The pace of change in healthcare and technology can be dizzying, and for many healthcare organizations, keeping up means choosing the right data platform. There are so many options on the market that it can be overwhelming, but what’s important is that the one you choose enable your particular business goals and desired outcomes.
If change is the only constant, then it’s critical that healthcare organizations equip themselves with adaptable, flexible tools, future-proofing their data architecture so they can embrace whatever the next wave of innovation brings. Whether it’s machine learning or large language models, AI makes a compelling case for data lakehouses — a combination of a data warehouse and data lake that enables both structure and dexterity. Here, we’ll explore why these matter in healthcare, and how these different data storage models function.
Before we discuss the data lakehouse, it’s important to break down its predecessors, the structures that inspired it — data lakes vs. data warehouses.
Think of a data lake as a vast, deep pool of data, an “ecosystem” of structured, unstructured, and semi-structured information all swimming around together. It gets these diverse types of data because it can ingest data from many different kinds of systems, whether that’s on-site or off, and it can store a massive amount of this information securely.
Data lakes are flexible, scalable, and primed for raw data storage. In this sense, their lack of structure can be a major asset that allows for speed, volume, and depth.
Data warehouses differ from data lakes in important ways, but the two are often complementary. Where a data lake stores a mass of diverse data points of varying structures, a data warehouse is designed with analytics in mind. Think of the rows upon rows of boxes being fetched by a big retailer’s robots, then imagine those aisles stretching past your sight line. These data storage systems are structured, meaning the data is more uniform and coherently organized than within a data lake. That makes it ideal for reports, analysis, and business insights.
Its main strengths are its sturdy foundation, speed, and analytical capabilities. With the right underlying structure, a data warehouse can generate presentable analyses for business stakeholders, improve clinical decision-making, elevate strategic planning, and enhance outcomes.
An understanding of data lakes and data warehouses is crucial to understand the latest, greatest technology in data storage and analytics: the data lakehouse. As AI and machine learning emerge, with more and more novel uses on the horizon, it’s a structure that supports innovation, combining the coherent organization of a warehouse with the vastness and flexibility of a lake. With an additional layer of metadata and governance, it filters bad or unusable data but makes unstructured data available for analysis.
For a lakehouse to deliver useful insights and better efficiency, it needs to perform the following functions:
By combining the best of two existing data storage structures, the lakehouse out-performs the data warehouse and data lake, simpler models that preceded and inspired it. By offering the best of both, the data lakehouse marks a meaningful evolution in healthcare and beyond.
Sure, the technology that powers a data lakehouse is impressive, but in the end, what matters most is how it enables better healthcare performance. There are three key areas where data lakehouses drive enhanced care and outcomes, helping healthcare groups achieve efficiency in the process, especially compared to a data warehouse or data lake alone.
Data lakehouses are already transforming healthcare, enabling next-generation features in generative AI and ML. If the future of healthcare is data, and the future of data is the lakehouse, it’s time to dive in. Learn more about how you can bring together the best of data warehouse and data lake architectures on a data platform powered by a lakehouse.