Building a Data River: Redefining Data Management in Law Firms
Historically, law firms have approached data management primarily from an operational standpoint. This operational management approach encompasses the day-to-day activities required to handle data as part of the firm’s transactional processes. In recent years, however, there has been a growing emphasis on achieving operational efficiency within these firms. This shift aims to streamline processes and enhance productivity, but it often overlooks the strategic implications of data management.
Challenging Traditional Data Management Norms
Traditionally, many law firms evaluate each business service sequentially, often utilising methodologies like Six Sigma to identify and optimise processes. Unfortunately, this approach results in documented procedures that begin and end at the boundaries of each functional area. Such compartmentalisation fails to account for the complex nature of data, which rarely aligns neatly within these functional silos. For instance, many of the firm’s systems and processes interface with common elements such as employee names, client details, and case information.
On the other hand, strategic data management necessitates a broader perspective—one that spans multiple functions and departments within the firm. Focusing on cross-functional data management processes can help firms better position themselves competitively in the legal market. However, this operational focus often leads to significant challenges, particularly with cross-functional communication.
The prevailing lack of interdepartmental transparency means that team members may not fully grasp the rationale behind operational data management decisions, even when those decisions directly impact their work. This communication breakdown weighs heavily on the firm’s culture, fostering an environment where trust erodes. Without transparency in processes and decision-making, employees may hesitate to share insights or feedback regarding data management practices. This reluctance can result in a pervasive atmosphere of distrust — both in the data itself and among colleagues. When leaders establish frameworks that inhibit communication, employees tend to replicate this behaviour, withholding their true feelings about data management.
Switching Focus to Shared Data Management
In response to these challenges, many firms are now making a conscious effort to reorient their approach to data management. One innovative concept that has emerged is the notion of a “data river.” This analogy illustrates the idea of creating a fluid data journey through the firm’s systems and processes, where the end goals of data usage significantly influence the data collection methods employed. This approach envisions a dynamic flow of information akin to a river nourishing a larger data lake utilised for reporting and analytics.
I often illustrate the effectiveness of this strategy by considering the client matter lifecycle. The goal is to dismantle traditional silos and enhance collaboration across various teams, processes, and systems. In short, flip from managing data points in vertical functional or process-based columns to managing data points in thematic (client, matter, employee, etc) horizontal rows.
This involves comprehensively analysing each team’s data utilisation and understanding how and when that data is captured. By doing so, firms can streamline and simplify data entry processes, ultimately improving the overall efficiency of data management.
However, transitioning to this more integrated approach can be highly disruptive. It often necessitates significant changes: revamping forms, modifying system integrations, redesigning taxonomies, and, in some cases, implementing automated processes. This shift signifies a move away from merely capturing data for individual departmental needs, instead transforming the data entry process into a cohesive and strategic activity that serves the entire organisation.
Gains from Navigating the Transition
While the journey toward a more integrated data management approach may involve challenges and adjustments, the potential gains far outweigh the difficulties encountered along the way. By fostering a culture of transparency, collaboration, and trust, firms can create a more dynamic data environment that enhances operational efficiency and nurtures a shared sense of purpose among employees. The transition to a data river model can lead to more informed decision-making, improved strategic positioning, and a stronger competitive edge in the legal landscape.
What does data governance have to do with the data river?
Data Governance is the strategic cross-functional coordination of data objectives, accountabilities, and responsibilities to support transparency and clarity of (predominately) the firm’s data in support of the firm’s strategy. It often includes concepts such as Data Ownership and Data Quality to help a firm gain better control over its data assets.
Data governance supports transparency, consistency, and accuracy of data to support operational processes and organisational knowledge. It is focused on data strategy, organisation and roles, policies, standards, valuation, and issues in the context of projects and services. In simple terms, it involves who uses what, how, when, and why, as well as ownership, accountability, security, and ways of resolving (or escalating) data issues.
Data governance implementation includes methods, technologies, and behaviours around the proper management of data. It identifies and treats with equal importance:
- People (who have ownership and accountability),
- Processes (how/when data moves through it’s lifecycle), and
- Technology (how/what system(s) is/are used, middleware, ETL).
Since data governance’s primary objective is to affect people’s data-related behaviour, it involves a change in culture. A data governance framework provides an operational context for this change by being the mechanism for the control (planning, monitoring, and enforcing) and leadership of data governance activities.
This means that data governance is perfectly positioned to support the identification, adoption, and management of a data river-type approach.
Final Thoughts
Data Governance enables and supports firms to redefine their data management strategy by focusing less on functional operational processes and more on cross-firm data quality.
Data Governance and other teams often use the river analogy to help explain the challenges of the status quo and the benefits of change.
