough crowd based decision making in institutions

  104. None
  105. “Software developers were asked on two separate days to estimate

    the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently. Judgments made by different people are even more likely to diverge.”
  106. here is our commercial offering

  107. design to unlock untapped expertise in organizations #Winning

  108. Allowing for Frictionless Crowdsourcing #ManualUnderwriting

  109. Allowing for Easy Data Aggregation #KeepingScore

  110. https://lexsemble.com/

  111. https://lexsemble.com/

  112. None
  113. Brief Aside About the Power of Crowd Sourced Prediction #LegalCrowdSourcing

  114. “FantasyJustice predicted the long-shot appointment of Neil Gorsuch for the

    U.S. Supreme Court! “On November 20th, less than two weeks after the election, FantasyJustice predicted the Gorsuch appointment,” wrote our colleague, Michael Bommarito on the LexPredict blog. “And except for a few brief hours on November 23rd, Gorsuch never fell from that lead.
  115. None
  116. None
  117. Algorithms

  118. Columbia Law Review October, 2004 Theodore W. Ruger, Pauline T.

    Kim, Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project:
  119. Ruger, et al (2004) relied upon Brieman(1984)

  120. Ruger, et al (2004) relied upon Brieman(1984) i.e. a single

    tree (as shown below)
  121. None
  122. Leo Brieman moved away from CART in Brieman (2001)

  123. Breiman, L.(2001). Random forests. Machine learning, 45(1), 5-32. Published in

    Machine Learning (A Springer Science Journal)
  124. One well-known problem with standard classification trees is their tendency

    toward overfitting
  125. This is because standard decision trees are weak learners

  126. Random forest is an approach to aggregate weak learners into

    collective strong learners (think of it as statistical crowd sourcing)
  127. Random Forest: Group of Decision Trees Outperforms and is more

    Robust (less likely to overfit) than a Single Decision Tree
  128. http://machinelearning202.pbworks.com/w/file/fetch/37597425/ performanceCompSupervisedLearning-caruana.pdf Random Forest (particularly with special config/ optimization) have

    proven to be unreasonably effective
  129. Our algorithm is a special version of random forest https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244

    available at
  130. None
  131. we have developed an algorithm that we call {Marshall}+ ~

    random forest
  132. Benchmarking since 1953 + Using only data available prior to

