Click to enlarge / Match the data-enabled deliverable, service or product to stakeholder needs.

How should legal teams get started with data?  Here’s a prescription, along with a #RealTalk diagnostic.


In Post 066, I shared that law firm and law department leaders often ask me how to get started with data analytics. I also shared that I usually respond by asking about their most important strategic objectives.

For today’s post, I will play doctor and take a cut at a framework to serve as a prescriptive roadmap. The above graphic is the result. It visualizes a practical framework to think structurally about high-value applications of data.


To Design a Value Proposition for Data Analytics, Start with Clear Thinking Around Needs

As with any new idea, tool, service or product, the key to designing a successful value proposition is to think deeply about the needs of the intended end-user.

Decision support, when applied to sufficiently high-stakes contexts in both the business of law (e.g. new business models for legal service delivery) and practice of law (e.g. litigation finance), likely offers the highest probability of generating material economic returns or a strategic leap forward for the sponsoring organization. Of course, identifying the decision opportunity and shaping the analytics approach requires a high threshold of domain knowledge as well as technical expertise across both data engineering and data science competencies — hence the need for a multi-disciplinary team.

Execution support. That said, for the time being, the most widespread data efforts in the legal market probably still focus on fairly familiar products around business intelligence, operational dashboards and financial reporting.  While there is plenty of opportunity here for useful deployment of data-informed decisions and actions, the pace of innovation here is unlikely to hit a drastic inflection point due to the heavy burdens around process improvement and change management for data handling, on an organization-by-organization basis.

Persuasion. Broadly in the world, the art of storytelling has gotten almost as much adoration as data science in recent years, but this is one area that hasn’t gotten much traction in legal. (Let’s see if 2019 brings us a bit more ✨ sparkle.)


Common Complaints, Pain Points & 😱💀 Horror Stories 👻🤡

As with many other topics about new and evolving capabilities, dialogue about data in the legal industry can be confusing and contradictory. There is a material level of hype (Big 📊 Data 🤯 disrupting 🤯 Big ⚖️ Law!!) – paired per usual with the requisite cynicism and skepticism (LOL 😆 lawyers can’t speak data 😖😰🙄).

In the abstract, I 💖 data. Full stop. 😍

Out in the real world, however, my adoration for data has many disclaimers, disclosures, and exceptions — particularly when it comes to the legal industry.  In this section, I address a few of my misgivings about the current state of data usage in the legal vertical, drawing not only on my own experiences but also the most common complaints I hear in my travels.

1. We Have the Data Sets We Deserve (but Not Always the Ones We Need 🦇)

This is often how it starts. Because most legal organizations are in possession of some data assets, and because data-informed or data-driven methods are all the rage these days, many organizations embark on directionless and costly exercises to squeeze insights from the data they already have.

Common scenario, full of traps.

Conversations that begin this way are hugely concerning to me.  What I hear in this exchange is “sure, I’d love to see something interesting or cool because it sounds like it’s free.”  That’s not a cost benefit equation with a high probability of a happy ending, for two reasons:

  1. Overvaluing existing data assets without regard to data quality or sufficiency (more on this a bit)
  2. Underestimating the cost and effort required to extract business value from those assets

On the business of law side, both data sufficiency and data condition are material concerns. A point worth noting here is that much of the existing business data in the legal industry oriented around cost to the buyer: most of the readily available data sets across law firms and law departments tend to come from time-and-billing systems and provide the history of transactions, essentially as accounting events. Of course, these data sets often suffer from material quality issues that require time and money to resolve.

Very few law firms and law departments have comparable depth or volume of data oriented around value delivered. In other words, there are precious few readily available and reliable systems of record that capture the history of legal events through which the service provider created and delivered value to the buyer of legal services. On a per-organization basis, more practice management or experience database solutions are emerging to help create this record, and in very few cases correlate those events directly to billing data. However, in 2018, such efforts are relatively nascent, particularly relative to the time-and-billing systems that have served law firms as their primary mission-critical system for many decades.

In such instances, the upside potential of the project often has a hard ceiling — yet issues with data condition can mount, not just in hard dollar expense but also time lag and overall drag on the organization. Particularly in projects without a sharp focus on the end-goal, those costs and subsequent adverse impact on the organization can be significant: not least, the potential for change fatigue and mounting resistance to future endeavors involving data.

In short, designing an analytics program at scale represents a material investment of organizational resources.  To secure sufficient ROI on such an effort, analytics must meet a higher bar than simply being interesting — analytics must be useful. And the overall utility and value proposition of the data effort should, whenever possible, be articulated at the outset, not while a costly effort is in flight.

