How to introduce new data intelligence tech in a law firm

Faced with numerous marketplace pressures to increase revenue and profitability, 54% of legal professionals reported investments in additional legal tech in the past year.

Law firm leadership has recognized that organizational resistance to data intelligence is almost foolhardy, given the availability and affordability of tailored data technology solutions and the massive ROI they can generate. For those firms that have decided to enhance their data intelligence capabilities, the intimidating task of choosing and integrating the right tools is a technological black box.

Once firms have shined a light on that box by choosing the right solutions and providers, the ensuing change management process is no less of a challenge, loaded with a million questions, starting with “where do I begin.” Here are some guideposts and best practices that will help alleviate the pain points associated with firms moving toward and completing their digital transformation.

Reverse engineering by beginning with a key business question

Many firm leaders taking charge of the implementation of new technologies might immediately dive into assessing the organization’s existing data since IT or analytics leaders will advise that clean, useable data is a major priority.

Contrary to what many firms may think, however, they need not get their “data houses” completely in order with big data-cleanup projects before starting to use advanced analytics. While the health of data sources is no doubt critical, firms should begin with a reverse-engineering mindset, by starting with the end in mind through a focused business question. The team should take a close look at the firm’s current strategic landscape and determine what key business questions are truly in need of answers. The firm may need to find out where the biggest organic client growth opportunities are or which teams are most effective at cross-collaboration in a client network, or predict which clients are most at risk of decline.

Once a firm knows what questions need to be answered, the team should form a list of hypotheses that can be tested with the data to help it focus on what data sources to examine. However, the team should avoid trying to “boil the ocean” by attempting to address too many business questions and hypotheses all at once. Testing out new analytics programs or data technology tools on just one or two focused, impactful business questions will set the stage for both early success and a smoother implementation long-term, by clearly distilling down what’s most important in terms of attention and prioritization of firm data assets.

Beginning the data discovery process to ensure actionable answers

With key questions and hypotheses well-defined, the firm can quickly start to determine which data sources are most important, whether it be finance data, people data, client engagement data, internal team communications data, or other external sources.  A good data assessment looks at the quality of the data, including aspects like the completeness of the data in the source, how many years the data source has been in use, the consistency of the data, and the data’s maturity in terms of firm adoption.

Perhaps the firm is unable to glean accurate data insights into metrics like time spent on certain work tasks or client CRM activity. Factors that might render a data source less useful might include: data belonging to a third-party vendor with limited licensing access; messy internal taxonomies; inconsistent finance data due to mergers and acquisitions; or a software system without modern APIs that makes it tough to extract and aggregate data.

However, firms should not balk at moving forward simply because they do not have perfectly clean data sources across the board. It can be a significant mistake to delay starting data analytics projects by seeking, and failing to achieve, perfection. Firms are often already sitting on ample value within their data, with the potential to extract powerful insights that they can turn into action. The best thing firms can do is begin sooner rather than later by starting to measure whatever they have available in an effort to better understand the drivers of different business outcomes.

Winning data intelligence buy-in from the ‘coalition of the willing’

A meaningful factor in becoming a data-driven law firm is creating a data-driven culture. And that means winning buy-in from key stakeholders in the organization by demonstrating that the insights are indeed actionable and create tangible business impact. Winning hearts and minds often mean showing people that this new technology will make their work lives better.

Once a firm has determined the most potentially fruitful data sources to aggregate in order to answer its key business question, it must decide to which departments and managers the analytics tool will roll out first. Some enthusiastic analytics champions will presume that there’s no harm in rolling out a new tool or system to many practice areas at once or even the entire firm.

But there’s no sustainable ROI in a splashy solution trying to serve too many masters, too hastily. Instead, if a small group of stakeholders sees the concrete benefits, and if a test case can lead to new processes that enable attorneys to improve efficiency or reduce cost, then that handful of willing leaders will observe the ROI and socialize the insights. The firm can then refine the positioning of the technology as needed for a larger audience and wider distribution.

Scaling new data-driven technology within the firm

A successful technology implementation strategy will be stratified according to stakeholder groups. This initial test group of stakeholders, end users, and partners or leaders are going to consume the insights which the tool outputs, and then take actions that create value. They are critical to scaling the technology because they will either feel empowered to make more informed decisions or they will tell you the tool is not generating tactical intelligence. Rapid, successful proof-of-concept wins create momentum, demonstrate ROI, and provide the visibility that organizations need to ultimately scale and capture significant value.

The firm’s systems owners and domain experts are a pivotal group — people who every day eat, sleep and breathe the data systems from which you’re pulling information. Without the right level of business context, you’re going to misinterpret and produce incorrect insights, even with the most powerful algorithms and analyses.

The third group will be “project sponsors,” senior people who can evangelize it with other senior leaders to help support change adoption.

The dedicated data science and business analyst teams are the fourth group, which can bring all these stakeholders together to execute the analysis, produce insights, and craft results into a business narrative that satisfies everybody.

Firms should beware of rolling out data intelligence solutions in “IT ticket” style, in which they end up with too many bespoke requests. This excessively customized approach can lead to a sort of pipeline of service tasks rather than a reusable and scalable intelligence tool.

It is also inadvisable to omit one of the above stakeholder groups in the early stages because it inevitably creates problems where the project may fall on its side and won’t be successful in the long term. If one of the crucial stakeholder groups is unavailable, the firm should wait until it can commit all necessary groups to the project.

Data technology adoption, for large organizations in particular, is all about telling positive stories around the various stages of impact and user adoption. This level of positive engagement has a snowball effect that gets people to believe in the value of data and trust it, and subsequently creates evangelists who reinforce positive adoption behaviors. A large firm can then confidently roll out a new data intelligence tool to a hundred lawyers who otherwise might not be naturally keen on bringing technology into their daily job activities.

Gaining a competitive edge using data intelligence

The pandemic has pushed numerous industries, including professional services, further toward digital transformation and data technology adoption.

Legal technology incorporating data intelligence capabilities will soon be table stakes in our industry, but until then law firms leveraging early digitization can enjoy a decisive competitive advantage. Just 32% of law firms say they are very prepared to use technology to be more productive, while 77% acknowledge the importance of legal technology.

Firms are executing remarkable initiatives using analytics technology; for example, building a more inclusive and diverse culture, introducing new offerings to existing clients at the right time, identifying potential client attrition risk factors, improving team dynamics, and accelerating firm-wide cross-collaboration — all of which drive rapid financial impact.

When executed thoughtfully, the adoption and integration of data-driven analytics technologies can be a fruitful and manageable process, enabling firms to dissolve cultural resistance, gain a stronger competitive edge, and protect revenues from future volatility.

Paul Giedraitis is founder and president of Orgaimi.