A Quick Guide to Choosing Between Data Lakes and Data Warehouses for Law Firms

Law firm leaders are increasingly turning to data-driven decision-making, inspired by the success of other forward-thinking firms. But when you have so much data available and need to analyze it effectively, a crucial question emerges: how do you best manage that data?

Often, firms find themselves choosing between the tried-and-tested traditional data warehouse or going for a more modern option, a data lake system.

In this comprehensive guide, we’ll break down what these options mean and even explore an option that you may not have been aware of a – hybrid model called the data lakehouse. This piece should give you all the information you need to build the best law firm data infrastructure. 

Let’s get started.

What is a data lake?

A data lake is an expansive storage repository for raw data in its unprocessed form. It’s not picky – it embraces various data formats, not just the usual neat and tidy ones. Each data element receives unique tagging and metadata, making it easy to locate when needed. Unlike other setups, data lakes don’t require a rigid plan when adding data. There’s no need to figure it all out in advance. The data scientists and analysts intervene post-ingestion, imposing structure and conducting targeted analytics for precise and accurate insights.

Let’s say your law firm has clients across the Middle East. While a warehouse might separate the data from your various websites, including your SA, OM, and AE domains, into distinct folders, a data lake does not. Instead, it relies on metadata and tags to distinguish the sources.

A data lake can hold everything from client interactions and case details to legal research and market trends. Each piece of information, tagged and organized, is ready for your data experts to sift through. They’ll work to uncover valuable insights that could shape your legal strategies and business decisions.

What is a data warehouse?

Data warehouses are places where all the data generated or collected by business applications come together and are stored for a specific analytics mission. These warehouses usually build their homes on relational databases, meaning they have a predefined plan (or schema) for the data. Before making itself at home, the data is cleansed, consolidated, and organized to be at its best for its intended uses.

For instance, let’s say you work in data-driven litigation and are knee-deep in a complex litigation case. You’ll likely need to analyze a vast amount of case-related information. A data warehouse could be your go-to tool, offering a structured and organized way to sift through evidence, track legal precedents, and uncover patterns.

Data Lake vs Data Warehouse

Now that you have an idea of these two storage configurations let’s delve into additional differences.

• Types of data: Think of data lakes as all-encompassing. They handle a variety of data, from legal research to client conversations. It’s stored as-is, with little to no change made before entering the lake. On the other hand, data warehouses are better suited for neat and structured information, as data is cleaned up and prepped before it even gets there.
• Analytics uses: Data lakes shine in data science applications, focusing on machine learning, predictive modeling, and advanced analytics, often with undefined analytics goals. Data warehouses, however, support less complex business intelligence (BI), ad hoc analysis, reporting, and data visualization.
• Users: Data lakes cater to data scientists and data analysts, assisted by data engineers who build pipelines and prep data. Conversely, data warehouses can serve business analysts, executives, and operational workers through self-service BI tools or queries performed by BI analysts and developers.
• Costs: Data lakes can have lower hardware costs, utilizing less expensive servers and storage. However, this advantage diminishes depending on the scope. With their large servers and disk storage systems, data warehouses generally incur higher deployment costs and may be more expensive to manage.

What is a data lakehouse?

Earlier, we mentioned an innovative new solution, meaning you might not have to choose! What is a data lakehouse, you ask? It takes the best parts of data lakes and warehouses, creating a flexible and open system that works well in today’s data scene.

In simple terms, a lakehouse ensures things run smoothly by supporting concurrent data tasks, maintaining data integrity, and giving direct access to source data through BI tools. 

A significant advantage is the decoupling of storage from computing, allowing for scalability, openness with standardized storage formats, and support for diverse data types and workloads. This can streamline processes and reduce operational costs. A noteworthy addition to this innovative landscape comes in the form of data clean room providers. These providers offer secure spaces within the lakehouse where businesses can collaboratively engage with clients and partners in a privacy-safe environment.

As we explore new frontiers in data management, lakehouses emerge as a promising alternative, bridging the gap between traditional data lakes and warehouses while embracing the power of cloud data management.

Which data repository should you choose for your law firm?

In law firms, the adoption of data warehouses and data lakes has been relatively slow compared to other industries. It’s mainly the big players with substantial data volume and specific business needs that have ventured into the world of data warehouses. Even among them, only a handful have committed to the significant investments required to establish and maintain these systems.

Nowadays, firms already equipped with data warehouses are eyeing expansion, considering the incorporation of data lakes. Meanwhile, those without are in the decision-making phase, contemplating which approach suits them best. The good news for the latter group is that these two alternatives aren’t mutually exclusive. The real question becomes, “Where do we begin?” For many, the answer may involve initially starting with a data lake and transitioning to a data warehouse over time.

Practically speaking, building a data lake is a more streamlined process, requiring less time and effort. Key decisions revolve around selecting data sources for a specific project and determining the amount of data from each source. Complex business decisions, like defining profitability calculations or prioritizing client details, can be postponed for future data warehouse projects.

According to Reuters, a skilled data integration expert familiar with legal and business systems can construct a data lake in less than a year. In contrast, data warehouses often evolve into multi-year endeavors involving numerous individuals. The challenges lie in deciding which data to include, establishing business rules for data cleansing, and applying those rules to data imports. Consequently, what may cost six figures for a data lake can easily escalate to multi-seven figures for a data warehouse.

But, as we’ve discussed, there is an alternative: data lakehouses. Unlike traditional data warehouses that excel with structured data, lakehouses cater to the diverse needs of modern enterprises dealing with unstructured and semi-structured data – in high volumes. While suitable for storage, data lakes often lack critical features like transaction support, data quality, and enforcement. The lakehouse, characterized by transaction support, schema enforcement, and governance, blends the strengths of data lakes and warehouses, leveraging low-cost cloud storage and making it a cost-effective solution with features such as open storage formats, BI support, and decoupled storage. If this sounds like it might be the right choice for you, check out Databrick’s guide to ‘What is a Delta Lake?’ today. 

Final Thoughts

By now, it should be clear that there’s no ‘right’ answer when choosing a data structure for law firms. Instead, your choice will rely on your business’s unique requirements, goals, and existing infrastructure. By carefully evaluating these factors and staying ahead of evolving technologies, legal professionals can navigate the complexities of data management to build a tailored solution that aligns with their specific needs.

It’s also important to remember that while the discourse often revolves around an either-or scenario, data warehouses and data lakes aren’t mutually exclusive. You can have both, and they can co-exist and even complement each other. Plus, as explained above, new hybrid models known as data lakehouses are becoming more prevalent and a viable option to consider for a blend of both. 

The journey toward effective data utilization is ongoing, and with informed decision-making, law firms can harness the power of data to drive success in the legal arena.

Have you liked this blog? We have several resources on data in law firms. Check out our data trends in the legal industry guide today to learn how to transition from data silos to data portability.

Guest Author Bio

Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing.

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