Data Driven Decision Making: Why Your Billing Data is So Valuable
News about “data-driven” solutions in various business verticals is frequent.
The good news for lawyers is that the advances in Artificial Intelligence (AI) and machine learning that make the data-driven solutions possible are also available to law firms and corporate legal departments.
Attorneys can harness these valuable technologies to bring significant improvements to various internal processes and workflows, while also standardizing their internal data to improve the accuracy of decision-making.
Data Driven Decision Making
Before diving into why data-driven solutions are on the rise, it’s important to review what “data-driven” decisions are and why the concept is becoming more valuable than ever.
Technopedia, an IT dictionary for computer and technology terms “data-driven decision making” (DDDM) as:
making decisions that are backed up by hard data rather than making decisions that are intuitive or based on observation alone.
The idea behind data-driven decision making is to form thorough and empirically-based decisions based on data sets which show “projected efficiency and how they might work out.”
Tom Davenport, an analytics advisor and Forbes contributor, forecasts data-driven businesses will continue to invest a push for “data culture” that will lead to new innovations.
Davenport further predicts that AI and machine learning will also continue to grow in popularity. Similarly, he also predicts automation technologies will continue to emerge to help “workflow, business rules and process mining” to free up time that can be spent elsewhere to complete higher-level work duties.
An Example of a “Data-Driven” Solution
Data-driven business solutions often use complex interrelations between various types of information and workflows. However, a simple example illustrated what a “data-driven” solution looks like, and how beneficial automated data analytics can be for a business.
Imagine a simple business scenario where an expert mechanic visits a small, struggling auto shop. The mechanic makes a deal to revive the shop, but problems arise on the first day.
The shop owner has a large storage facility with massive amounts of inventory — half of which is valuable while the other half is junk. The mechanic helps customers by providing used and working parts, but can waste hours looking for the right one due to the disorder. Apart from locating parts, the time-consuming data entry to update the stock list slows the process even further.
Marketing director for Wasp Barcode and guest writer for Entrepreneur, Brian Sutter claims:
Research shows that even a proficient data entry operator will make one error for every 300 characters he or she enters and that level of inaccuracy can lead to big headaches if your stock includes hundreds or thousands of products.
What first appears as a simple inventory problem, turns into poor business operations as a result of low productivity.
A productive inventory solution looks beyond logging materials into a database for easy location.
It looks to automate operations with real-time notifications to the shop owner as inventory is running low on specific parts, listing current repairs, analysis of common car failures in the same region as the shop, and more.
The pure data-driven aspect of this concept can show the shop owner about a mismatch in parts, push an upsell, and apply notification alerts of potential future problems using past data regarding make, model and year.
A More Complex Example: Case Study of General Electric
As previously noted, data-driven solutions can also be complex. An example of a more complex use of data analytics comes through a case study of General Electric’s aircraft engines division.
Over 15,000,000 General Electric jet engines take off and land every year in the US. To remain cost-effective, General Electric committed $1 billion to install sensors on gas turbines, jet engines, and other machine parts that connect to the cloud and analyze the flow of information to pinpoint specific ways to improve machine productivity.
The company uses the data collected from the jet engines to produce information to determine whether it’s possible for a plane to further conserve fuel and protect airline parts during passenger flights. Additional information regarding service repair schedules for various parts of the aircraft is obtained from other sensors, allowing preventive and predictive maintenance.
Using this data-driven solution, General Electric’s customers saved millions of dollars in fuel and repair costs over time – all based on data and related analysis.
The company uses the data collected from the jet engines to produce information to determine whether it’s possible for a plane to further conserve fuel and protect airline parts during passenger flights.
Additional information regarding service repair schedules for various parts of the aircraft is obtained from other sensors, allowing preventive and predictive maintenance.
Using this data-driven solution, General Electric’s customers saved millions of dollars in fuel and repair costs over time – all based on data and related analysis.
Millions of business decisions like these are made every day around the world. Such decisions are often influenced by a person's industry experience, knowledge of the tasks at hand, and intuition. If the decision of when to replace parts are left to intuition or personal experience, business operations will not be as efficient and would remain subject to human error.
Myriad business decisions like these are becoming augmented and improved as data-driven technology continues to develop.
Data-Driven Decision Making for the Legal Industry
When it comes to law firms, a tremendous amount of data is collected. Such data is normally digitally stored. This is a great opportunity to access valuable insight in order to improve both internal workflows and management decision-making in various ways that can ultimately increase revenue and drive efficiencies.
For example, attorneys make important valuation decisions about cases as to how much the case is worth, how many hours a case will take to complete, and the extent to which there is a chance of winning. Such decisions are often made from years of experience and intuition.
Similarly, some valuation decisions are billing-related, and include whether the amounts logged for hourly work and expenses are excessive.
Law firms are required to exercise billing judgment to prevent “excessive, redundant or otherwise unnecessary” charges and expenses from being made to their clients.
Lawyers and other timekeepers in law firms frequently make these valuation decisions when entering time and reviewing pre-bills for approval.
Moreover, in-house counsel regularly make similar decisions when reviewing invoices from their outside counsel.
Data-driven legal billing application present an opportunity to automate these billing-related valuation decisions with greater speed, accuracy, and predictability.
By standardizing billing data through automation, law firms are more easily able to rely upon and use their own billing data to gain valuable insights into staffing and training needs.
In addition, with data-driven applications, billing data can also be used to improve realization and collection rates, and thereby significantly improve law firm profits, without charging clients more money.
For in-house counsel, using a data-centric approach to legal spend significantly decreases the time and resources required to review bills. However, data-driven applications also provide greater transparency, uniformity, and predictability for the company’s outside counsel partners.
In fact, more and more law firms and corporate clients are upgrading their operations to automate these billing-related valuation decisions.
In addition, several applications also use advanced data-related technologies to allow both in-house and outside counsel to more easily comply with billing rules.
Corporate clients are increasingly specifying the kinds of activities for which their outside counsel law firms can charge. These limitations are usually set forth in contractual documents are called “billing guidelines”.
Data-driven applications like Onit for in-house counsel, and BillerAssist and iTimekeep for outside counsel, allow billing rules to be set for time and expense entries to automate compliance.
If any billing entry fails to meet the activated rules, the item will be flagged for review or automatically adjusted.
Ensuring automatic alerts for violation of internal or client-mandated legal bill rules ensures compliant bills, fewer invoice rejections, and happy clients.
Additional important features of data-based applications should allow firms to easily integrate into their existing billing systems.
Compatibility with various self-hosted and locally installed systems should also be a significant factor when researching data-driven applications.
Conclusion
People who regularly use Google or Amazon are already interacting with data-driven technology.
Data-driven legal billing is the wave of the future for law firms and corporate legal departments alike, and provides a much more efficient and simple solution to increase law firm revenues and profits, at the same time as clients benefit from greater efficiencies and lower overall costs.