2. Mostly Neglected Everywhere: Assessments of Data Sufficiency

My favorite question format on the GMAT is unique to the exam: data sufficiency. (Yes, it is weirdly 🤓🤓🤓 to admit I have a favorite question type on standardized tests. For inquiring minds, I 💕 logic games on the LSAT and analogies on the SAT.)

I provide an example below:

Source: PrepScholarGMAT

For everyone whose eyes glazed over, that’s OK. Data sufficiency enjoys a flavor of notoriety even among the b-school crowd. The question doesn’t directly test for quantitative problem-solving aptitude; the provided problem (in the example above, the ratio of full-time to part-time employees in Division X and Company Z) is usually asinine and beside the point.

Rather, data sufficiency questions test aptitude for metacognition: how to 🤔 think about 🤔 thinking. Correctly answering a data sufficiency question is at its core an exercise in logical reasoning, in addition to a test of content understanding of number properties and statistics. More precisely put, this question format demands rigorous and structured thinking about the underlying method of problem-solving, and most importantly the ability to correctly identify the factual inputs required to generate insights relevant to the problems or objectives at hand.

This represents hard work for our brains that feels unnatural, because the exercise of evaluating data sufficiency requires that we focus on the white space — something beyond reacting to or dealing with the facts and numbers that are right in front of us. This question asks us to think categorically and descriptively about whether we might need facts and numbers that are not readily available.

This is a very specific type of thinking that we don’t often practice in the legal industry. We ought to.

3. Data Sets Have Origin Stories, but We Often Ignore Them

Most data sets don’t simply appear out of thin air via immaculate conception. In some way shape or form, most data sets are generated by people.

In most brick-and-mortar businesses and product-heavy sectors, sensors do much of the work around collecting and gathering data, often unobtrusively in the background. The same is true for high-tech platform plays where data on user behavior turns out to be the core product to be monetized.

That said, the legal services market is still a services-first category, and our industry still relies heavily on manual data inputs. As a result, busy and stressed people are clicking buttons or filling out fields to generate many of our data sets, particularly in intra-organizational initiatives in knowledge management and practice data maintenance. This type of data work demands accuracy and precision, which are two areas where humans tend not to excel. These tasks also tend to comprise the more tedious and soul-crushing components of anyone’s job: data upkeep/maintenance is usually the “one last thing” on the checklist or to do list before quitting time, and on most days, these tasks probably don’t get done. 🤷‍♀️

Particularly because we tend to function in environments that produce low-fidelity data, understanding the means and mechanics of data collection is a critical prerequisite to defensible analysis and interpretation. Too many teams working with data in law firms and law departments function as database (or spreadsheet) administrators, overwhelmed with the mechanics of data collection and cleaning without a serious attempt to engage with the content of the data sets — i.e. what the data says and what it all might mean.


The Root Cause Diagnostic: New & Different Methods Require More & Different Skills (but Not in One New Super-Lawyer)

Out in the real world, using data to actually solve real problems is much 😓 harder and more 😩 effortful than a day of GMAT prep, and the requisite skills are both valuable and rare. The ability to identify the needed inputs and assess the best method of obtaining them are skills we don’t associate directly with data analysis. These upstream competencies in fact-finding and fact collection are skills taught in research, investigation and intelligence work — across academic, enforcement, military and corporate traditions.

In practice, work to assess and achieve data sufficiency tends to look both non-linear and messy, because it is. In addition to a firm grasp on the mechanics of research design, the team must bring to bear some depth of domain knowledge (content understanding of the specific facts and data points relevant to the problem at hand) and environmental familiarity (the ability to navigate the available universe of sources and to assess each information source for reliability). Lastly, this type of work benefits from a few specific attributes: intellectual curiosity, resourcefulness, and tenacity.

Often, generating original and useful insights requires multiple cycles of hypothetical reasoning followed by factual investigation. Formulating smart and specific questions to explore requires creative and open-ended thinking rooted in meaningful understanding of the problem, and actually going out to verify what is happening out in the real world requires a willingness to engage in legwork.

All this is a tall order. In 2011, McKinsey predicted that “by 2018, the U.S. alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”  See “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute, June 2011.  Fast forward to this year: big data and analytics has topped the skill shortage list for the 4th year running in the annual Harvey Nash/KPMG CIO Survey, and two-thirds of IT leaders say this shortage is “preventing them from keeping up with the pace of change.” See “Big Data Skills Shortages – and How to Work Around Them,” Computer Weekly, June 2018.

