Profit-CenterFour years ago, a person parking in the company parking lot dinged the door on my new  truck. He or she opened their car door too wide. At the time, I thought, “wow, I could file a lawsuit for damages and become the new profit center for my family.” Okay. I thought something else (which I will refrain from saying as this is a family blog). But, had I thought the “family profit center” idea it would have reflected a popular idea circulating in the legal industry. Consultants and some general counsel advocated turning law departments into profit centers. I thought this nonsense had died. But, I saw a new white paper on the topic so I guess we need to work harder to kill this bad idea.

Alchemy and the Law Department Profit Center

The white paper, whose author I will refrain from identifying, focused on some tired oldies with the profit center pitch. We can run through them.

1. Pursuing wrongdoers. Someone harms the company. The law department pursues the perpetrator. The recovery effort works. The company wins damages or secures a settlement payment. The recovery exceeds the law department’s costs. The net amount is profit to the company. The law department is a profit center.

Um, no. Ignore the risk (claims against your company), the disruption (document gathering, depositions), and the general distraction. The idea suggests: let the harm happen, wait as long as you can to let damages build, then recover. That strategy would optimize the company’s and law department’s profits. Abusing the legal system is different than running a business. Recoveries compensate for harm (no harm, no recovery, no profit). Sometimes they remind the wrongdoer that harming others does not pay. Better strategy: identify risks and prevent them lowering the company’s overall cost.

2. Improving efficiency. This is a strange notion. The idea is simple: reduce the company’s waste, which lowers cost, which increases profit. Whoever reduces waste becomes, ta da, a profit center.

Um, no. Strange as this may seem, doing your job does not convert you from a cost center to a profit center. Everyone in a company, even the lawyers, should work to reduce costs. One could construct a fiduciary argument that lawyers and other employees owe shareholders a duty to reduce costs. Profits increase as you lower costs. But, a lower cost law department remains a cost center. Better strategy: incorporate waste reduction as part of your organization’s ethos and focus on productivity.

3. Helping procurement do better. This idea builds off the waste reduction idea. Lawyers work with the procurement group. Lawyers can help procurement improve at what it does. As procurement does better, costs drop, profits increase and, ta da again, the law department becomes a profit center.

Um, no. This is a wacky notion. Lawyers doing their jobs turns the law department into a profit center? Part of the job of an in-house lawyer is to help other departments do their jobs, even procurement (unclear why they were singled out). Yes, procurement helps the company with major purchases, but every department buys things so the law department should help all departments improve their operations. Better strategy: look for ways to reduce friction through legal process improvement.

4. Turning IP into gold. This is an oldie, but a favorite. Every company has IP. Others must want your IP. Maintain an active licensing program run by the law department and the law department—you guessed it, ta da—turns into a profit center.

Um, no. Other departments took risks, invested in people, equipment, and materials leading to inventions. If what those departments created has value through licensing, they should benefit (minus the costs of the licensing program). The law department does not become a profit center by recovering those investments. And, what does it do as the pipeline runs dry? Better strategy: partner with all departments on ways to maximize asset efficiency.

Maybe those were bad ideas. Could a law department become a profit center? Sure. If the law department invests in people, equipment, or materials that lead to ideas it can license, it can become a profit center. Imagine a law department that develops a contract management program. It licenses the program to other companies. One could question whether that is the role of the law department and whether those investments should go to other departments. But, those are policy questions. The law department made the investment and if it recovers value in excess of the investment, the law department earned the profit.

Aim to be a Competitive Advantage

Where should a law department focus its time? A law department should focus on becoming a competitive advantage for its parent corporation. IT departments, human resources departments, finance departments, and other service departments should do the same thing.

What does being a “competitive advantage” mean? Start with basic law department functions. A law department should aim to reduce its spending per lawsuit dropping below competitive law departments. They should keep risk at an equivalent level or lower it. If company A’s cost per slip-and-fall lawsuit is $50,000 and it is $40,000 for company B, company A’s lawsuit costs put the company at a competitive disadvantage. It should bring its cost below $40,000. The cost includes expenses, settlements, and disruption costs (e.g., time of employees taken away from work). A law department wants the cost of its functions at or below the industry average. Even better, they should aim for the bottom quartile of the industry (on a risk-neutral evaluation). Getting to that competitive advantage by increasing risk is unacceptable.

With costs under control, the law department can focus on real drivers of competitive advantage. Doing things the same way and just as good (or bad) as everyone else does not provide a competitive advantage. If the industry average time to sign a new distributor agreement is 90 days, streamline processes so that your company can get them signed in 60 days. The 30 day saving translates into revenue, a competitive advantage. Do the terms and conditions of your contracts create greater friction than terms and conditions in competitor contracts? Simplify the terms and reduce friction. Make it easier to buy from your company.

Law departments that form relational structures with their legal services providers have advantages over departments pursuing transactional relationships. A transactional relationship is the structure we see today. Hire a firm for a matter and move on. Use RFPs to excess, bargain for the lowest price, and forego enduring relationships. Law firms have no incentive to invest in innovation for the client. The law firm will not spend resources finding ways to increase the client’s competitive advantage.

Relational structure clients look for enduring relationships. In a relational structure, the client and the law firm re-work processes to cross organizational borders. By integrating processes across borders, the client and firm achieve greater process improvement than either can achieve on its own. They work as one rather than as distinct entities. Both have incentives to invest in the future of the other. The law firm looks for ways to give the in-house law department that competitive advantage. The advantage could come from new ways of doing things, new things to do, and even new business opportunities for the company (such as new financial products).

The client benefits from innovation and the law department demonstrates greater value. The law department may drive new business, but at a minimum it reduces its drag on the existing business. No one tries to turn the law department into something other than a cost center. But, the law department focuses on becoming a competitive advantage.

Be Comfortable in Your Skin

The “law center as profit center” idea came out of law departments looking for ways to show they add value. They made a mistake; they thought value equalled profit. Get comfortable living in the “cost center” skin. But be wise. Spend money to avoid lawsuits rather than prosecute or defend lawsuits. Preventing lawsuits reduces cost and friction.

I have argued for the competitive advantage view without discussing certain challenges of becoming a profit center. But, I should mention them. First, profit centers approach challenges from a different viewpoint than risk management centers. Corporations need checks and balances. As a law department moves from risk management to profit, the incentives change. Is it in the best interest of the shareholders for law to make that move? Who watches the henhouse?

Second, as a profit center, the law department moves from service provider to competitor within the organization. It must demonstrate an equal or higher return on investment in the law department than other departments. As a service department, it should consider return on investment, but not as part of that competition. The ROI question is whether it uses the resources given it efficiently. Focusing on reducing antitrust risk may have a higher ROI than focusing on reducing contract risk. That information helps as the law department considers ways to spend its resources.

Law departments can and should demonstrate value to their parent corporations. Many metrics will do that. Showing the law department’s competitive advantage is consistent with risk management, cost management, and adding value. Leave the profit center concept to your clients.

SymbolThis could get ugly. I’ll step our way through it so stay close and hopefully you will make it through to the end okay.

Dr. Stephen Wolfram is the guy you did not hang around with when you were in school. He was born in London in 1959. As often happens with people of high intelligence, he struggled in school and had no patience for the “silly” arithmetic books he was asked to read. But by his early teens, he had written three books on particle physics (not published).

One of the reasons you would not have hung around with Stephen in school is that he hardly spent enough time at a school for you to get to know him. By age 15, he had published articles about his research in quantum field theory and particle physics. He went to Eton College, but left before graduating and at age 17 entered St. John’s College, Oxford. He also left St. John’s before graduating and enrolled at the California Institute of Technology where, at age 20, he received a PhD in particle physics. One of the members of his thesis committee was Richard Feynman (yes, that Richard Feynman). What next? He joined the faculty at Caltech and at age 21, became the youngest recipient of a MacArthur Fellowship (the so-called genius grant).

If you think Richard Feynman was a brilliant theoretical physicist who did things ranging from assisting in the development of the atomic bomb, to creating Feynman diagrams (visual representations of the mathematical expressions describing the behavior of subatomic particles), to nanotechnology, you are right. But he also was perceptive about human character. When Wolfram wrote to Feynman saying he was considering starting an institute to study complex systems, Feynman replied “You do not understand ordinary people,” and suggested Wolfram “find a way to do your research with as little contact with non-technical people as possible.” Again, another reason why you probably would not have hung with Wolfram.

