Since 2016, health data has exploded, growing by nearly 900%. Today, the healthcare industry is the largest producer of data in the world, but most healthcare practices have little experience managing healthcare data lakes (large repositories of data).
Despite the rapidly expanding volume of healthcare data, many healthcare organizations only use structured data, which makes up a mere 10% – 20% of all available data. Data lakes help change that.
Data lakes centralize the remaining 80% – 90% of unused data, helping your organization pivot make more informed decisions. The more your practice can leverage all of the data at your fingertips, the more power you have to improve patient outcomes and satisfaction, and optimize operations and spending. Let’s discover how.
What Is a Healthcare Data Lake?
As new data continues to inundate healthcare organizations, the big question is: How can we safely store it and leverage it to optimize decision-making? A large part of the answer lies within data lakes.
Broadly speaking, a data lake is a type of storage repository. Data lake architectures are designed to store a wide variety of raw data assets, from structured and unstructured data to semistructured and binary data.
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Those four categories comprise nearly every file type imaginable, including:
- Relational data
- Audio files
- Free text
Data lakes are exceptionally fast and versatile because they use a flat architecture to store data in its native form—in object blobs or files—and it remains easily accessible and conveniently centralized for end-users.
Additionally, data lakes can be equipped with multiple security layers, allowing you to maintain data integrity and adhere to privacy and compliance regulations.
Advantages of Healthcare Data Lakes
In contrast to other operational data repositories, like enterprise data warehouses (EDW), data lakes are a more comprehensive option.
When data enters a data lake ecosystem, it retains its native format. In other words, it is neither converted to meet uniform data standards nor enriched in any way.
Additionally, data lakes have schema-on-read access, as opposed to schema-on-write access. This means that the schemas are written at the moment of analysis, rather than pre-existing. This removes the need to transform data when importing it into a data lake ecosystem, making it incredibly time-efficient and rapidly accessible. Data analysts can quickly discover trends (in topics as varied as disease and spending), reveal obscure correlations, and uncover subtle patterns—all in real-time.
Other features allow you to:
- Centralize clinical data from a variety of information types
- Accelerate time-to-value (TTV)
- Apply various tools to schema-on-read access
- Improve searchability of data content
- Provides low-cost storage for large data volumes
Data lakes are prized for being highly agile, efficient, and reconfigurable. For a hybrid approach, consider the lakehouse model. The lakehouse model combines the best of both data lakes and EDWs.
Data Lakes in Healthcare
Data lakes are an attractive option for any life sciences organization, from healthcare providers to insurance companies. With the help of machine learning algorithms, data lakes permit data scientists and healthcare professionals to analyze big data in a variety of useful ways. Here are some examples.
- Analyze patient outcomes
- Perform predictive and prescriptive analysis
- Free up previously siloed data to improve patient care
- Analyze budget and spending efficiency
- Detect fraud, waste, and abuse within insurance claims data
- Discover billing opportunities in free text
- Provide a 360-degree view of healthcare customers
Public Health and Research Facilities
- Conduct research and development (R&D) on medications, clinical trials, and durable medical equipment (DME)
- Test hypotheses and mine relevant data
- Leverage genomic information to study risk profiles
How to Set Up a Data Lake in Your Healthcare Organization
Since this is not a step-by-step guide for setting up a data lake, we won’t get bogged down in the computer science details. Instead, let’s talk about the approach to setting up a data lake in your healthcare organization.
Establishing a data lake can take one of two forms. First, you can follow the traditional route by setting it up within your organization’s own data centers. This is known as an “on-premises” data lake.
Alternatively, you can set it up using cloud services like Google Cloud, Microsoft Azure, or Amazon AWS. These are known as “cloud-based” data lakes. Each method has its advantages and disadvantages.
At True North, we can help you decide which method is right for your organization. Once your data lake is up and running, our managed services will keep it performing at its peak.
Who Can Benefit From a Healthcare Data Lake?
You may still be wondering: Is a data lake really essential for my healthcare organization? The answer largely depends on the degree to which you wish to manage fluid data.
If your organization must quickly analyze vast amounts of health data, in bulk or in real-time, from an array of different data sources and in different data formats—then, yes: A data lake is essential.
Data lakes add tremendous value to an organization by improving patient outcomes, bending the cost curve, and fostering innovation using evidence-based strategies.
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Trust True North With Your Healthcare Data Lakes
The enormous amounts of data generated by the healthcare industry demand a fast, scalable, cost-effective, and intelligent approach to data management and analysis.
Data lake solutions are the answer. As one of the most comprehensive repositories available, data lakes allow healthcare organizations to study complex data use cases like never before.
At True North, we understand the importance of utilizing big data to enhance decision making at all levels of your organization. Contact us today to discover how we can help you choose, implement, and manage your data lake.