A 2017 analysis by PwC echoed McKinsey’s 2011 prediction, noting that the overall talent need will be “mostly for business people with analytics skills, not just analysts.” See “What’s Next for the Data Science and Analytics Job Market?,” PwC Research and Insights, January 2017.

The grid above sets forth their recommended skill inventory mapped to jobs across two categories: analytics-enabled business roles and more technical data science roles. This is an instructive model for legal practitioners and legal educators alike: increasingly, there is a need to rethink the boundaries of the legal practitioner role, either as “data-driven (legal) decision makers” or “data-enabled (legal) analysts.”

I particularly favor this PwC model because it sidesteps a couple of familiar traps. Once in a while, Law Twitter revives the debate over whether lawyers should learn to code (strong opinions abound). I’ve never been fond of how this question is framed. Firstly, I’m not sure it’s helpful to debate whether all lawyers should learn to code. Secondly, I think the question lacks nuance and specificity about what level of conceptual literacy or practical prowess “learning to code” would or should comprise. (As for my two cents on the “learn to code” debate, I agree 🙌 with Jason Barnwell’s hilarious tweet below.)

What the PwC skill grid accomplishes in one visual is to remind us that the full complement of analytics skills modern organizations need must be spread across several jobs. Analytics is a team sport. What’s more, the PwC grid also communicates very effectively that each distinct role demands a differing mix of skills.

Click to enlarge / Don’t try this at home alone 😱

Modern law, too, is increasingly a team sport. Legal teams are particularly challenged to field a high-performing team that brings together the full complement of necessary skills, for several reasons. In the current state, the vertical not only has a skills shortage but no real pipeline strategy to attract high-caliber analytics talent. See Post 066 (talent shortage as structural barrier to innovation).  That talent shortage is exacerbated by the extreme fragmentation in the industry. See Post 051 (key graphic). Indeed, as a mental exercise, I’d be willing to lay 10-to-1 odds that there simply aren’t 200 qualified candidates to lead serious data analytics initiatives at each of the Am Law 200 firms.

The industry relies on pockets of brilliance for thought leadership (Dan Katz of Chicago-Kent & LexPredict and Evan Parker of LawyerMetrix come to mind). However, advancing analytics-enabled thinking at scale will require a much larger talent pool and/or a more creative market-making solution to help scale the limited supply of talent to a broader swath of demand.


A New Hope, Always: the Legal Industry Isn’t Actually That Far Behind the Curve

Before I close, though, let’s take a slightly different look at the state of data analytics in legal — because tech products represent one pathway to scaling the promise of analytics on a one-to-many model.  See Susskind, The End of Lawyers? (2010) (introducing one-to-one and one-to-many terminology). Certainly as of late, more and more legal tech and content players are focused on data applications with the potential to arm legal practitioners and business stakeholders to make better legal and business decisions faster: these efforts currently coalesce around natural language processing, from extraction to categorization and reasoning. Some of these component technologies are already baked into mainstream products in legal research and document intelligence categories:

Click to enlarge / Partial view of the Research and Document Intelligence categories

Many of these products are still likely enjoying success in early markets, but both categories seem poised on the cusp of penetrating mainstream markets. The Research category demonstrates an unsurprising level of consolidation given the longstanding oligarchy of incumbent content publishers. The document subcategories are still fairly crowded, particularly in the diligence engine subcategory: whether a clear winner will emerge, it is too early to say.

One last noteworthy point is that the legal vertical may represent a fairly optimal lab environment to test frontier technologies in the more advanced NLP subcategories like machine translation. In that sense, the perpetual notion that the legal industry is perennially behind everyone else may be a bit fatalistic — and signals in the marketplace suggest that there is likely sufficient interest from both capital and the buying market to drive those experiments forward, particularly in the upmarket segment of Big Law:

  • Eigen Technologies, which works closely with both Linklaters and Hogan Lovells, raised a $17.5m Series A round in June of this year, with Goldman Sachs as lead investor. See “Eigen Technologies Raises £13m,” finextra.com, June 11, 2018.
  • Luminance raised a $10m Series A round in late 2017 with Slaughter and May on its investor roster. See “Slaughters Ramps Up Luminance Investment in $10m Round,” The Lawyer, November 29, 2017.

So, is the legal industry really a decade behind everyone else in how we use and consume data to make decisions? While it probably feels that way to many market participants, I take a slightly more positive view. The advance guard is certainly charging forward. As for most of us in the middle of the pack, I’m here to tell you that many of our frustrations and complaints are commonly heard outside the legal industry.