Wolfram left Caltech and joined the faculty of the University of Illinois at Urbana-Champaign where he founded the Center for Complex Systems Research and the journal Complex Systems. When he was at Caltech, Wolfram had developed a computer program called Symbolic Manipulation Program. A battle with Caltech over the rights to the program and related issues led to Wolfram leaving for the University of Illinois. Shortly after arriving at Illinois, Wolfram began developing Mathematica and within a year founded his company Wolfram Research. Today, Mathematica is widely used around the world and Wolfram Research, which Wolfram joined full-time shortly after founding it, develops and promotes the program.

In 2002, Wolfram published the book A New Kind of Science, in which he argues that the universe is digital. He further argues that simple computational systems can be devised to model and explain all of nature. In 2014, Wolfram finally named the programming langue that had been driving Mathematica for 25 years, calling it “Wolfram Language.” Wolfram Language can be used to write the computational systems, but Wolfram had been expanding the Language’s reach. Wolfram spends his time on Mathematica, on developing Wolfram Language, and on giving it greater exposure so others will use it. In essence, Wolfram followed Richard Feynman’s advice by creating a world in which he can spend most of his time working with technical people on his vision of a computational future.

And I Care Because …?

Last week, Wolfram posted a long blog post laying out his vision for computational law. The post covers a lot of ground and stretches from Aristotle to the present, so I won’t try to cover it all in my recapitulation. Instead, I’ll focus the rest of this blog on the key point in Wolfram’s blog, his argument that now is the time (and of course Wolfram Language is the vehicle) for creating a symbolic discourse language. In other words, Wolfram believes we are ready for a language we can use to express legal concepts and which computers can use to compute outputs. Creating the symbolic discourse language, within Wolfram Language (a symbolic language) is his next step. Again, talk like this is probably another reason why you wouldn’t have hung with young Wolfram.

Think of symbolic discourse language as something that exists between natural language and computer language. Without getting deeply into computer software and hardware, think of the computer’s operating system (e.g. Windows or Mac OS) as the base level. On top of the operating system we have applications, like Word. When you want to write a letter, you can open Word and just type. Word interacts with the computer’s operating system and the operating system interacts with the hardware, so that when you click “print” your letter is printed.

That system worked well for lawyers and poets, but those who used math were left struggling. They had to program the computer to run their computations, and that meant learning computer languages such as Fortran (in the old days) or C.

Wolfram created a new language that allowed people to run math and get answers to formulas or graphs without having to go deep into programming. The new language, Wolfram Language, is a symbolic language. That means you can enter relatively simple commands and Wolfram Language converts them into the complex commands that drive the computer. The more sophisticated the language, the more symbolic the commands you can use.

If you ask Wolfram Alpha, which takes as one form of input natural language, “what is the diameter of the earth?” it can translate your natural language inquiry into the code needed to search for the information, assemble it, and present it to you in a way that you can understand.

Now think of a court decision. Judges do not use symbolic language. They attempt to explain the law, the facts, and their reasoning using natural language. But using natural language can get messy. Think about separating “preponderance of the evidence” from “beyond reasonable doubt.” You get the terms, but that doesn’t mean a computer or others get the terms. They convey a concept, but not precisely.

A symbolic language could take each term and turn it into something a computer can understand (e.g. >50.0%). Once the computer can understand it, it can receive inputs and deliver outputs. Lawyers and judges would then write contracts, briefs, case law, and other materials using the symbolic discourse language instead of natural language.

If you are straining to extend this idea to all legal discourse, that isn’t surprising. It will take quite an effort to develop the entire symbolic discourse language. But Wolfram’s point is that our knowledge and tools have developed to the point where he thinks his team can do it.

Don’t Get Rid Of Lawyers Just Yet

Let’s address the first issues that come up in a lawyer’s mind when reading this story: what is good or bad in it for me? You may find it surprising, but Wolfram does not take the position that the symbolic discourse language will be the end of lawyers. He says, “Today lawyers have to learn to write legalese. In the future, they’re going to have to learn to write what amounts to code: contracts expressed precisely in a symbolic discourse language. … [I]t will help lawyers think better about contracts.” For those in legal education, this is another, and perhaps the most powerful yet, reason to start teaching law students logic and coding.

If symbolic discourse language won’t decimate lawyers, will it decimate the law. Will law become so simple that anyone can do it? Not so, according to Wolfram,

Once computational law becomes established, the complexity of what can be done will increase rapidly. Typically a contract defines some model of the world, and specifies what should happen in different situations. Today the logical and algorithmic structure of models defined by contracts still tends to be fairly simple. But with computational contracts it’ll be feasible for them to be much more complex—so that they can for example more faithfully capture how the world works.

He goes on to describe how the symbolic discourse language will interact with machine learning software that is gathering information from other sources (e.g., the internet) that the language uses to inform the contract. This gets a bit tricky, but I’ll take a stab at explaining it borrowing from one of Wolfram’s examples.

The contract calls for X to happen when condition Y is satisfied. But Y is something itself difficult to define as “satisfied” or “not satisfied” in simple terms. Wolfram uses the example of fruit. I will pay you $10,000 for delivering to me a certain quantity of fruit meeting the standard “Fancy Grade.” The question is whether the fruit met the standard.

We could define the standard as no more than Z% of the fruit has blemishes and we can further define a “blemish”. A computer could examine all the fruit, calculate the percentage of blemished area, and feed that into the contract yielding an output: pay or don’t pay.

Many lawyers may be shouting “huzzah” right now. We’ve just said that law will evolve to a symbolic discourse language (in other words, legalese of a different type), become more complex, and require knowledge of both legal principles and computers. Is law going back to an opaque art that will require clients to pay for access? I don’t think so, but let’s leave that question to the side and explore other “what does it mean” questions.

Crushing Poetry Out Of Law

Every law student knows the Aristotle quote, “The law is reason, free from passion.” Wolfram says that symbolic discourse language would take us there, “In a sense, the symbolic discourse language is a representation in which all the nuance and ‘poetry’ have been ‘crushed’ out of the natural language.” This will raise some interesting questions, particularly when it comes to equitable considerations. Should contract law be devoid of poetry?

Going in another direction, we can ask how symbolic discourse language might affect our understanding of the economic underpinnings of contracts. On October 10, 2016, the Royal Swedish Academy of Sciences awarded the Sveriges Riskbank Prize in Economic Sciences in Memory of Alfred Nobel 2016 to Oliver Hart and Bengt Holmström “for their contributions to contract theory.” They have focused much of their work on the area of incomplete contracts. The theory starts with the thesis that contracts are incomplete, because they cannot specify what is to be done in every future situation.

Part of specifying the future is data, part is computational power, and part is complexity of the contract. Today, we can’t possibly analyze sufficient data to predict all possible future consequences. Even if we could get enough data, we would need tremendous computational power to analyze it. Finally, writing a contract to cover all the contingencies would result in a document no one would dare write or read.

Wolfram posits a future where those problems would be greatly mitigated. Computers can scour vast databases and use machine learning to analyze data relevant to the contract. With access to tremendous computing capacity, the power to analyze the data becomes available. Finally, if the contract will be written in code—and given that the computer itself could write at least some of that code—we don’t care how long the contract becomes. We can see a touch of this today in electronic trading on the stock exchange. Computers gather and analyze the data, develop algorithms based on the data, and place the trades.

As you can begin to see through the fog, data plays an ever more important role in contracting. Data helps inform the terms of the contract, but data also becomes the fodder for the programs that determine whether terms of the contract have been met. Data also affects the dispute resolution process. If both parties and the court have access to massive quantities of data and the computing power and systems (machine learning) to analyze the data, dispute resolution could become focused on very narrow issues as more contract issues are answered through complex contracts and data.

We Still Have Ground To Cover

Wolfram’s post (which consumes 20 single-space pages) touches on some of these issues and addresses many more, yet still leaves large gaps in its wake. The core proposition is this. Work so far, with some exceptions (many of which Wolfram notes) has been focused on backing into discourse analysis by examining what courts have done and attempting to find ex ante ways to construct systems for describing the logic of law. Wolfram proposes to construct a symbolic discourse language that lawyers, judges, legislators, and society would use to create law. Computers could use the language, augmented by machine learning analysis of relevant data, to evaluate questions arising under the law.

Wolfram acknowledges the huge amount of work it will take to accomplish this feat. But, as his biography suggests, he is not someone to shy away from challenging questions or large amounts of work.

Lawyers should consider what Wolfram proposes in a different light. Perhaps Wolfram will succeed or perhaps this will be a challenge that survives him. But, most of the work we see today involving computers and law involves attempts to automate the present or to decipher the past, not create the future. Just as we are at an inflection point in the delivery of legal services, one could argue we are at a similar point in the substance of law. It has become too chaotic and faces challenges too great (e.g., explosion of relevant data, speed of societal change), for the current approaches to developing law to work. Add on to that other issues, such as the privatization of law and many issues never reach the courts, affecting the evolution of common law.

