7 Steps to Better Data-Driven Problem Solving in Your Legal Practice

The term “problem-solving” gets kicked around in legal firms like a soccer ball at a picnic, but how often do we put time and energy into streamlining this activity? Let’s face it, there are so many things demanding our attention at work that we’re spending all our time putting out fires instead of preventing them.

If the status quo doesn’t sound very productive or profitable, you’re right—it’s not! As with most other aspects of your business, developing a system around problem-solving strategies can put your firm miles ahead of the competition.

On the McKinsey Podcast, McKinsey & Company senior partner Hugo Sarrazin recently explored the benefits of a systematized problem-solving process. The podcast features a compelling discussion with Charles Conn, coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything, that provides a framework for understanding and using data-driven, BI-enabled approaches to streamlining repetitive and quantifiable tasks (such as generating reports). The podcast recommends this 7-step process to improve and expedite your firm’s approach to problem-solving:

Step 1: Define the problem.

In this step, you and your team should put considerable thought into addressing three questions:

• What are we trying to solve?
• What are the constraints that exist?
• What are the dependencies?

By clearly framing and defining the problem, you enable all stakeholders to have a voice in the solution and you also help the most important aspects of the issue to bubble to the surface.

Step 2: Break the problem into smaller pieces.

Use a logic tree (also known as a decision tree) to delve into the problem’s complexity and uncertainty. Determine what data is necessary to solve each piece and whether that data is readily available.

Breaking the problem into smaller parts can help you determine which aspects of the problem are best to focus on and how to assign appropriate solution-finding tasks to team members.

Step 3: Rigorously prioritize.

It can be easy for logic trees to grow an unwieldy number of branches, and prioritization is key to keeping the layers manageable. Ask yourself:

• How important is this branch of the tree in terms of the overall problem we’re trying to solve?
• How much can we move the needle on this specific aspect of the problem?

The goal of this step is to identify where and how your efforts can have the biggest impact on driving solutions.

Step 4: Develop a work plan.

Once you determine what you’re going to work on and in what order, define your team’s process for getting things done. This step may include detailed schedules, process documents, and communication protocols. It is your job to pinpoint the data, structures, and resources needed to solve the problem with enough depth and precision to meet your given criteria and timeframe. Once you put your plan on paper, take a step back and ask:

• Is the proposed plan realistic?
• Is it appropriate?
• Will it truly solve the problem at hand?

Taking a moment to analyze and reflect on your plan is crucial to determining whether you should re-examine your assumptions or move forward with implementation.

Step 5: Analyze the data.

Slice and dice your datasets, run your algorithms, and explore your visualizations—but remember that you are the subject matter expert in your field, not the machine. At every step of data crunching, pause to assess whether your data is appropriate and valid. Are the patterns revealed by machine learning going to lend insight to a solution, or are the biases of the algorithm going to get in the way?

Never forget that analysis is an iterative process.

Step 6: Synthesize your discoveries.

Take the results of your analyses and think about them at a high level.

• Do your discoveries mesh with each other?
• Do they make sense?
• Do they lend themselves to concrete actions that your team can take to solve the given problem?

If not, repeat Step 5 and tweak your process before synthesizing your discoveries again. Sarrazin and Conn warn not to confuse analysis with synthesis; they are two distinct and equally important processes.

Step 7: Answer the question, “What should I do?”

If you’ve completed Steps 1 through 6 correctly, you’ve reached the core of the problem-solving process. The proper synthesis will reveal what your team should do, how to do it, and when.

Execute, then assess the results and begin this process again.

Why it Matters

Systematic problem-solving, while extremely valuable, can be tough to implement consistently. Why go through the trouble of adopting it into your action-packed, mile-a-minute work life? Why push to make it part of your firm’s culture?

Because it’s good business.

Systematic problem-solving saves time and, therefore, money. It prevents “this is how we’ve always done it” thinking. It helps you reach better solutions for your clients and your firm’s employees. Most importantly, it empowers your attorneys and staff by giving them a go-to process they can trust to drive positive results.

In short, problem-solving systems are essential for efficiency, productivity, and profitability.

How Business Intelligence Systems and Technology Fit In

Effective problem-solving systems rely on data. How much did we bill? What was last year’s growth? What are our future projections? If you can’t access your data quickly and easily, your problem-solving process—whether systematic or not—can slow to a crawl.

Innovative BI solutions such as Termi put data in your hands. No more asking support staff to pull reports, no more waiting for information, no more guesstimating because accessing hard numbers is such a pain. With Termi, your data is all right at your fingertips, when and how you need it.

Termi keeps your focus on the problem-solving task instead of the data-tracking minutia. Its dashboards and interfaces make finding information both simple and intuitive. Termi also makes data available to a variety of stakeholders, allowing different viewpoints to be brought into the problem-solving arena.

Think About It

Simon London, host of the McKinsey Podcast, begins the episode by saying, “Many would argue that at the very top of the [number one skills] list comes problem-solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.”

Determining the system your firm will use for problem-solving requires practice, thought, and attention. If mastered, better problem-solving abilities can be a key driver for boosting your firm’s bottom line. We all encounter obstacles on the path to success, so don’t just meet them; anticipate and mitigate them.

Interested in learning more about how Helm360 can improve your firm’s BI systems? Would you like to see Termi in action? Contact us for a personalized demo!