What I do think we need to improve might be our attitudes: more willingness to invest in attracting new technical talent and more attention to developing the talent we already have by way of education and training would probably go a long way.


What’s next?  See Introducing contributor Dan Rodriguez (076)

Photo by Carson Arias via Unsplash / When innovation dreams fade, heads often roll.

Great things don’t just happen.  People make them happen.  So who is actually working on legal innovation (and why haven’t they fixed everything already)?


Innovation is a strange word.  At least, it tends to affect people strangely, particularly in the legal industry.

Of late, eye-rolling 🙄 and face-palming 🤦🏻 are gaining traction as the response du jour.  Despite the growing levels of skepticism in and around the echo chambers and the pockets of battle-weary veterans, the word “innovation” still has some ✨ magic and mystique.  Clients declare publicly that they expect firms to do better, and firms give every assurance that they are trying.  We are inundated daily with press releases: new startups, new initiatives, new partnerships.  Eyes might roll, but the innovation award shows (there are so many 🏅🏆🥇 of them!) must go on.

All of that sound and fury doesn’t come for free.  It takes a great deal of work, by real people in real businesses.  This talent pool, which is limited, should not be taken for granted.

Part I (062) of this series delved into the price tag of legal innovation in the current state of play, borne by both investors and incumbents who fund innovation efforts.  Part II (063) took a role-based view of legal markets and the various inefficiencies innovation teams’ access to buyers and users.  Part III takes a closer look at the talent required to make innovation actually happen, as well as some of the structural barriers that legal innovation teams face in accessing that talent.

Click to enlarge

Over the past year, Legal Evolution has devoted many posts to specific examples of change agents and their efforts in various segments of the market.  Today’s post takes a slightly different approach.  The intent is not to place the spotlight on the individual people doing the pick-and-shovel, block-and-tackle work of legal innovation.

Rather, the aim is to take a structural and functional view of human capital by analyzing its various component parts: (a) the skills, knowledge and experiences that people need to perform at a high level as well as (b) the organizational capabilities, processes and systems required to acquire, develop and retain the right talent as well as deploy them in correctly configured teams to work on the right problems.


This is What a First-Rate Innovation Team Does

Given the extremely fragmented and messy structure of the industry, see Post 051 (key graphic), a navigable discussion on talent will require some table-setting and some structure to guide our thinking.

A. Innovation Is a Team Sport that Requires Specialized Skills

A good starting point for this discussion is to ask, what is the universe of specialized knowledge and skills required to make innovation actually happen?  A quick detour to design thinking theory will be helpful here.  Popularized by both IDEO and the Stanford d.school, the below visual encapsulates the mental model at the heart of design thinking — the overlap between desirability, feasibility, and viability.

Click to enlarge

When all cylinders are firing, innovation teams might hit the sweet spot for a game-changing idea, but such occurrences are rare.  The more consistent value of design thinking discipline is that testing for the above three elements helps to de-risk innovation investments.  For instance, ideas that fail to meet desirability standards can and should be eliminated immediately: if there is no clear pathway to enough customers paying customers who care, even the best idea will die on the vine.

Each element can be expressed as a set of questions to be researched, answered and validated.

1. Desirability — Customer Needs

Desirability is about ensuring that the innovation team is working on a solution with verifiable market demand.  In that sense, desirability is all about understanding the intended customer (both buyers and users).  A thorough effort to validate desirability helps innovation teams sidestep over-investment of finite resources into ideas that sound good but actually aren’t (e.g., a thousand hours spent on an app nobody wants).

Click to enlarge

In IDEO’s parlance, desirability asks whether the contemplated idea “makes sense to people and makes sense for people.”  This framing is useful in a general sense, but for legal innovation team it is helpful add one more note: validation tests for desirability should always be pinned very tightly to a clearly defined market segment.  In that sense, the desirability axis in design thinking maps very neatly to the problem-solution fit (PSF) framework from Lean Startup.  See Post 063 (summarizing problem-solution and product-market fit tests requiring efficient access to users and buyers).

For startups burning down finite cash reserves, this focus is likely to be imposed on them.  But for incumbents such as law firms, maintaining focus and clarity on the intended target market is critical: when incumbents pivot away from their established customer base to an unplanned effort to acquire new clients or enter a new market, they simultaneously lose a significant comparative advantage (superior access to buyers/users) and may face drastically different economics for both R&D and GTM (go-to-market).