Wolfram’s path is not artificial intelligence in the law. It doesn’t remove lawyers from the equation (though that is theoretically still possible at some point). Instead, and at a closer point in time, it leverages the power of computers to use data, handle complexity, and make law (in theory) more precise (though at a cost to humanity, a topic for another post). Those are benefits we can deliver to clients and, ultimately, what makes his path intriguing. It will be up to lawyers to determine whether they let this turn out to be an ugly or beautiful path.

BespokeThurgood Marshall was not just a justice on the U.S. Supreme Court, he was the lawyer that led many of the civil rights cases striking down de jure racism in the United States. While he successfully prosecuted many cases, he is perhaps best known for the Brown v. Board of Education landmark decision in 1954. The Supreme Court unanimously ruled that “separate educational facilities are inherently unequal” and, therefore, racial segregation of public schools violated the equal protection clause of the 14th Amendment.

Justice Marshall’s strategy to bring down racist practices in the United States is still studied today as a model for how to run an activist program through the court system. But today’s court system is far more complex, and the contexts in which activists bring their challenges far more convoluted, than at any time in the past. If a few years from now you were faced with a challenge of the type that Justice Marshall faced, could you run a similarly successful campaign?

The Maker Era

We have entered the maker era. You can buy a 3D printer, put it in your basement, and within minutes you can make or replicate replacement parts, toys, or your own inventions. Connect your computer and software, and now you are in the business of designing whatever you want. Once you are satisfied with the design, you can send the file to a commercial manufacturer or license it over the web. You are in competition with the global world of manufacturing.

What if you are a nascent clothing designer? New nanotechnologies allow you to spray on clothing. You can add colors, create texture and design combinations, and even build in special features (anti-perspirant?) into the new shirt. If you don’t like what you created, simply peel it off, dissolve it, and start over again.

If you aren’t interested in manufacturing or clothing, perhaps you would like to use software to mine behavior? Go to the internet and you can download software that lets you analyze text, apply machine learning, and find secrets hidden in the words. You can analyze sentiments, build behavioral paradigms, and test your ideas without leaving the comfort of your office.

Lawyers often struggle to comprehend how fast the world is moving around them. 3D printing, nanotechnology, and artificial intelligence are just three of the many areas rapidly scaling from the laboratory to real life. Yet most lawyers are still in the quill and paper era, trying to master Word and occasionally venturing into Excel or PowerPoint.

Most law and technology posts focus on simple automation or perhaps elementary artificial intelligence applications, such as finding all bankruptcy cases with certain elements. The “scary” futurists speculate about robotic lawyers sitting next to human lawyers as they compete for the next case that comes over the transom.

In this essay, I’m going to take you on a bit of a journey into the future. Let’s imagine how a clever software engineer with knowledge of the law and a lawyer with a bent for social justice could start a movement in the 21st century.

The Designer’s Lawsuit

Thurgood Marshall faced a significant challenge in the 1930s, 1940s and 1950s when he was attacking racism as a lawyer for the NAACP. He had to maneuver key issues through federal district courts and appellate courts, often hostile, to get those issues before the U.S. Supreme Court. To do so, he relied on research, experience, intuition, and luck. Could he do it differently in the 21st century?

Imagine using computational linguistics and machine learning to look at all the reported opinions relevant to the issues you wanted to get before the U.S. Supreme Court. The software “reads” the opinions and the briefs, finding those obscure connections you couldn’t find even you if you had the time to read the hundreds of thousands of pages.

To make the analysis more interesting, you also look at databases built from the biographies of the judges who may sit on the case at each federal court level. The databases include all the information about the judge’s experience (undergraduate school and major, law school, etc.), training (law firms, government positions, etc.), and personal characteristics (age, gender, etc.). It also includes everything the judge has written or said that is public, outside of opinions. Speeches, law review articles, and op-ed pieces sit in the database.

While it may seem like you have the relevant information you need, you don’t stop there. You pull together information on the community where each judge lives. What is the political sentiment within the community? Is it affluent? What religions predominate? You dig deep into the community to understand how it may affect the judge’s thinking.

You also look at the national climate. Where have the trends been going—are the people in the United States moving in your favor or against you on the issues? Are there similar issues from which you can gain guidance? What about legislative movements at the local or national level?

With this massive database, you turn loose your machine learning software again on a small subset, the “training” database. The algorithms (and you use many, stacked to mimic the way the human brain processes information—as best we can tell) run through the information over and over again. The algorithms are learning, attempting to “understand” the data. You ask questions and the algorithms respond.

As the algorithms respond you check the results. You reject most responses and inform the software of its hits and misses. Over time, the hits grow and the misses shrink. The algorithms seem to be organizing information the way an expert might. You reach the point where you think the algorithms are ready for the big time.

You turn them loose on your large data set and wait to see what happens. As the results start to come in, you see that the algorithms have identified some court possibilities you would have picked, but there are some unexpected choices as well. In fact, it turns out the unexpected choices rank higher on the probability of success than your expert choices.

That was the easy part. The next step is to ask the software to design the lawsuit. What arguments will work best in each court? Which arguments should be emphasized and which added as “just in case.” Again, you find some of the picks familiar, but some are unexpected. In fact, there are a few creative uses of arguments that came out of cases decided long ago, but that seem to fit with the times.

With knowledge of the arguments, you turn the work over to the next program: the legal argument drafting program. For many years now, software has been writing corporate earnings articles and recaps of sports games without human intervention. The software takes the financial results or the game record and, using some training on writing styles (a dash of Hemingway mixed with a bit of Lardner), turns out articles that people can’t distinguish from articles written by journalists.

That software has now been trained on legal writing styles. Using opinions by Holmes, Jackson, Hand, and Posner, you have trained the software to write like a judge (or, perhaps, like some judges wish they could write). With the legal arguments preferred by the machine learning algorithms, the legal argument drafting program turns out a passable first cut at a motion for summary judgment. You can use that to back into a complaint. Once you know the district judge and she issues her opinion, you can use the software again to churn out the first draft of your appellate brief. You have designed your first lawsuit. (And before you yell at me about all the professional responsibility issues involved, please remember the spirit in which this essay is intended – to spark interest in what types of analyses will be possible, not to suggest ways to improperly create and file claims.)

Don’t Wait, Dive In

The designer lawsuit is, of course, a thing of the future. The software we have today can do bits and pieces of what I have described, but we still have a way to go before all of those pieces can knit together a new case tweaked for what may work best in each court. Still, we are closer than what many lawyers think.

The point of this exercise is not to scare lawyers into thinking software will replace them soon. It also isn’t to add on to the fatalist pile the thought that lawyers will soon become extinct, or nothing more than the handmaidens of computers.

I do hope the story has piqued your interest in staying current with what software can do. The amount of data available to lawyers is vast and far beyond what we can reasonably consume and use to help our clients. It grows much larger each day. As tools come online that can help us digest that mass, to not use them approaches the irresponsible. That information contains judicially recognizable information that may tip the balance in an argument. It puts judicial decisions in the context of what is happening in society (and if you still think judicial decisions aren’t political or are limited to extending law on the books, then I apologize for rudely dragging you into the real world).

We have entered the augmented age, where humans plus computers can take us further, faster, than humans or computers can go alone. If you believe that is fantasy, think about what you hold in your hand: a smartphone you can talk to that reaches out to all the data on the internet to answer your question. In seconds, Siri or Cortana translates your spoken request to digital commands, processes them, and come back with an answer or at least relevant web sites. Your mind has been augmented, through the smartphone, by the internet. That connection grows closer and stronger each day

When will we have designer lawsuits? Five years? Ten years? Longer? We don’t know and the correct answer isn’t relevant. The designer lawsuit will not be like falling off a cliff: one moment you do all the work and the next the computer does it for you. We will creep closer a step at a time, with the steps coming quickly at some points. Whenever the time comes, you will be much better off for having kept pace with the changes, than trying to quickly run to catch up. No matter how fast you are, you will not succeed.

GoldRushOn January 24, 1848, John Wilson Marshall was building a water-powered sawmill for John Sutter. The mill was close to Coloma, California, near the base of the Sierra Nevada mountains. Marshall was a carpenter who had emigrated from New Jersey. Although Marshall wasn’t looking for gold, he later claimed that he knew immediately upon seeing the flakes what he had found.

At the time Marshall found the flakes, Mexico and the United States were still at war over the California territory. The population of the territory was mostly Native Americans (around 150,000) with some 6,500 Californos (people of Mexican or Spanish descent) and about 700 others (mostly from the United States).  A few days after the discovery (and not because of it) the United States and Mexico signed the Treaty of Guadalupe Hidalgo which ended the Mexican-American War and left California to the United States.

