Designing a Data Repository for Healthcare Organizations

Designing a data repository can be a challenging task with various factors to consider, from the volume and velocity of data to its veracity.

A data repository is a critical piece of infrastructure for any healthcare organization. It empowers organizations to make better decisions by giving them access to a wealth of data that would otherwise be siloed in different departments or systems. But designing a data repository can be a challenging task with various factors to consider, from the volume and velocity of data to its veracity.

Benefits of a Centralized Data Repository

  • Improved data accuracy: When data is centralized in one location, it is easier to ensure that data is accurate as all updates can be made in one place and authorized users can access the most up-to-date version of the data.
  • Heightened security: Having a central location for storing data also increases security as authorized users can access the repository while unauthorized users are denied access. In addition, updates can be made to security settings in one place.
  • Enhanced efficiency: Centralizing data makes it easier for authorized users to find the information they need since data is stored in one location, rather than across multiple locations. Centralizing data also makes it easier to track changes and update information.
  • Advanced decision-making: Having a centralized repository of accurate and up-to-date information improves decision-making as decision-makers have quick and easy access to all relevant information. Furthermore, centralizing information makes it easier to spot patterns and trends.

Types of Data Repositories

  • Data warehouses: Where all an organization's historical data is copied and transformed into a new format that supports reporting and analytics.
  • Datamarts: Where a subset of an organization's historic structured data is copied for reporting and analytics purposes.
  • Data lakes: Where all an organization's structured and unstructured source data is copied as is for storage and processing.
  • Data lakehouses: A combination of a data lake and data warehouse which allows users to run analytics directly against the raw source data.

Steps to Building and Deploying a Data Repository

1. Assess Data Needs

The first step in designing a data repository is to assess your organization's data needs. This includes understanding the types of data that must be included, as well as the volume and velocity of that data. To get started, we recommend conducting a data audit or performing a data assessment to generate a clear picture of your current state and what areas need improvement.

2. Design the Data Repository

Once you have a clear understanding of your organization's data needs, it's time to design the actual repository. This will involve choosing the right type of repository and ensuring it is able to accommodate the volume, velocity, and variety of data that it will store.

3. Build the Data Repository

Once the design phase is complete, you’ll then need to build the repository. This involves working with IT staff and outside vendors to procure and install necessary hardware and software. You'll also need to develop processes for loading, cleansing, and managing data within the repository.

4. Maintain the Data Repository

Over time, it will be necessary to perform regular repository maintenance to ensure it continues to meet the needs of your organization. This includes tasks such as back-ups, security updates, and capacity planning. You’ll also need to monitor usage patterns and make changes, as needed (e.g., adding new users, granting/revoking access permissions, etc.).

5. Evaluate Performance

After the repository has been running for a while, it's essential to evaluate performance. This includes assessing whether it is meeting business goals and providing recommendations for needed improvements. Additionally, you should periodically review the design of the repository itself to ensure that it is still optimal over time, due to changes in technology or business needs.

Healthcare Organizations are Sitting on Mountains of Data

Only some organizations feel confident in their ability to effectively manage healthcare data. If your organization struggles with complex data management issues including gaining insights from disparate datasets, Guidehouse can help. We have successfully implemented a range of technology-enabled consulting initiatives designed to help federal and commercial providers and other organizations gain operational insights and act on their data.

Learn more about how we can help your organization overcome its most demanding challenges in the video below.


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