2. Feasibility — Tech Advances

Feasibility is about designing a realistic solution, one that the innovation team can reasonably expect to deliver that will work reliably in real-world conditions for actual users.  This also requires continuing focus on customer understanding, but strategic feasibility assessments will consider user needs in tandem with the innovation team’s core capabilities.

Click to enlarge

Particularly within the high-tech sector, the feasibility axis tends to set ambitious and fast-moving targets for innovation teams.  In effect, this establishes a very large solution space in which innovation teams can explore: in IDEO’s iteration, feasibility asks “what is functionally feasible in the foreseeable future?”

Although this broad standard makes sense for technology companies with the core capabilities to push existing tech beyond current limits, legal innovation teams need to exercise more discipline. This is because most legal service providers like law firms and law departments lack the investment appetite required to build and maintain full-stack technology teams internally (although there are always a few exceptions to every rule).  Further, very few teams field the kind of best-in-class technical talent required to push the boundaries of existing technological feasibility. Instead, within the legal vertical, feasibility means stitching together existing technologies into fluid and cohesive solutions.

In addition to feasibility on the innovator’s side of the house, legal innovation teams should also take into consideration the total cost of consumption and execution risks for the customer, including the time, effort, and client-side resources required for implementation.  Feasibility concerns feed directly into design choices: the key is to build something that will work in the real world.  For that reason, ecosystem factors absolutely matter (like compatibility, interoperability, data/content availability).  For example, how will the proposed solution interact with legacy infrastructure and the enterprise environment?  Effective validation for feasibility helps innovation teams eliminate unrealistic pathways more quickly and to allocate finite resources to the most promising pathways to a workable solution.

Lastly, legal innovation teams — particularly those embedded within incumbent organizations — should assess feasibility within the context of the organization’s existing strengths, capabilities and assets.  Inevitably, innovation and improvement efforts will end up stretching an organization’s operational strengths.  However, if the contemplated solution offers very little opportunity to leverage the organization’s current assets and capabilities, it is usually a sign that the innovation team may have designed a very promising solution that would be better built and taken to market by someone else.

3. Viability — Business Value

Viability is about ensuring that the innovation effort fits comfortably with the company’s broader competitive strategy. This requires that the team establish and maintain a very clear understanding of broader market conditions and the company’s current competitive position. The crux of the viability asks whether the proposed solution aligns with and advances the company’s business goals.

Click to enlarge

Validating for viability means testing for alignment: with concurrent strategic objectives, existing market-facing activities from branding to sales and service delivery, and forward-facing plans for how the company intends to compete and win in specific markets.  IDEO’s articulation of the viability axis asks whether the contemplated innovation is “likely to become part of a sustainable business model.” Testing for viability highlights the non-negotiable need for executive sponsorship of innovation initiatives.

Without strong executive sponsorship, innovation teams often lack (a) line of sight into the high-level strategic thinking that guides the company’s decision-making and (b) direct and unfettered access to customers (both buyers and users), where existing relationships and channels are usually managed by teams much closer to existing P&L.  Both factors appear to be more serious barriers for incumbent organizations, where innovation teams and Skunkworks initiatives must compete for resources with existing revenue streams (and much more influential political forces designed to protect the status quo).

However, even product teams in small and relatively young startups can suffer from lack of direct and clear leadership.  Whenever the founder and/or chief executive steps away from direct oversight of product decisions, R&D teams can miss the mark in several ways.  Ideas for new features and isolated product/service improvements don’t always generate sufficient business value for the company.  Potential solutions that were viable for innovation team to prototype may carry hidden costs when inconsistent user skills are introduced, and attractive unit economics may not always scale due to customer-side variations.

B. Innovation in Action: Fact-finding, Decision-making and Execution

Design thinking provides a tractable and accessible framework for the substantive thinking that should inform innovation investments, but we need to think more practically and tangibly about how these questions translate into concrete activities that drive results.  There are many existing frameworks that preach one approach or another, but today’s discussion opts for an inventory of required activities and skills (what’s needed to get the job done) rather than a prescriptive methodology (how to get it done):

Fact-finding.  Much of the design thinking framework above directly pushes for rigorous and wide-ranging efforts to validate desirability/feasibility hypotheses through fact-finding work.  The primary purpose of market and user research in innovation activities is to facilitate insight generation; in turn, this enables evidence-based choices in very early decisions like market selection, opportunity spotting and assessments, and ideation/prototyping toward problem-solution fit.