    the decision Mean Court Direction [FE] Mean Court Direction 10 [FE] Mean Court Direction Issue [FE] Mean Court Direction Issue 10 [FE] Mean Court Direction Petitioner [FE] Mean Court Direction Petitioner 10 [FE] Mean Court Direction Respondent [FE] Mean Court Direction Respondent 10 [FE] Mean Court Direction Circuit Origin [FE] Mean Court Direction Circuit Origin 10 [FE] Mean Court Direction Circuit Source [FE] Mean Court Direction Circuit Source 10 [FE] Difference Justice Court Direction [FE] Abs. Difference Justice Court Direction [FE] Difference Justice Court Direction Issue [FE] Abs. Difference Justice Court Direction Issue [FE] Z Score Difference Justice Court Direction Issue [FE] Difference Justice Court Direction Petitioner [FE] Abs. Difference Justice Court Direction Petitioner [FE] Difference Justice Court Direction Respondent [FE] Abs. Difference Justice Court Direction Respondent [FE] Z Score Justice Court Direction Difference [FE] Justice Lower Court Direction Difference [FE] Justice Lower Court Direction Abs. Difference [FE] Justice Lower Court Direction Z Score [FE] Z Score Justice Lower Court Direction Difference [FE] Agreement of Justice with Majority [FE] Agreement of Justice with Majority 10 [FE] Difference Court and Lower Ct Direction [FE] Abs. Difference Court and Lower Ct Direction [FE] Z-Score Difference Court and Lower Ct Direction [FE] Z-Score Abs. Difference Court and Lower Ct Direction [FE] Justice [S] Justice Gender [FE] Is Chief [FE] Party President [FE] Natural Court [S] Segal Cover Score [SC] Year of Birth [FE] Mean Lower Court Direction Circuit Source [FE] Mean Lower Court Direction Circuit Source 10 [FE] Mean Lower Court Direction Issue [FE] Mean Lower Court Direction Issue 10 [FE] Mean Lower Court Direction Petitioner [FE] Mean Lower Court Direction Petitioner 10 [FE] Mean Lower Court Direction Respondent [FE] Mean Lower Court Direction Respondent 10 [FE] Mean Justice Direction [FE] Mean Justice Direction 10 [FE] Mean Justice Direction Z Score [FE] Mean Justice Direction Petitioner [FE] Mean Justice Direction Petitioner 10 [FE] Mean Justice Direction Respondent [FE] Mean Justice Direction Respondent 10 [FE] Mean Justice Direction for Circuit Origin [FE] Mean Justice Direction for Circuit Origin 10 [FE] Mean Justice Direction for Circuit Source [FE] Mean Justice Direction for Circuit Source 10 [FE] Mean Justice Direction by Issue [FE] Mean Justice Direction by Issue 10 [FE] Mean Justice Direction by Issue Z Score [FE] Admin Action [S] Case Origin [S] Case Origin Circuit [S] Case Source [S] Case Source Circuit [S] Law Type [S] Lower Court Disposition Direction [S] Lower Court Disposition [S] Lower Court Disagreement [S] Issue [S] Issue Area [S] Jurisdiction Manner [S] Month Argument [FE] Month Decision [FE] Petitioner [S] Petitioner Binned [FE] Respondent [S] Respondent Binned [FE] Cert Reason [S] Mean Agreement Level of Current Court [FE] Std. Dev. of Agreement Level of Current Court [FE] Mean Current Court Direction Circuit Origin [FE] Std. Dev. Current Court Direction Circuit Origin [FE] Mean Current Court Direction Circuit Source [FE] Std. Dev. Current Court Direction Circuit Source [FE] Mean Current Court Direction Issue [FE] Z-Score Current Court Direction Issue [FE] Std. Dev. Current Court Direction Issue [FE] Mean Current Court Direction [FE] Std. Dev. Current Court Direction [FE] Mean Current Court Direction Petitioner [FE] Std. Dev. Current Court Direction Petitioner [FE] Mean Current Court Direction Respondent [FE] Std. Dev. Current Court Direction Respondent [FE] 0.00781 0.00205 0.00283 0.00604 0.00764 0.00971 0.00793 TOTAL 0.04403 Justice and Court Background Information Case Information 0.00978 0.00971 0.00845 0.00953 0.01015 0.01370 0.01190 0.01125 0.00706 0.01541 0.01469 0.00595 0.02014 0.01349 0.01406 0.01199 0.01490 0.01179 0.01408 TOTAL 0.22814 Overall Historic Supreme Court Trends 0.00988 0.01997 0.01546 0.00938 0.00863 0.00904 0.00875 0.00925 0.00791 0.00864 0.00951 0.01017 TOTAL 0.12663 Lower Court Trends 0.00962 0.01017 0.01334 0.00933 0.00949 0.00874 0.00973 0.00900 TOTAL 0.07946 0.00955 0.00936 0.00789 0.00850 0.00945 0.01021 0.01469 0.00832 0.01266 0.00918 0.00942 0.00863 0.00894 0.00882 0.00888 Current Supreme Court Trends TOTAL 0.14456 Individual Supreme Court Justice Trends 0.01248 0.01530 0.00826 0.00732 0.01027 0.00724 0.01030 0.00792 0.00945 0.00891 0.00970 0.01881 0.00950 0.00771 TOTAL 0.14323 0.01210 0.00929 0.01167 0.00968 0.01055 0.00705 0.00708 0.00690 0.00699 0.01280 0.01922 0.02494 0.01126 0.00992 0.00866 0.01483 0.01522 0.01199 0.01217 0.01150 TOTAL 0.23391 Differences in Trends
  133. Total Cases Predicted Total Votes Predicted 7,700 68,964

  134. Justice Prediction Case Prediction 70.9% accuracy 69.6% accuracy From 1953

    - 2014
  135. None
  136. If You Want a Taste of How the Algorithm Works

  137. Quantitative Methods for Lawyers

  138. http://www.quantitativemethodsclass.com/ Professor Daniel Martin Katz Intro Class

  139. Legal Analytics Professor Daniel Martin Katz Professor Michael J Bommarito

    II
  140. http://www.legalanalyticscourse.com/ Professor Daniel Martin Katz Professor Michael J. Bommarito II

    Advanced Class
  141. None
  142. Experts, Crowds, Algorithms

  143. For most problems ... ensembles of these streams outperform any

    single stream
  144. Humans + Machines

  145. Humans + Machines >

  146. Humans + Machines Humans or Machines >

  147. question is how to assemble such streams for particular problems

  148. so that we are not required to rely exclusively on

    experts
  149. law is a field dominated by individual human experts

  150. in most fields - significant quality improvements have been made

    by moving from experts to ensembles
  151. Ensembles come in various forms

  152. Here is a well known example

  153. Poll Aggregation is one form of ensemble where the learning

    question is to determine how much weight (if any) to assign to each individual poll
  154. poll weighting

  155. it does not always get it right aggregation of similar

    biased signals does not work
  156. Must Compare to Alternative Methods Dont compare to perfection

