Data Driven Decisions through Data Modernization for Largest Health & Human Services Provider


Data modernization is the process of transforming data into usable, accessible, and scalable forms. Data can be used to produce insights that guide decisions to improve business performance and outcomes.

Data-driven decision making is a practice where decisions are made based on data or statistics rather than intuition or guesswork. A large number of companies now rely on big data as a key part of their decision-making process.


The Department of Health and Human Services (HHS) can benefit from both big data and analytics to make more informed decisions about how to serve its clients better through enhanced services, improved outcomes, and higher quality care.


The data modernization transformation will have a significant impact on the way we do business and how we serve our stakeholders.

The project’s objective is to provide the necessary information, tools, and resources to enable state-level health and human services agencies to modernize their data management practices.


Challenges

Data modernization is a process of transformation, to get the most out of the data. Data needs to be updated and was to get it ready for analytics. This process is helpful for healthcare providers, who want to make smarter decisions with the help of data-driven strategies.


The many challenges faced during data modernization can be categorized into:


- Lack of IT Expertise: It takes a specific skill set and background to do this work and it takes time and effort from the employees.

- Lack of Data Standards: Standards need to be set in place before any type of updates can be made or else there will be discrepancies among the different datasets.

- Technical Challenges: These may include problems with hardware or software that makes it hard for them to do their job properly.


Solutions

Long 80 being a healthcare-focused, infrastructure managed service provider & solutions company with extensive experience in technology transformation, systematically helped the customer consolidate data from on-premise, public & private cloud sources into a data warehouse. This data integration enabled the extraction of meaningful information through Business Intelligence (BI) tools.

 

  • Created a centralized data warehouse with data
  • BI tools to transform complex, unorganized, and confusing data 
  • Developed analytical and strategic Tableau dashboards 


Solution Outcomes

  • Meaningful information through data integration across applications on-premise and cloud 
  • Actionable insights through 360o view of clinical, non-clinical, financial, and operational data 
  • BI tools for strategic data-driven decision making by executives

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