Decision-making.  In many cases, the core innovation team may not own decision rights over strategic factors like innovation investments, market selection, or product positioning and pricing.  Even so, effective innovation efforts require that the best-informed individuals participate meaningfully in the decision-making process.  For that reason, high-performing innovation teams will be proactive in articulating decision points and shaping clear options for executive decision-makers.  This is critical to facilitate validation of the viability axis, and enabling competencies in specialized communication (e.g., documentation of findings and executive briefing) are nice-to-haves.

Execution.  The concrete skills required for execution vary greatly across specific types of innovation plays.  See Post 063 (categorizing 5 broad types of innovation plays in current legal market).  For startups and new entrants, execution encompasses the build-out and deployment of entire business functions (e.g. marketing, sales, accounting) in the correct sequence to support rapid growth.  Achieving product-market fit for a new offering usually demands experimentation with product/service definition as well as revenue model & pricing design; these jobs demand skills in strategic marketing and sales as well as some level of financial and business analysis.  For incumbents seeking incremental gains in existing business lines or developing new services, general management and operational skills will be important as well as exceptional communication skills to (i) manage stakeholder engagement, (ii) drive cross-functional collaboration, (iii) navigate interdependencies that tend to crop up in highly matrixed organizations.


The Current State of Play: A Talent Diagnostic

Regarding the current state of play, we can ask two broad questions that reveal a lot:

  1. Do (most) legal innovation teams have access to individuals with the necessary skills, knowledge and experience?
  2. Do (most) legal organizations have the necessary capabilities, processes and systems in place to attract, retain, develop and deploy innovation talent?

The answer to both questions circa 2018 is no.  That is your competition: under-resourced and under-skilled.  To rise above the competition, the following is a pretty solid talent checklist for your own innovation team:

  • Who are the individuals that comprise these teams now?  Are they the right people?
  • What are the “necessary” skills, knowledge and experience for our specific innovation agenda?  Do we have (enough of) the right skills, knowledge and experience?
  • How are these teams organized and deployed?  Are they working on the right problems?
  • Are these teams sufficiently supported, funded and guided?
  • To the extent there are gaps in the current state, why do they exist?

A. We Need Candor to Counterbalance the Hype (#RealTalk!)

Each of the above questions merit thoughtful consideration, but they usually get short shrift in most organizations (both in and outside of the legal industry).

Why?  These are difficult questions that make most people feel…. 😟 uncomfortable.  The primary source of discomfort, of course, is the sneaking suspicion 😒 that the answers will be bad.  Completely rational fears 😨 follow: admitting that the current state and our rate of progress are both sub-optimal might make us look bad (e.g. ineffectual, unqualified, inadequate).  The discomfort alone feels bad and demoralizing.  All of this is a reasonable emotional response to a challenging situation.

What is more important is how we decide to act in response to that discomfort.  Too often, organizations practice diligent avoidance of these fundamental questions.  But confronting these anxieties is part and parcel of the organizational resilience required to push meaningful progress forward.  The process of doing so demands tolerance of friction and a willingness to criticize each other, even at the expense of (temporarily) hurt feelings.  This type of culture is difficult to build and maintain anywhere, but it is especially rare in the legal industry.

Within incumbent organizations, cultural norms as well as internal politics and individual incentives push change agents to choose an easier way out.  That easier way, however, demands that we contribute to the hype machine, and in lieu of the emotional discomfort of candor, we take on the emotional labor of managing organizational fragility.

Often, we find valid reasons to put a positive spin on both the pace of change and the magnitude of impact wrought by incremental changes to the status quo.  Positive reinforcement and collective affirmation have their place in leading organizational change, but consistent preference for good feelings over real results comes at the expense of honest assessments about where we stand and how far we have to go.  The byproducts are law firm marketing overreach, underpinned by the lawyer theory of value and reinforced by the law department goat rodeo.  (H/T to the great Casey Flaherty for his entire body of work dissecting the negative impacts and root causes of the current reality distortion field in legal innovation; these links represent only a tiny selection, but they are all highly relevant, endlessly educational and reliably amusing.)

B. The Market-wide Challenge: Serious & Widespread Skill Gap

Obviously, the high-level diagnostic is bad.  Most legal innovation teams suffer from inefficient access to talent with the necessary skills, knowledge and experience, and most legal organizations struggle to attract, retain, develop and deploy innovation talent effectively.  Structurally, there are at least three critical market-wide challenges that conspire to create significant inefficiencies in the talent market: (1) overall market scarcity; (2) rapid shift toward ineffective specialization; (3) insufficient focus on core industry-wide problems.