Sutter and Marshall tried to keep the discovery confidential, but obviously did not succeed. Within two months a newspaper was reporting on the discovery. By mid-June, three-quarters of the male population of San Francisco had left to goldmine, and by August the number of miners in the Sutter Mill area had reached 4,000.

The next year was written into U.S. History as The Gold Rush of ’49. The non-native population of California grew from 700 before the discovery to 100,000 at the end of 1849. By September 1850, California had become a state. The Rush peaked in 1852 when approximately $81 million (in 1852 dollars) of gold was pulled from the ground. By 1857, the annual production had dropped to around $45 million where it stayed for many years. The Rush was one of the most important events in re-shaping the face of the United States.

The 19th century in the U.S. was known for gold, but the 20th century was marked by hydrocarbons. While some believe the 21st century’s gold, especially later in the century, may be water, the current rush focuses on data.

The Strange Stories of Law

You have heard the statistic: each day more data is generated and stored than the amount of data that existed in all of history prior to the computer age. Large companies that entered the world as retailers, search engines, or social media companies found the real value of their businesses was in data. In Silicon Valley, it almost became irrelevant what your business could do, the focus was on the data set it could build.

And then we come to the legal industry. We can tell two versions of the legal industry story. The first story goes like this:

Recognizing the threat cybersecurity breaches present to their clients, law firms decide to thwart the attackers using an unusual approach. They accepted the futility of keeping hackers out of their systems. Instead of following the norm of keeping information as accessible data, which can be indexed, accessed, and manipulated, law firms keep their information somewhat like teenage boys keep their rooms. As one law firm leader said, “We decided that if our data was a mess and even we, who know it best, have difficulty finding and doing anything with it, hackers would have more trouble and simply give up.”

A team of red hat associates was tasked with hacking the system to find a group of documents to use as templates when drafting for a client. The blue hat defense team’s strategy was simple. “We pretended we were partners and randomly withheld helpful information from the red hat team.” The red hat team gave up after many hours and decided to draft from scratch.

The second story goes like this:

While clients and the world around them screamed about data, lawyers continued their quest to be oblivious. Lawyers in firms, corporations, and other service organizations knew that if they hadn’t enjoyed knowledge management, they would enjoy data management even less. Again adopting the “to do nothing is to do something” approach, lawyers have ignored pleas to treat their documents as data gold.

When asked about this strategy, a lawyer responded “The world around us has been changing for decades and yet here we sit today, almost unchanged. To respond to this ‘data fad’ by doing something would go against our strongly held belief that all tasks should be done by lawyers and not other service providers, even computers. Indeed, we are considering asking bar associations to file actions against all computer companies for the unauthorized practice of law.”

Choose which version you prefer, but the reality is that lawyers in firms, departments, and other legal service provider organizations are in the same boat. Legal data is not created and stored as the precious commodity it is.

The Stories Data Could Tell

The work that lawyers do tells stories of risks and responses. What gave rise to the lawsuits? What did the parties do? What steps were taken in the ligation? How long did they take? What were the responses? We can explore similar questions in any area of law, and it is those questions and responses which are embedded in what law firms store on their servers.

The challenge with most data sets, unlike those in law, is not getting to them. Finding data sets can be easy. The challenge is getting them in shape to use. Data scientists call this step data wrangling or data munging and it eats up 80% of their time.

Think about a data set in your firm that you actually keep as a data set: your customer information. Your firm or law department has a system for keeping track of customer (in the case of law departments, law firm) information. If you check the system, you will find out of date entries, missing information, duplicate entries, and incorrect entries. Imagine how long it would take if you froze the system today and had someone focus on cleaning up the database. Of course, as soon as they finished and you resumed using the database, you would find it out of date.

Now apply those problems to your real data. All of the documents sitting on your servers form your database. You may have a knowledge management system, and still your data is not ready for use. At best, you have a collection of documents with some perfunctory information filled in by field. Your knowledge management system uses a not-too-sophisticated search process to locate documents responsive to your request. When you find them, you can’t do much with them except copy and use them as templates. Definitely not state-of-the-art.

When I talk about data, I mean the ability to access specific information from those files, combine it with other data, and produce information that will help solve client problems. For example, what if you could combine data from all the employment lawsuits you have handled with data from government and court data sets. Could you construct a model that gives specific information about each type of employment lawsuit?

You may think of this as fantasy, but it isn’t. Today, startups have breached the barrier and are applying this type of analytics and more as they find and use data sets. One small but growing area is computational linguistics. Put very simply, CL applies statistical tools to text. Through machine learning, computers can use the CL tools to understand text far beyond “supreme w/5 court.” Tools using CL in law are in the early stages, but they all face the same challenge: getting access to clean data sets.

This is where lawyers enter the picture. By recognizing today that the information built into the data sets is the gold that will help law firms and law departments protect clients, lawyers take the first step. The second, is to start transforming what already is in the sets into data, and the third is to store whatever new items are created as data.

If You Make It The Bad Guys Will Come

At this point, a good question to ask is what about the cybersecurity threat? As they say, there are two types of companies: those that have been hacked and those that don’t think they have been hacked. The experts with whom I have talked agree that law firms are and will continue being hacked. The firms just do not have the sophistication to prevent the hacks. That is not a slam against law firms. It is hard to find any organization, and so far no one has named one, that is immune to hacks.

So if the hacks will happen, why should lawyers turn what they have into data? My first scenario above was written in jest, but lawyers do ask if it isn’t better to have the hackers find the messy teenager’s room than a nice, neat library?

The response to hacking isn’t to abandon the quest for data, just like the response to computers isn’t to become a modern-day luddite. All firms and corporations should take reasonable steps (and today more are going well past reasonable) to protect against hacks. Assume there will be hacks and focus on the data. Just because a hacker can get into a system doesn’t mean the hacker can get access to, un-encrypt, and assemble all the data in a way that will help them. You have a security alarm on your house, but you don’t leave all of your valuables lying on the kitchen counter. Thieves still take gold, but we still mine it. Cybersecurity is a challenge, not a bar, to keeping and accessing data.

Data Is Becoming Essential

Data is going to be more than a way to use and manipulate what you create and store. It will become an essential part of the modern law practice. Let’s look at one last example: blockchains. I won’t go into a detailed description of blockchains, I’ll keep it simple. A blockchain is a database that is distributed, not centralized. Each record in the chain may hold data, a program, or both. The records are hardened against tampering through strong encryption and distribution. Blockchains reduce and sometimes eliminate the need for intermediaries.

The terms of a smart contract are built into the code embedded in the blockchain. If condition X happens, then Y occurs. No ambiguity, no equity (at least that is the theory). Once the contract is formed and built into the blockchain, no on can alter the blockchain (more precisely, an altered blockchain becomes an instantly visible anomaly rejected by blockchain holders).

Lawyers who do not understand blockchain, code, computers, or how the system should work will be at a severe disadvantage. Yet big banks and other large players are actively looking into using blockchain or similar technologies as part of their systems. Since the contract is in the code, we can treat the contract as data and start combining and manipulating it.

Mine the Data Now

Lawyers have believed for centuries that they need to study the law, but they can pick up everything else quickly so that they can apply the law to it. Litigators are famous for believing they can litigate an employment case in the pharmaceutical industry this week and an antitrust case in the retail industry next week. Large firms have moved beyond this by making everyone specialize (and sub-specialize), but the feeling still exists. So, lawyers wait and watch. When they think something has become so well established that the world can’t possible go back, lawyers make their move.

While lawyers may believe they can wait until everyone is deep into data and then put their toes in the water, it doesn’t work that way. I mentioned at the outset competitors in the retail, search engine, and social media industries. They have built data sets so large and deep that it is unlikely anyone can catch them. In fact, recognizing that the prize is data and not tools, Silicon Valley has embraced a new trend. These companies are posting on the Internet for anyone to use many of the most sophisticated tools they have developed.

Why would they open source the tools? Because these companies know that the tools are useful and by open sourcing them they may get interesting insights from others who use them. Making the tools available allows the scientists who developed them to showcase their work, an important part of attracting and keeping talent. But these companies also know that without their incredible data sets, others will not be able to use the tools to replicate what these companies do. The tools help, but the data sets are essential.

Law firms and law departments have yet to realize that tools are becoming widely available. The firms and departments will need help, from academia, consultants, and others, to understand and employ the tools. But, the tools will not be the chokepoint. The real value is in the data. Each firm and each department has value in its proprietary data. To realize that value, they must start treating it as gold and not as dirt. Welcome to the 21st century.