  157. A Visual Depiction of How to build an ensemble method

    in our judicial prediction example
  158. expert forecast crowd forecast learning problem is to discover how

    to blend streams of intelligence algorithm forecast ensemble method ensemble model
  159. expert forecast crowd forecast learning problem is to discover how

    to blend streams of intelligence algorithm forecast ensemble method ensemble model via back testing we can learn the weights to apply for particular problems
  160. None
  161. Given our ability to offer forecasts of judicial outcomes, we

    wondered if this information could inform an event driven trading strategy ?
  162. Paper Released August 24, 2015 http://arxiv.org/abs/1508.05751 available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726

  163. None
  164. None
  165. None
  166. None
  167. lots of litigation decisions are just a version of this

    basic idea law = finance
  168. lots of litigation decisions are actually implicit litigation finance (or

    self insurance) #fin(legal)tech
  169. None
  170. of course the most well known fin(legal)tech is current litigation

    finance industry
  171. funding sources includes: institutional crowdfunding competitive funding platform

  172. institutional www.burfordcapital.com/ http://www.gerchenkeller.com/ http://www.fulbrookmanagement.com/ http://www.longfordcapital.com/ http://www.benthamimf.com/

  173. competitive funding platform think of it as https://mighty.com/

  174. https://www.lexshares.com/ crowdfunding

  175. None
  176. are these funding decisions:

  177. impressionistic investing ? are these funding decisions:

  178. are these funding decisions: impressionistic investing ? or based on

    real underwriting ?
  179. impressionistic investing ?

  180. a lack of real transparency allows for massive returns lit(fin)

    [today]
  181. greater transparency reduces margins + requires better underwriting lit(fin) [future]

    +
  182. better underwriting requires leveraging experts + crowds + algorithms to

    allow for better predictions
  183. None
  184. fin(legal)tech pricing as

  185. hourly rates alternative fees vs.

  186. this is a really finance/insurance question

  187. Who should bear the cost associated with an overrun?

  188. As clients demand a shift toward AFA’s

  189. Question is how to rigorously underwrite / predict costs of

    matters?
  190. Weak Lawyer: Asserts dimensions along which their case is a

    special snowflake
  191. Strong Lawyer - Starts Embracing

  192. #LegalAnalytics #LegalData #PredictionScience #fin(legal)tech

  193. None
  194. #fin(legal)tech law firm pricing goal

  195. it is *not* predicting cost of this particular matter where

    n=1
  196. correctly characterize the distributional properties of a portfolio of matters

  197. including identification of outliers both + and -

  198. apply portfolio theory

  199. to take n=1 and scale to n=many #fin(legal)tech

  200. #fin(legal)tech #self insurance today this is how you would run

    a more rigorous version of
  201. AIG to Launch Data- Driven Legal Ops Business in 2016

    https://bol.bna.com/aig-to- launch-data-driven-legal- ops-business-in-2016/
  202. #fin(legal)tech tomorrow? learn from legal ops service offering to build

    a commercial insurance product offering legal cost insurance ? other exotic insurance offerings?
  203. #fin(legal)tech In such a world, Law Firm is *not* interfacing

    with client but rather insurance company regarding fees
  204. None
  205. fin(legal)tech Transactional Work as

  206. we just discussed price of lawyers

  207. now lets think about transactional value

  208. Meet Bob

  209. Meet Bob lawyer on a major corporate transaction

  210. Meet Bob Bob is about to engage in yet another

    round of markup on deal terms lawyer on a major corporate transaction
  211. Meet Bob Bob is about to engage in yet another

    round of markup on deal terms this round is likely to generate a delay on the expected close of the deal lawyer on a major corporate transaction
  212. None
  213. how much value is created by these modifications? how much

    delay will be introduced? vs.
  214. Need a better understanding of the actual drivers of risk

  215. Being able to compute the change in risk as a

    function of a change in deal terms
  216. None
  217. Outside of M+A Requires Mapping of Deal Terms to actual

    substantive outcomes #legaldata #legalanalytics
  218. this is particularly important when non-lawyers are doing the negotiation

    (for example your global sales force)
  219. None
  220. fin(tech) fin(legal)tech vs. Additional Lessons

  221. fin(tech) is commercial field where there have have been huge

    advances in working with unstructured data
  222. 80%+ of the world’s data is unstructured data

  223. in a variety of ways fin(tech) has already confronted this

  224. Solution is to either let tech or human process that

    data
  225. And humans are actually pretty good pattern detectors

  226. But only for certain types of problems

  227. Trading (HFT in particular) is about looking for anomalies 60

    Seconds of HFT
  228. None
  229. Two Relevant Examples of Anomaly Detection in Law

  230. example 1

  231. the discovery + compliance convergence

  232. None
  233. None
  234. None
  235. None
  236. a hard #bigdata problem in law (near real time) compliance

    FCPA, Product Defect, etc.
  237. the goal is near real time monitoring

  238. defect w/5 ‘airbag’ version 1.0 backdate w/5 ‘option’ etc.