Simply put, legal innovation is suffering from a serious skill gap.  As harsh as it sounds, high-caliber professionals with the necessary specialized business and technical skills are in short supply.  The recent explosion in demand for innovation talent (e.g., the number of law firms and law departments jumping on the innovation bandwagon) exacerbates the scarcity problem.  The resulting supply-demand imbalance is made even worse by the rapid proliferation of new and highly specialized roles in the legal vertical.

Generally speaking, specialization should be a positive development.  Specialization is a common labor market response to rising complexity, and it often signals increasing sophistication.  In some cases, these newly created roles represent broader participation of allied professionals and the introduction of more and different competencies into the legal vertical.  But many signals suggest that the current trend toward specialization is not all to the good.

In an illustrative example, I share below a sample inventory of required skills to provide data-driven decision support for an Am Law 100 firm.

Click to enlarge

This skills inventory above features three distinct but closely related points of interest.  The first is that this particular combination of skills are purpose-built for a very specific objective: the provision of strategic decision support and execution enablement to drive profitable growth in an Am Law 100 firm.  The second is the sheer number and variety of skills required to accomplish that objective.  The third is that not all of the listed skills are new, even to the legal vertical.  (All three features are signals, albeit subtle ones, that most of us in legal are doing it wrong.)

C. Strategy First, Innovation Second (Otherwise, Shiny Things ⇒ Emotional Labor ⇒ Arrested Development)

I am often asked by law firm and law department leaders on how they should get started with data analytics, and I know that most of them are expecting a much simpler and shorter answer than I can give.  My usual response is to respond with a question of my own: what are the three most important strategic objectives they seek to accomplish in the next two to four years?  In most cases, this brings the conversation to a screeching halt.  Sometimes it’s because the organization’s mid-term strategy hasn’t been clearly articulated, but almost everyone is surprised that this is relevant in any way to the original question about how to implement data analytics.

Without a clear strategy that is being followed with discipline, we are perennially distracted by shiny things. We gravitate toward whatever skill, role or technology is being touted as the new hotness and then search out use cases that are only vaguely relevant.  This is almost always a recipe for disaster and a certain pathway toward ineffective specialization.  Why?  Because high-caliber talent in newly emerging domains like data analytics is expensive (as well they should be).  The investment in that type of talent can only pay off when applied with laser sharp focus on a clearly articulated problem that matters.

In most cases, effective responses to complex problems require creative thinking about how to combine old and new competencies to improve holistic system performance of the organization.  And yet most incumbent players add more complexity when establishing new roles.  Excessive variation in titles and unnecessary separation of new teams from core business functions only serve to exacerbate diseconomies of scale by escalating coordination costs and imposing untenable communication overhead.

Environments that already tend toward the highly bureaucratic and matrixed only become worse.  A second-order effect of this inefficient specialization is the hidden explosion in a different type of emotional labor required to navigate bad systems.

Consider a (very) partial list of recent developments in Big Law: pricing functions residing separately from service delivery teams or existing at odds with the core finance function; the ongoing fuzziness across marketing, communications/PR, business development and account management teams; knowledge management teams that reside in some no man’s land between the library, practice support teams, and the core IT function; “data science” teams straddling a gray area between practice-specific analytics and enhanced BI for the business side. It’s almost unsurprising that equity partners are known to sigh at the mounting overhead and ask in a bewildered tone what all of these people do all day.  In turn, “all of these people” remain at risk for growing disillusionment and accumulating lawyer aversion.

The pull toward ineffective specialization applies fractally to the market as it does to individual organizations.  Our industry is full of duplicated efforts by countless innovation teams in separate organizations that are too leanly staffed and under-resourced to accomplish their stated objectives on their own, leaving the industry stuck in a perpetual state of arrested development.  In many cases, these teams fail to validate the desirability of their solutions and end up expending significant time and effort on the fringes: minor problems in non-core areas of the industry.  This is a shame, because the industry suffers from a handful of fundamental problems that demand a coordinated and inter-organizational response.


Access to Talent: Likely a Slow Burn to Improve, But a Few Potential Pathways

The below graphic identifies three distinct dimensions (law, tech and business) from which legal innovation teams must draw talent, along with a conceptual and relative rating of current-state access for incumbents and new entrants.  Whatever the situational context, innovation teams in the legal vertical are likely to require some mix of skills, knowledge and experience across these three dimensions.  This construct helps break down some of the existing inefficiencies in the talent market and to envision some possible responses.