BanishBetterIn the United States, we are in the thick of a presidential race and so far all I know is that things will be better. Every candidate uses every opportunity he or she gets to tell me how things will get better if he or she is elected. As Americans, we will be taller, stronger, and smarter, we will weigh less, move more quickly, make more money, have more leisure time, fight fewer battles, and achieve more of our dreams. Whatever it may be, the candidate occupying 30 seconds of air time will make it better when he or she gets into office.

My wife is a great fan of the old television series M*A*S*H and so I quote the eminent Colonel Potter here when I say “horse hockey!” We are all adults and we know that “better” in the context of a political election can best be interpreted as meaning “different.” I am not saying that, using objective measures, everything will go downhill. On average, people will make more or less money, will have higher or lower scores on education measures, and the average standard of living will increase or decrease. But those statistics are not the same as saying things will be better.

That is one of the great things about normative words, they sound impressive but don’t pack a substantive punch. Politicians love words that don’t mean much, which probably is why I am hearing so many of those words, including “better,” from candidates right now. But words that do not mean much cause us to lose trust in the speaker and that is why I think as lawyers we need to stop using better.

Better Should Have Been Gooder

In law, no analysis of words and their use is complete unless we dive into history. If we look at the etymology of the word “better” we find that it has been around for a long time. Linguists have traced it back through Middle English, Old English and into Proto-Germanic languages. As we all learned in middle school in the United States, English is an irregular language. As a lean thinker, I like to describe it as a non-standardized language. But since Richard Susskind has conditioned us to think about what we do as falling on a continuum from bespoke to commoditized, I guess we should describe it as a “bespoke language.” Better is one of those bespoke additions to English.

If English had moved along the Susskind continuum towards commoditization, “good” would have given rise to “gooder” and then “goodest.” Instead, our ancestors pulled in some German, moving from “good” to “better” and then “best.” The Word Detective nicely sums up our predicament:

Life would be a bit simpler, especially for folks learning English, if the comparative and superlative forms of “good” conformed to the usual practice and appended “er” (“gooder”) and “est” (“goodest”) to the base word (as in “long,” “longer” and “longest”). But it’s too late now, because we’re stuck using the forms that went with the Germanic root “bat,” meaning “advantage or improvement.”  Its comparative form was “batizon,” and its superlative was “batistaz,” which entered English as “betera” and “betest.” These were later smoothed out to “better” and “best” and adopted as the companions to “good,” which lacked its own comparative and superlative.

Regardless of the irregularity of our language, it seems we have always wanted a way to say that A is, in some undefined and mostly personal way, superior to B. We have a way, but we can do better.

Better Does Not Add Value

Lawyers have latched on to “better” and love to throw it around, perhaps for no other reason than to demonstrate their familiarity with Old English, Middle English and Proto-Germanic languages. We have better courts, better judges, better arguments, better contracts, better caselaw (or is it “case law,” I’m still confused about which is better), better lawyers, better law firms and better fee arrangements. The list continues with better law schools, better professors, better legal writing, and even esoterica such as better legal reasoning.

This is where we get to the heart of my complaint about lawyers using “better.” In most discussions, it adds little or no value. If we are discussing whether to hire a recent law school graduate, it does not add anything to say she went to a better law school (“she went to Harvard, which is better than Yale”). Absent from the sentence is any suggestion of measurable and objective difference. Is it better because it costs less? Because on average Harvard students speak four languages and Yalies only three? Better leaves us with that faint feeling that the speaker feels he is somewhat “better” than us, because we do not recognize the superiority of his choice.

Substitute Measures for Better

In casual conversations, better probably does little harm while adding little value.  When talking about meaningful issues, however, better interferes with serious discussion. To argue one law firm is better than another, the speaker should have solid data to back up their view. We would prefer to see some objective quality measures, metrics demonstrating higher efficiency, lower cost per unit of output, or something equally as solid. Instead, better simply means the speaker chooses that firm over others.

When we compare legal service delivery models, it would help the discussion to show that one model requires fewer steps, overall requires less time, consumes fewer inputs, or yields more successful outcomes along a quantifiable scale. Simply saying “I like my way of handling cases, because it is better” does not help anyone.

Better is a safe haven for those who prefer the comfort of “I know it when I see it” over the risk of “I can measure and demonstrate it” approach to argument. Lawyers have been trained in the former, but abandoned math and science classes to avoid the latter. The challenge for lawyers is that the rest of the world decided to stick with those math and science classes. That is why today people outside of law (referred to colloquially as “clients”) talk about Big Data while in law we think about the Big Empty.

Big Data is the world where people collect data about everything and then turn that data into correlations. When the data is big enough, it does not matter whether A causes B, it just matters whether they tend to happen together. For example, it does not matter whether having a baby causes you to drink more, it is just important for the retailer to know that people who buy lots of diapers also buy lots of beer.

The world of law has preferred Big Empty, where we do not gather data on anything except what we get paid. Even when we have access to incredible amounts of data (and most of us do) we ignore the data in favor of the less resource-intensive “better” approach to arguments. Strangely, this is not leading to “better” relations between in-house counsel and outside counsel. The situation is deteriorating.

Lawyers Have the Data to Improve

We could change the situation by following a new path. The new path would include gathering and using the data all around us to make comparisons. Starting with law schools, we could gather meaningful data about student qualifications prior to law school and student performance during law school. What we gather today is convenient, not meaningful. We also could compare student performance on critical measures at the outset of law school and the end of law school (and, for that matter, at critical points during law school). By tying those measures to other data—first job information—we could develop a model that tells us what inputs yield what outputs. It is not difficult to capture this data, we just choose not to do it.

For practicing lawyers, we have a wealth of data we can gather and analyze. Today, a better law firm usually means a law firm that costs more money and hires its graduates from a few, well-defined law schools. Recently, I had a conversation with an in-house legal operations manager at a major defense contractor. She explained that the company hired only the best law firms. When I asked what that meant, she said “well, the most expensive law firms in New York, of course.” When I asked what defined them as “best,” she replied “it is just like buying anything, you know it is the best because it costs the most.” ‘Nuff said.

Assuming best means something other than “costs the most,” we should collect data on those other criteria and compare performance. Litigators file briefs in courts. We can measure many data points off those briefs and compare them with case outcomes (including interim steps in cases). In fact, some academics and disruptive companies in legal services are starting to do this.

We can review contracts, capture data from the contracts, and run comparisons. Many other documents crafted by lawyers are publicly available and we can use the same process with those documents.

We can track advice to outcomes. We can measure cycle times from beginning to completion, the quality of the outcomes, and yes, even the cost of the outcomes. We are swimming in pools of data. Despite the abundance of data, lawyers like to pretend they live in a world of Big Empty.

The harsh reality for lawyers is that the world has moved from assuming superiority using normative comparisons to wanting proof of superiority using quantitative comparisons. Don’t just tell me it is better, prove it.

The legal world moves slowly and, as I have written elsewhere, most leaders in the legal profession are running out the string on their careers hoping that change will happen so slowly they can retire before having to deal with it. Those who are not in leadership positions seem content to graze on whatever clients throw their way, hoping that fresh work will appear tomorrow without any effort on their part. Perhaps the grazers are making the right bet, but I doubt it.

As we drift through the political season, and continue to hear about our “better tomorrow,” we should start asking the tough questions. For lawyers, the tough questions include how can we use data to show that we really are “better.” If we make the change from normative to quantitative, I think we will be better off. We will know, because we will see an increase in client trust.

PredictiveLawFirmOne of the trends wrought by the new era of technology is to reimagine your product or service as a new category. Hotmail isn’t an email service, it is the web as a platform (since Hotmail is a web-based service). Tesla isn’t a car company, it is an automaker as an energy company (since a driving force behind Tesla’s success is the battery technology). It seems that products and services are no longer what they appear to be, they are merely portals to something else.

While the renaming game might seem like semantics — a way to gussy up a product or service that is what it is (Hotmail is an email system, Tesla is a car), to others it brings a fresh perspective to what you can do with an evolving technology. Today, we have lots of services using the web as a platform, and Tesla’s work in batteries has expanded the ways we can store power in homes and businesses. This discussion naturally leads to the question: is a law firm really a law firm?

The Law Firms Of The Past

Let’s start with where law firms have been. For roughly 100 years, the era during which law firms grew from one or two person entities to the size they are today, law firms have been service providers. The operating model was straightforward. Clients had problems and needed solutions. If the clients believed the solutions lay within the domain of lawyers, the clients went to law firms. The lawyers listened to the problems, did whatever research was appropriate, and then presented solutions. This is, of course, a simplified version of the model, but one that still functions today.

One of the many challenges to this model falls in the middle part, where I said “did whatever research was appropriate.” Think back to the pictures you have seen of lawyers’ offices in the early 1900s. Not many law books in their libraries. Research was easier and often involved knowing a small group of cases relevant to an issue.

