The global legal community is talking a lot about the potential of software and tech tools. Others in the community are talking about the so-called “resistance” of lawyers to working with technology at all. Whether you believe there are such starkly defined “sides” on this issue, one conclusion is unavoidable: there is a sea change underway in the legal profession, indeed in the entire economy. One of the main drivers of this sea change is artificial intelligence.
But AI is not some monolithic thing. Though it is changing the world and will continue to do so, AI does not represent an unprecedented disruption of life as we know it. Technological revolutions have been common for centuries. But the spread of knowledge that occurred in the wake of the invention of the printing press, for example, did not end book production. There may have been people in dark corners claiming that the printing press meant the “death” of printing. Were they right? To an extent. Professional book printers – people writing copies of books by hand, by candlelight – probably had to change their business practices.
But on a massive scale, the world improved after book production became easier and standardized. For the most part, economies thrive from such innovation. AI represents the next great moment of endemic change.
Building a Data Strategy
Much work needs to be done to demystify a new technology when it comes along. For example, some people think AI is a giant, all-knowing robot brain that rips decision-making out of the hands of humans. This is far from the truth. AI is just software, built from data. In order for AI to draw any meaningful conclusions from data, in fact, we need a vast amount. Where do we get all that data? How do we know it’s the data we need?
To answer these questions and many others, we need a data strategy. A data strategy consists of two high-level core concepts. The first is a top-down mission statement acknowledging the value of an organization’s data. The second is a framework for developing new data-related capabilities. Think of it like a doctor first diagnosing a patient, and then recommending exercises and treatments to improve and maximize that patient’s quality of life.
A good data strategy takes an inventory of an organization’s goals, and of its resources, both human and technological. Neither people, nor AI and other tech tools, can function well in an organization where data is not managed well. However, humans and machines function superbly in tandem, with well-organized and appropriately accessible data.
Managing Legal Data
It’s easy to talk about data in the abstract, because data is somewhat abstract. At least at first. To convert data into something tangible and useful, your organization needs to consider four aspects:
- Recognition: Is useful data recognized as such? What is potentially being ignored?
- Storage: Is data being effectively stored? Is anything missing?
- Publication: Are there directories of all data? How are these directories structured and organized?
- Accessibility: Who has access to the data? How is the data used? Does everyone know how to find the data they need?
This list is unranked for a reason: a good data strategy – in legal, or in any other field – moves through these 4 stages in a cycle. Each aspect leads directly into the others, with constant improvement and iteration the ideal.
Let’s look at each of these four elements of a good legal data strategy, bearing in mind that each element is part of an ever-evolving cycle.
The recognition stage of a legal data strategy represents the first place where people and technology start to integrate their respective roles. Recognizing data you have in your organization is not always easy. Oftentimes, we take a lot of basic data for granted. In documents, entity names, dates, contract lengths, and much more can be automatically recognized by trained software tools like ContraxSuite. In emails, Google, Outlook, and other services keep track of metadata and content.
Data recognition doesn’t just happen at the software level, though. Legal professionals at every echelon need to work together to address what kinds of data the organization has, what kinds of data the organization needs, and whether current capture methods are getting the job done. If your organization has a lot of European clients, do you know what aspects of your agreements with those clients are subject to change under the GDPR? Do you know what your risk exposure might be in the range of that discrepancy?
We gather data so we can make predictions about the world. If we gather enough data, over a long enough period of time, we can be confident that our predictions will be accurate and carry significant weight. This is why data storage is so important. Recognizing good data is not enough; we also have to store our data in a meaningful, organized way. This is where technology tools like document management systems become integral to an organization’s data strategy.
An effective legal data strategy will reliably store both structured data and unstructured data. Cloud management systems, and other forms of DMS, have made it easier than ever to store, organize, and utilize structured electronic data.
Once an organization has begun recognizing and storing data, the next stage involves normalizing the data so it is easily accessible. Unstructured data needs to be processed for specific features so that it can become structured data. Standard forms need to be discussed and agreed on in order to reduce redundancy and improve data integrity. Proper data warehousing needs to be established.
Effective accessibility means having a clear system for retrieving, analyzing, extracting, transforming, and otherwise managing data. This is largely a human-centered task; AI software can’t tell you the best way to run your organization.
We don’t just need to recognize and store data and make it accessible, though. We need to use it. We need to produce something from it. This is another arena where humans and AI programs can work together.
“Publication” in this sense doesn’t necessarily mean that an organization is publishing and releasing company data for the whole world to see (although this often does happen in the form of SEC filings, earnings reports, press releases, etc.). Publication here refers to maintaining a directory of data within an organization. A DMS can aid in this process, but a DMS might have a default method of organizing data. This default method may or may not be the best way to communicate important data to others in your organization (e.g. some document management systems may not be able to build helpful diagrams and charts to communicate a focused message about a particular matter).
Publication is heavily focused on communication. What is our data telling us? What conclusions can we draw from an analysis of all this data? Many actionable insights come from this stage.
Building a Legal Data Strategy
For more information, visit our legal data strategy website. Keep in mind as well that tools like LexSemble and ContraxSuite can be integrated into a larger legal data strategy. Contact LexPredict to start a conversation about how we can help you build a data strategy for your legal organization.
LexPredict is an enterprise legal technology and consulting firm. Our consulting teams specialize in legal analytics, legal data science and training, risk management, and legal data strategy consulting. We work with corporate legal departments and law firms to empower better organizational decision-making by improving processes, technology, and the ways people interact with both. We develop software and data tools, and also offer execution and education services.
LexPredict has a number of software and data products, including LexSemble, ContraxSuite, CounselTracker, and LexReserve. These products assist organizations with early case assessment and decision trees, contract analytics and workflows, outside counsel spend management, and case valuation. LexPredict also offers advisory and capital services for legal tech startups through its LexGen Ventures arm.