  239. near real time monitoring of version 2.0 a massive volume

    of communications
  240. None
  241. Behavior will change But Behavior Change will lag (i.e. rogue

    action will be done offline) (i.e. folks will craft incriminating communications at least for a while) Corp Security Beginning to mirror today’s NSA
  242. thus, discovery (in part) becomes compliance and some (only some)

    litigation is avoided legal standards will still shift real time monitoring will generate lots of false positives
  243. None
  244. example 2

  245. we all have a tell

  246. lots of efforts to trade on sentiment lessons from fin(tech)

  247. efforts to trade on the sentiment contained in these and

    other related documents
  248. sentiment analysis

  249. sentiment analysis

  250. understanding your opponents or other key decision makers tells (and

    your own)
  251. legal sentiment analysis a new source of competitive legal intelligence

  252. None
  253. combating complexity through information mgmt. lessons from fin(tech)

  254. Information Management is a significant problem in legal

  255. data that could inform operations is not collected / or

    not regularized
  256. information necessary to undertake due diligence or other regulatory exercises

    is locked in an antiquated format (i.e. pdf, word, tif file)
  257. Dodd-Frank RRP for SIFI’s (Systemically Important Financial Institution) EXAMPLE:

  258. Resolution & Recovery Plans are Living Wills for Banks

  259. “The living will is effectively a roadmap and simulation of

    the largest possible series of transactions in a bank’s lifetime, the type of analytical exercise that is common in electronic systems design or software testing, but unprecedented in law.”
  260. Ideal RRP is a ‘War Game’ whereby a SIFI demonstrates

    it is robust to failure of various counterparties
  261. but requires review and understanding of the set of agreements

    across all business lines (p&l’s)
  262. problem is legal work product is not a pointable data

    object
  263. None
  264. None
  265. horizontal integration of legal work product in the broader corporate

    technology ecosystem represents a source of immediate value creation
  266. “Watson [and related machine learning technologies] will catalyze better organization

    of legal information and legal data, forcing organizations to better manage their current data and delivering substantial returns from this information management step alone....”
  267. for example - contracts should be born (or processed) as

    computational objects to point straight into finance/acct and other relevant IT systems stored legal work product
  268. sensor data + contracts talking to other contracts

  269. #InternetofContracts which is a special case of the #InternetofLegalThings This

    is the which is a special case of the #InternetofThings #IOT
  270. None
  271. None
  272. None
  273. we are starting a decade(s) long process of overhauling the

    global financial infrastructure
  274. it is a massive friction reduction exercise

  275. Big 4 vs. Big Law who will get to drive

    this agenda? (i will bet on the big 4)
  276. only time will tell …

  277. #fin(tech)

  278. #fin(tech)

  279. #fin(tech)

  280. #fin(tech)

  281. #fin(tech)

  282. #fin(tech)

  283. but blockchain is important bitcoin is probably not that important

  284. None
  285. SOME CONCLUDING IMPLICATIONS

  286. in order to support enterprise quality risk models remove unnecessary

    friction
  287. < > Implication #1

  288. every organization in law needs a data strategy

  289. Capture, Clean, Regularize Data to support a range of tasks

  290. Deploy Data for Specific Enterprise Applications Develop a data roadmap

    http://legaldatastrategy.com/
  291. http://legaldatastrategy.com/

  292. None
  293. < > Implication #2

  294. every organization in law needs a relevant human capital #LegalAnalytics

  295. Either going to need homegrow your own talent

  296. and/or work with organizations who can help (consultants, etc.)

  297. None
  298. Finally, we recently organized 
 this conversation

  299. FinLegalTechConference.com November 4, 2016

  300. Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of

    Law
  301. Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of

    Law
  302. Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of

    Law Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of Law Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of Law
  303. None
  304. Associate Professor of Law Illinois Tech - Chicago Kent Affiliated

    Faculty Stanford CodeX Center for Legal Informatics College of Law Chief Strategy Officer LexPredict
  305. Fellow Stanford CodeX Center for Legal Informatics Adjunct Professor University

    of Michigan Center for Study of Complex Systems Chief Executive Officer LexPredict
  306. LexPredict.com

  307. ComputationalLegalStudies.com BLOG

  308. @ computational

  309. TheLawLab.com

  310. Michael J. Bommarito II @ mjbommar computationallegalstudies.com lexpredict.com bommaritollc.com university

    of michigan center for the study of complex systems @
  311. Daniel Martin Katz @ computational computationallegalstudies.com lexpredict.com danielmartinkatz.com illinois tech

    - chicago kent college of law @