We all know the story. The number of trial court decisions, appellate decisions, statutes, regulations, codes, and other materials a lawyer needs to consult to do that appropriate research has exploded. A search a few decades ago that found a few dozen cases today may yield a few thousand cases. In a recent decision getting some visibility, a Federal appellate court judge did some Internet research to supplement his analysis. Why? Because, according to the judge, the parties to the dispute weren’t getting to the court all of the information it needed. The volume of information relevant to a decision grows every minute.

Another way of thinking about the issue is to describe this voluminous data as a set of data sets. For example, one data set could be case law. Another data set could be statutes, and so on. Using this approach, what exists in the computers of a law firm could be a data set or multiple data sets. This isn’t a new idea, and I’m sure there are many knowledge management experts yawning at this point (and picking apart my description), so I’ll move on.

The important point is to start thinking about much of what lawyers work with and have stored in their systems as data, which then generates the question – what do we do with this data? Do we let the data sit and just call upon it (assuming we can find it) when we need to answer a question? Or, do we find a greater purpose for it? So far, we haven’t done much because the data isn’t easily accessible or isn’t very easy to use.

The Law Firms of the Future

Instead of thinking about the law firm as a service provider, let’s think about it as a data warehouse. Within its computers exists a tremendous amount of information about clients, behaviors, and outcomes. Each lawsuit, counseling session, and drafted document contains information about how clients operate, where they have risks, and where they have opportunities.

Many industry observers have commented on the failure of firms to build robust knowledge management systems to access and use this information. But, when they talk about knowledge management they usually use the term in a traditional sense. They talk about having access to the information so lawyers don’t reinvent the wheel. If I could easily access what happened in other similar matters my firm has handled, I could get to a client solution more quickly and efficiently than if I start building the wheel myself.

Another approach, is to think of the law firm data warehouse as a tool to use in predicting behaviors. Right now, I’m not aware of any law firm that could do what I’m about to describe and I don’t think any are even close. But, if we don’t imagine the future, it will be harder to get there.

The law firms of the future could track the data streams coming in and going out to build very interesting data warehouses. The information stored in their computers would move beyond simple knowledge management into very interesting data sets. With those data sets, lawyers (or at least technologists working with lawyers) could build models to use in predictive analytics.

What would be in these data sets? Instead of tracking descriptive information about the document (client name, date created, matter name, type of matter, etc.) the data captured would be client relevant. For single plaintiff employment cases, it might be demographic information about the plaintiff, geographic information about the employment locus, and information about the claim specifics. Assume we identified two dozen data points and captured them for every case. Within a year, the data set at a firm that handles a substantial number of these cases would be quite impressive.

From that data set, we could start running predictive analytics models. We would start learning about the triggers for claims and what behaviors to avoid. Combined with external data sets (the EEOC makes available information about claim frequency and outcome), we could build a model that prompts some questions. What things should a business do to reduce or avoid certain types of claims? Where should a business do more training? What type of training would help?

Instead of driving behaviors based on experience and what an individual lawyers has seen, we would drive behaviors based on broader data sets and more representative information. Most importantly, we would have flipped the legal service delivery model on its head. The law firm as reactive legal service provider becomes the proactive predictive analytics firm helping clients reduce risk.

The Clients of the Future

If the role of many lawyers evolves to predictive rather than reactive, why won’t clients take on this responsibility and stop using law firm lawyers? They won’t for many of the same reasons IT departments and HR departments outsource some of their work. The skills needed to do high quality predictive lawyering will not be easy, or even cheap, to come by. Those law departments with ten or fewer lawyers certainly won’t hire teams of data scientists and social scientists any more than they hire teams of legal specialists today.

Larger departments will have the option of hiring the teams necessary to do predictive lawyering, but will have to address many issues if they want to do so. Will law departments want to become data analytics hubs? Will they have the tools and resources to support teams? Should it be the function of a law department to do predictive analytics?

IT and HR departments learned long ago that their value to organizations often doesn’t lie in data crunching, it is in taking on strategic tasks. Law departments that are bulking up today would be wise to look at departments that have gone before them and ask whether taking on lots of in-house lawyers is the best way to use limited resources. I think a better path to the future for mainstream lawyering will be using in-house resources to do strategic work, built on analytical work done outside.

What Will Law Firms Become

Some percentage of law firms tomorrow will still resemble law firms of the past. These may be the very high end, bespoke work firms, the so-called “bet the company” law firms. But, there is a very interesting space for law firms that see the future as a combination of humans and technology. For those firms, understanding that they are data enterprises — just like retailers are data enterprises using their interactions with customers to predict what will sell and at what price — could drive an entirely new type of legal service model. These new firms will use data scientists, technologists, and even social scientists to build business models around proactively helping clients. These will be the predictive analytics law firms.

PeopleAnalyticsLet’s be honest with each other, you dislike the billable hour. Firms may benefit from using it and some lawyers may personally benefit, but that doesn’t mean anyone likes it. Since the 1960s, when using the billable hour as the way to charge for legal services grew from odd to mainstream, lawyers have not enjoyed the tyranny of the timesheet.

I’m of that generation that filled them out by hand when we started. One technique was to keep a few of the blank sheets on your desk and as the day progressed you filled in a row each time you moved from one billable event to another. That was a bit cumbersome, because at the end of the day if you had hopped back and forth, you had to redo the timesheet to aggregate work by clients and matters.

Others would scribble notes on a yellow pad. At the end of the day (I’m not sure if this is revisionist history—end of a few days? a week?) they would fill out the formal timesheet, with everything properly grouped and cleaned up descriptions. Of course, there were those who didn’t do the clean up until the end of the month. They would take a day or two to sort everything out, re-write it all, and then submit it. Billable hours would dip at the end and beginning of a month. The world changed forever when we moved to computer-based systems.

Today, of course, there are all sorts of tools and an app for that. The experts in billing know that timeliness is critical to accuracy. And, with clients looking for real-time data feeds, the days of filling out timesheets once in a while are long gone. Contemporaneous timekeeping is all the rage. But, the tyranny of the timesheet is still there.

One of the reasons, I’m told, that we have so much trouble getting lawyers to move into the 21st century is the culture of the individual. Lawyers, because of their personalities and training, believe in the power of one—and each lawyer is the one (move over, Neo). As individuals, we like to do things our own way, not the group way. We like to come up with our own solutions, do our own research, write our own documents, and handle our own matters. We believe this approach is essential to our creativity and effectiveness as lawyers. When someone suggests that we operate as a team, that we follow a process, or that we adhere to a project plan, it threatens our “oneness.” While clients may pay the price for this approach, they still have the privilege of working with us and that should make up for any monetary inconvenience.

The Emerging Science of People Analytics

Well, hold on to your seats lawyers, things are going to get bumpy. One of the challenges with implementing process improvement in the lawyer workforce is getting specific about how time is spent. Yes, we record our days in six-tenths increments, but the descriptions and accuracies of those reports are coarse, at best. At the beginning of the 20th century, when lawyer time sheets were first invented, we would have used the observation method to overcome this challenge. We would stick someone with a stopwatch in your office and have them record what you did and for how long. After a week or two, we would have a detailed record of how you actually spent your time, and we could dig into process improvement. Please note, you really don’t want us to use observations because you would find about 60% to 80% of your day does not add value.

Well, you may have heard that technology, or rather software, is eating all things (a nice phrase from venture capitalist Marc Andreesen). The latest bite is something called “people analytics.” It “uses time management data to help companies understand the relationships—external and internal—driving corporate decision-making.” If you are wondering whether people analytics is a real thing, Wharton has already had two people analytics conferences, so it must be real.

It gets better. Microsoft just announced it is acquiring VoloMetrix, a software company that does people analytics. VoloMetrix’ software looks at emails and calendars and creates analytics based on how people are using their time. For example, if a manager spends a total of 30 hours a week sending emails to and meetin with her managers, she probably is managing up rather than spending most of her time on productive work. Microsoft intends to develop the software and then embed it in Office 365 as part of Microsoft’s efforts to use software tools to improve productivity.


Yes, Outlook may tell you to stop sucking up to the boss and get on with it.


Alex “Sandy” Pentland, who directs MIT’s Human Dynamics Laboratory and the MIT Media Lab Entrepreneurship Program, is one of the leaders in the people analytics field. His team developed sociometric devices—smartphones using special software—that teams of employees would wear during the day. The devices measured proximity to other employees, who was talking, engagement levels, and other data points. They did not capture what was being said. But, from this data Pentland’s team could determine which group dynamics led to more creativity or productivity. By altering the work situation, such as aligning work breaks rather than staggering them, Pentland’s team drove performance improvement along many metrics.