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A careful examination reveals several clear takeaways:

1. Recovering Lawyers Play Prominent Roles

Not every innovation effort or project will require best-in-class legal talent, but many will require some contribution from lawyers.  Particularly in more ambitious innovation plays to shift traditional one-to-one service delivery models to a one-to-many solution approach, heavier touches from high-caliber practitioners will be critical.  See Susskind, The End of Lawyers? (2010) (introducing one-to-one and one-to-many terminology).  In any whole-product substitution or significant time/labor displacement plays, innovation teams will need to secure subject-matter expertise from experienced practitioners to fully understand customer needs and solution requirements.  In effect, products and solutions for lawyers benefit from lawyer input, particularly in validation and design phases.  That said, the build and go-to-market work will most likely require partnership with co-founders, advisors or strategic partners who bring necessary business and technical competencies to de-risk investment.

  • The upmarket trajectory of ALSPs is a representative example of the opportunity for high-caliber lawyers to lead legal innovation.  The most prominent ALSPs are led by highly pedigreed lawyers with experience in the upper echelons of the Am Law 100: Axiom Executive Chairman Mark Harris hails from Davis Polk; in his previous life, Alex Hamilton of Radiant Law co-Chaired the global Technology Transactions Group at Latham.  The unbundling, disaggregation and reassembly of  increasingly sophisticated tranches of work is a challenge tailor-made for ex-practitioners with depth of experience in both legal buy and service delivery in relevant practice areas.
  • Shift of legal tech toward practice-specific solutions is another opportunity for ex-practitioners to lead the market in spotting innovation opportunities and designing products or platforms that are superior to current market alternatives.  Transaction management platform Doxly is led by Hayley Altman, formerly a corporate and securities lawyer and partner at Ice Miller.  Former Gibson Dunn litigator Alma Asay founded litigation management platform Allegory and served as its CEO until its acquisition by Integreon.

2. Increased Mobility Across Segments and Functional Roles

The uptick in talent mobility is likely to manifest as a continuation of the Big Law diaspora, but recent years have seen more diverse cross-pollination across market segments and individual roles.

  • Law firms and law departments swap business talent.  The recent move by David Cambria from ADM to Baker McKenzie is a headliner example, but there are several prominent example of switch-hitters in the opposite direction.  Before he joined Shell to manage global sourcing and legal operations, Vince Cordo served as the head of pricing at Reed Smith.  Rebecca Benavides, the Director of Legal Business at Microsoft, was previously the Director of Legal Project Management at Norton Rose Fulbright.  Before she was Google’s Director of Legal Operations, Mary O’Carroll reported to the COO of Orrick, where she managed large-scale projects to improve profitability.
  • Lawyers as product evangelists.  Lucy Bassli is another example of a recent high-profile move out of the in-house function.  Formerly an associate general counsel overseeing contracts for Microsoft, Bassli now serves as the Chief Legal Strategist for LawGeex.

3. Agency Model for Specialized Talent

The shortage of high-caliber business and technology talent is not likely to find a quick fix.  The most critical drag here is the non-negotiable need for a threshold level of content understanding and domain knowledge: to drive meaningful advances in the way legal work is done, innovation teams must establish a baseline comprehension of the business context around that legal need.

  • Build versus Buy.  As Josh Kubicki taught me when he was my boss at Seyfarth, new competencies might be expensive to buy but they are incredibly time-consuming to build.  Upskilling existing talent is an imperative, but one that can be accelerated only so far. The current supply-demand mismatch demands a market-level solution, and recent developments suggest that an agency model might fit the bill.  (He’s doing this now, at Bold Duck Studio, which offers a range of packaged services to facilitate innovation activities.)
  • Innovation as a service.  Jason Moyse at LawMade and Ryan McClead of Sente Advisors are two more examples of business and technology professionals who are looking to leverage their respective market-scarce competencies to facilitate innovation processes for a broader swath of the legal market.  Nicole Bradick‘s Theory and Principle is another example of specialized technical skill set on offer: an outsourced legal tech product development capability.

4. Still, We Need Systemic Investment in Human Capital

The above-cited examples suggest emerging trends with the potential to improve matching efficiency between innovation talent and innovation needs.  That said, the extent of the supply-demand gap demands an industry-wide response.  There is a clear need to step up systemic investment in professional development for business and technology staff and to explore cross-organizational forums that can accelerate the pace of knowledge sharing and collaborative innovation.

Casey Flaherty often says good lawyers aren’t scarce, good systems are.  Similarly, I don’t think that innovation talent is intrinsically scarce, but we definitely lack systems in the industry to identify and develop high-potential talent.  And we certainly need better systems to match the talent that does exist with the right opportunities.

What’s next? See Our journey to Big (067)