In the professional services world, people analytics is being used to revise ideas about how to retain employees and what makes them more satisfied at work. For example, McKinsey & Company has used people analytics to significantly reduce consultant turnover. Using data rather than “who knows whom” has helped many organizations uncover what employees really are doing and thinking.

The Lawyers’ World Of Don’t Bother Me

At the other end of the spectrum, we have the legal profession. Lawyers fight the concept that anything they do can be broken into data points, measured, and used for improvement. If you view yourself as unique, a law firm of one, who does things your way because that is “the way” you must confront a harsh reality—the world doesn’t believe you. Corporate law departments will feel the influence of these new approaches to human resources. Lawyers in those departments will find themselves being measured and evaluated under people analytics the same as other employees in their organizations.


Clients will wonder why lawyers in law firms don’t use more sophisticated approaches to people management, and law firms will need to be ready with answers.


People analytics, like any data analytics approach, is not the tool that solves everything. To assume that one could use people analytics but ignore the people would result in disaster. People analytics must be used with other tools to develop a well-rounded picture of employees. This is particularly important as human skills become more important in the workplace. But, we also must avoid the urge to ignore data when it comes to performance. A happy but unproductive workforce will not help the law firm or the client.

Use The Data Tools

To paraphrase Lewis Carroll, the more we fight the behinder we get. Providing legal services today involves much more than listening to a client’s problem and giving an opinion or delivering a document. It is a complex task in a fast moving environment that involves a much deeper and more nuanced understanding the environment in which the client operates. This isn’t an equation solely for large law firms and corporate legal departments, it is true throughout all levels of legal services delivery. Individuals’ lives are much more complicated today than 10, 20 or 30 years ago, so advising them isn’t as easy today as it was then.

Understanding and using data analytics generally, or even people analytics specifically, is not giving in to the dark side. It is recognizing that having 1.3 million lawyers in the United States (2 million in the world) all doing things their own way does not help anyone, including clients and their lawyers. Would you feel good if each of the almost 1 million doctors in the United States decided to practice medicine in his or her own unique way, including developing his or her own way to treat diseases? That was medicine 100 years ago and I wouldn’t want to go back to that age, yet that is law 100 years ago and today.

It may be some time before we see law firms using people analytics to improve the performance and satisfaction of lawyers and other individuals in law firms. Lawyers will decide whether law firms not addressing the needs of those who work at the firm are the best places to pursue a career. The more telling moment will come when clients decide those law firms also aren’t the best legal service providers for their needs.

BigDataIn a recent Harvard Business Review article, Kira Radinsky makes the argument that we need to start thinking about companies that have large historical data sets and block others from getting access to them as potential monopolists. Ms. Radinksy, the CTO and Co-founder of SalesPredict, explains how some technologies, like search engines, operate best when they have deep historical data to include in their algorithms. For example, one leading search engine performs 31% better with deep historical data. This performance metric raises the obvious implication, if true more generally, that without a deep historical data set your organization’s performance could be at risk. Ms. Radinsky points out that around 70% of organizations “still aren’t doing much with big data.” In other words, if you don’t have a big historical data set now, and if it is hard to build one, you may be out of luck.

Lawyers Without Data (Avocats Sans Données)*

When it comes to the legal industry, we know a different story. While some players are starting to use data and some new entrants (presumably LegalZoom and Rocket Lawyer, for example) are generating big historical data sets, overall we are a data set poor industry. I’m ignoring data sets that are built on billable hour data, since I consider such data both highly unreliable and of questionable value. I also am ignoring case law, a data set of better quality and higher value. While there is nothing wrong with case law, it is a highly derivative data set with some distinct peculiarities, so I’m not sure yet how much overall value it brings.

The data sets that we really want, that would add value, aren’t being built. These data sets would give us rich insights into individual and organizational behavior and risk management mechanisms. It is one thing to do an ediscovery search and find the incriminating emails in an antitrust lawsuit. But, it is something else to watch the email flow, catch the emails when created, and change behavior so the lawsuit never happens. While this may sound futuristic, there is nothing that says if we can search after the fact, we can’t search before the meltdown. Now, imagine what we could do if we could aggregate some data across companies and industries.

We can take this concept further in other areas. Instead of fraud detection we have something called fraud prevention. Patterns of behavior trigger alarms telling retail loss prevention teams that they should conduct an investigation. As they learn more about risk, those teams change behaviors in the organization making it more difficult for the fraud to arise.

As lawyers, we should have high interest in matching behaviors to outcomes. Rather than drafting a contract “tightly,” why not focus on setting up the commercial arrangements so the likelihood of a breach is remote. I have (often) seen parties do quite the opposite. In an effort to “protect” themselves, some parties twist the negotiations and drafting so tightly that it is hard to conceive of anyone complying with the contract. Shock and dismay ensues when the parties get into a dispute and find that neither of them was complying with the contract terms.

Ms. Radinksy asks whether there should be a Sherman Antitrust Act for data. I think the answer is no. I believe the Act is sufficient to address situations where a company acts improperly to obtain a monopoly position, whether through data acquisition or some other means. Rather, I think the question is to what extent are we comfortable with companies building large data sets and then using them to modify behavior, even if they do so legally. That is a public policy question, not a legal question.

There are many reasons in favor and against allowing the growth of big data sets that can be used to modify behavior without additional policy limits on their use. Those arguments, however, start to slide into the area of our comfort level generally with data having an ever-growing impact on our lives. Zosha Millman just published a nice article on lawyers living in a machine-learning world, and it touches on some of the big data issues lawyers will need to confront.

Lean Thinking and Big Data

When we approach things from a lean thinking perspective, our goal is to do as much as we can using simple tools and approaches before we contemplate technology. As soon as I jump from person to tool, I increase the complexity. Tools must be built, maintained, replaced, and generally have limited utility. Tools often beget more tools, creating a complexity build-up.

When we go to gathering big data sets and then using that tool, and we haven’t thought through the process, we find surprises. Big data is here to stay (and grow). But, we are finding lots of surprises as we jump to using it without having worked through the processes. Lawyers are not deep in the big data trenches yet, so we have an opportunity. As we look towards building our own big data sets, we should first start thinking about the processes surrounding the acquisition and use of those data sets. Then, perhaps, as we build the sets we can avoid some of the surprises our clients are grappling with today.

* Doctors have Doctors Without Borders (Médecins Sans Frontières) to help the world, so perhaps we need an organization for lawyers recognizing our contribution to the world by “operating” without data.

The Friendship Algorithm of Dr. Sheldon Cooper (The Big Bang Theory)

Lawyers have the reputation of being, shall we say, weak when it comes to math. Of course, many lawyers stopped taking math when they graduated high school and followed degree paths in college that helped them avoid math. Some lawyers are math proficient (e.g. economics majors, most IP attorneys), but there is enough truth in the “weak in math” stereotype that it persists. As we start talking about using metrics in law, we can see many lawyers start to twitch and shiver, as nightmares of math tests come racing back. So, with full recognition that many lawyers already are traumatized by the modern fascination with big data, I bring you the newest hurdle: algorithms.

You Already Know Algorithms

An algorithm, defined informally, is “a set of rules that precisely defines a sequence of operations.” Algorithms exist all around us and you interact with them ever day (at least you do if you touch a computer). According to Alex (Sandy) Pentland, Professor Computer Science, MIT; Director, Human Dynamics Lab and the Media Lab Entrepreneurship Program, lawyers are very familiar with algorithms. As Professor Pentland says, “creating a law is just specifying an algorithm, and governance via bureaucrats is how you execute the program of law.”

That probably doesn’t give you much comfort, because most lawyers don’t think of themselves as cogs in an algorithm machine. They like to think that what they do has a higher meaning and design. Perhaps lawyers do exceed basic algorithmic work some of the time. But, it is hard to argue that much of what lawyers have come to do is not the application of algorithms to routine problems. You should become comfortable with the idea of algorithms, because they are becoming very familiar to your clients.

Algorithms are Transforming Your Clients

Ram Charan, the famed management consultant, recently published an article in Fortune called “The Algorithmic CEO.”[1] In the article, Charan explains how the digital revolution is affecting the leadership of businesses. Charan starts the article by saying, “The single greatest instrument of change in today’s business world, and the one that is creating major uncertainties for an ever-growing universe of companies, is the advancement of mathematical algorithms and their related sophisticated software.”

While Charan acknowledges that some of the change is in its early stages, the pace and magnitude of the change are such that this algorithmic transformation already is affecting businesses. Charan closes the short article by saying:

To some degree, every company will have to become a math house. This will require more than hiring new kinds of expertise and grafting new skills on the existing organization. Many companies will need to substantially change the way they are organized, managed, and led. Every organization will have to make use of algorithms in its decision-making. The use of algorithms will have to become as much a part of tomorrow’s management vocabulary as, say, profit margins and the supply chain are today. And every member of the executive team will need to understand his or her role in growing the business.

Be Algorithmic Or Be Forgotten

Lawyers, law firms and law departments that want to compete in this algorithmic world will have to more than hire a few techies. The practice of law is not immune to the algorithmic future, and legal service delivery will not be insulated from the world of computers. Lawyers and law organizations that want to survive the re-structuring of the industry must become adept at understanding and using technology, including algorithms, to deliver what clients want in legal services. Waiting for clients to request such changes will put the legal organization at an extreme disadvantage. By the time the client asks, the need is so well developed that an unresponsive department or firm will be well behind the curve to develop and adequate reply.

[1] The article is based on Charan’s recent book, The Attacker’s Advantage.

UTBMS CodesWhen I talk about metrics with many people in the U.S. legal industry, the conversation frequently turns to the use of UTBMS (Uniform Task-Based Management System) codes. The UTBMS system has been around for a while (Jim Hassett wrote a nice piece about UTBMS code history and resurgence posted here) and I know many in-house lawyers are fans of the UTBMS system. I, however, have never been a fan of the system. As I have worked over the years to get lawyers interested in becoming more efficient, more productive, and to drive quality higher, and as the metrics discussions turn to UTBMS codes, I have become more frustrated. To me, UTBMS codes take us in the wrong direction. So, recognizing the argument that sometimes you make do with what you have, let me explain why I think UTBMS codes are not the way to go.

Self-Reporting Inaccuracy

Let’s start with the basic premise of the UTBMS code system – self-reporting. Timekeepers are required to self-report what they do throughout the day, using the codes, typically in six-minute increments. Self-reported data, especially when it involves things like measuring time spent on tasks, is notoriously unreliable. We can explore that a bit more.

Most firms require timekeepers to submit their time daily. Some timekeepers are diligent and record time throughout the day; others record time at the end of the day or intermittently. There are still many instances when timekeepers don’t meet the daily requirement. As the length of time between the event and the recording of the event increases, accuracy decreases.

Even when time is entered contemporaneously, we have a fair amount of inaccuracy built into the system. Assume someone does a quick task on a file that takes four minutes. They probably will enter that task as a six-minute task with a UTBMS code. That means there is a 50% error rate in the time entry (2 minutes/4 minutes). Put another way, the timekeeper recorded 150% of his actual time. Of course, the error can go the other way. The timekeeper might spend eight minutes on the task, but only record six minutes, a 25% reduction.

It is possible that with enough time entries using the same UTBMS code, these variances will even out. But, it also is possible that a given timekeeper always rounds up (anything over six minutes becomes 12 minutes) or rounds down (anything less than 12 minutes becomes six minutes). You won’t know when you look at the reports from your ebilling system. In fact, it would take some investigative work to find out. You would have to do time and motion studies of each attorney, and compare your results to what the attorneys records (and at the same time avoid what is called “observer-expectancy effect” – having the lawyer’s behavior change because he is being watched).

To further complicate matters, we are assuming that the lawyer knows whether he spent four, eight or eleven minutes doing a task. People can be good at gauging time. If each lawyer recorded his or her time immediately after finishing the task, the entries might be reasonably accurate (again, we wouldn’t know for sure unless we did some studies). But, if the lawyer doesn’t enter his or her time until later, the accuracy rate will fall. What might have been a reasonable “I spent about four minutes doing this” becomes more of a guess or fill in the blank (“I worked on this starting around 10:15 and had a meeting at 10:30, so I probably spent 15 minutes on it).

In my early days of learning lean, I did a lot of time and motion studies. The time people thought it took them to do something varied considerably (that is, often off by more than 100%) from the time it actually took them to do something. We never used self-reported data because it simply wasn’t sufficiently accurate.

Coding Inaccuracy

UTBMS codes also raise the problem of coding error. Some portions of the UTBMS code system are more robust than others (e.g. litigation versus corporate). We know that to get anything out of the system, we have to make sure each timekeeper is well-trained in how to apply the codes to work. The training process introduces another source of variance.

Multiple trainers often have different versions of the truth – which code to use in a given situation. This variance becomes more pronounced when the trainers are from different law firms. We also have variances that creep in over time. The timekeeper walking out of the room applies the codes one way, but over time her practices creep. She shifts how she applies the codes, either because a particular client views tasks differently, or because the partner she works for has his own way of doing things, or just because she adopts practices that are easiest for her. The more timekeepers there are, the more variance in approaches, the more the quality of the data decreases.

Other Variable Inaccuracy

Of course, another issue with UTBMS codes is that they ignore the many variables that impact performance. Let’s look at some examples.

Assume we have two single-plaintiff lawsuits where the issues and facts are relatively similar, and both are in the same jurisdiction so the law applying in each case is the same. Lawyer 1 is handling the first case and Lawyer 2 is handling the second case.

One of the premises of the UTBMS code system is that we can use the data to compare performance by lawyers. We assume we can look at the time spent by Lawyer 1, compare it to Lawyer 2, and draw a conclusion about which lawyer is more efficient.

But, there are far too many variables with values we don’t know to make such a judgment. The following list contains just a few examples of those variables as they could apply to one piece of the case-the plaintiff’s deposition:

  • Was the plaintiff in one lawsuit very experienced with depositions, and the plaintiff in the other inexperienced?
  • What impact did the plaintiff’s attorney have in each case (preparing the plaintiff, at the deposition, otherwise in the case)?
  • What other factors affected the deposition (e.g. mood of each participant, logistical issues)?

We can extend this list, and then create similar lists for each aspect of the lawsuits. Because the process for each lawsuit is largely uncontrolled, the number of variables affecting the lawsuits and the ways in which each could affect each lawsuit or combine makes the comparison weak, at best. While it is possible that over the course of a large number of lawsuits, the variances may even out, in reality we seldom if ever have sufficient lawsuits to make the comparison. In any event, we don’t track and measure the other key variables affecting performance on each of the lawsuits so it would be difficult to draw any conclusions about the lawyers.

Measuring Effort Not Value

Yet another challenge of UTBMS codes is that they measure effort, not value. Even putting aside other issues with UTBMS codes, all they measure is effort. We only know how many minutes it took to accomplish the task. As hopefully everyone in the legal industry knows today, effort does not equal value. Whether a lawyer spends one hour or 10 hours on a task, we don’t know how that task ties to the value of the matter, such as the outcome of the lawsuit.

Let’s go back to our two lawsuits. In each case, the time required to prepare for and depose the plaintiff was 10 hours. The effort required in each case was the same. But, in the first case the plaintiff’s deposition was pivotal to the outcome. In the second case, the plaintiff’s deposition had no impact on the outcome. The effort was the same, but the value was quite different. Because we aren’t measuring value, we aren’t comparing like things. If we magnify this problem by considering that for each step in the lawsuit we only measure effort and not value, you can see how we get to a big disconnect between what we are measuring with UTBMS codes and valued outcomes.

Another way to look at this is through the lens of behavior change. When we consider those two depositions, each taking 10 hours, we come to different conclusions about what to do if we also consider value. For one deposition (little value), we would want to reduce the time (effort) spent on the deposition. For the other deposition (high value) we may want to increase the time (effort) spent on the deposition (for those who would say we don’t know in advance, the simple case could be taking more time in the deposition itself). Without evaluating value for each piece of the lawsuit puzzle, we don’t know whether to increase, decrease, or not modify the effort. And, if we do adjust the effort, there is not guaranty we will positively impact value.

A Tool Past Its Prime

Back in the mid-1990s, when UTBMS codes first came about, we really did not have great alternatives. We were doing almost nothing with alternative fees and we knew very little about applying lean to services. We were, however, generating a lot of billable hour information, we already were capturing task descriptions, and putting some standardization around what we already were doing made sense.

In the past 20 years, we have learned a few things. Today, we have better tools. We can use value stream maps, lean accounting, and business-focused metrics to track processes, measure value, and make changes tied to value.

Continuing to spend a lot of time and effort on UTBMS codes, in my opinion, does not add value to improving the efficient, productivity, or quality of legal services. I don’t want the least effort low value service. I want a high value service delivered efficiently. To accomplish that goal, I want to focus on what adds value, measure the value, and ruthlessly eliminate things that don’t add value or detract from the value. I want the entire improvement process to deliver what I need (meaning don’t provide more or less service than I need).

Send me an email or a tweet and let me know if you disagree, why you think there still is a place for UTBMS codes.