DATA WAREHOUSING CASE STUDY
hBITS has built and installed custom
Data Warehouse solutions to provide
customers a unified data experience
and exponential business gains.
AT A GLANCE
OVERVIEW
The client organization in this case leads the world in
the effective treatment of autism, guided by science
and inspired by compassion, to help people with
autism and their families thrive.
The enterprise is 5K + employees strong and
recognizes that we all face unique challenges, while
committing to put people first and caring about every
patient, caregiver, and teammate.
BUSINESS CHALLENGES
The business was dependent on distributed systems like
Challenges
Dependency on distributed
systems
Administrative Waste
Lack of universal reporting
system
Benefits
Reduced operational costs within 6
months of DW implementation
Faster identification of root cause along
with in-depth analysis .
Increased business transaction visibility.
Remarkable savings in system
integration testing.
HRMS, Practice Management, Salesforce, clinical tools,
finance & account software etc., acquired during years of
organic growth.
The validation and entry of each data set was laborintensive, time-consuming, and error-prone.
There was a lack of universal reporting system that was a
result of lack of budget in the initial period.
Inadequate use of data for analysis in business making.
No visual representation of data was used to save time and
facilitate analysis.
Weak data extraction practices and data storage system for
key business decision making
Slow and inefficient report generation impacting monthly
yearly auditing system process.
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Data Warehouse
infrastructure and solutions
were implemented for the
leading autism treatment
provider in the United States.
The customer benefitted by
achieving access to all
strategic data for key
decision making
BUSINESS SOLUTION
The initial data was transferred into a staging area
After reviewing the existing state hBITS implemented a
custom Data Warehouse (DW) solution to solve all the
data problems. One DW owner from the IT department
and one from the Business side were identified to drive
and manage the DW initiative.
The DW Solution would act as a source of data for any
reporting, data analytics needs. Based on the specific
business needs it was proposed to build a DW first before
the budgeted Business Intelligence (B)I tool was ready.
Newly requested reports were prepared with quick
turnaround that ran on that DW during this transition
phase.
The DW process was categorized into key three areas
of:
Data Extraction
Data Storage
Data Representation
A strong foundation was set through an accurate data
modeling process. This ensured the system was ready to
support all the current and future reports, while easily
identify and retrieve data analytic points to help the
business grow.
Furthermore, a Data governance team was also setup first
that governed the DW execution process, as well as
audited and maintained the quality of the entire DW
architecture. Finally, the Key solution components were
added in each area for a fully functional solution.
KEY SOLUTION COMPONENTS
Different solution components that were selected and
added to the structure to create the required
infrastructure included:
from ETL and then the data was transferred to
actual live tables.
DW config within the Data Warehouse was
maintained to track the overall design of the
system.
Multiple Data Marts were created to expose the
data to different categories of user groups. This
ensured only the required data set were exposed
according to the user groups. This was an added
mechanism to the data protection policy.
BI
BI tool was added in the later phase. The BI tool
architecture was designed to support reusability
and support the ease of new dashboard creation.
Existing Data Marts from DW were integrated with
the BI tool to create a new Dashboard within few
hours.
Reporting Service
With the limited licensing of the BI tool, a
reporting service was implemented which
operated on top of the data warehouse. This
reporting service generated more than 200+
reports in a cost effective manner and could
distribute the reports to more than 3k+ users.
Search Service
A search service was developed to take
advantage of DW. This service could be easily
integrated into any application. This enhanced
the data search capacity of existing applications
and improved the performance of search by 75%.
ETL
An ETL tool was selected to manage the transfer of
data across all the layers/systems.
The ETL tool has capability to transfer huge data set
and also transform the raw data into valid data which
the business needs.
Various ETL pipelines were created depending upon
the type and sources of data.
Data Warehouse
Data Warehouse was designed using a Star Schema
data model design. This design was customized to
suit the type of the data system that was being
aggregated. This also enhanced the performance of
the system.
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BENEFITS
On-premise data completely moved to cloud DW.
On-premise apps reporting dependency is completely removed, and system scale-up is not required to
achieve effective reporting.
Increased reporting service up-time with a unified reporting system.
All third-party systems completely integrated with the existing DW.
Inclusion of advanced BI tools to take full advantage of DW data.
Real-time data sharing across users to help business define market strategies.
Internal and external data marts created for data scientists to help them perform data analysis.
About
We are a dynamic group of seasoned entrepreneurs, tech nerds, Indian natives, and international transplants. We have come
together to bring to reality of our shared vision for becoming a leading IT service and solutions company. HumaneBITS (aka
hBITS) was founded in 2015 by leaders who had already developed smart processes for the health-care industry. They took
what they did in health-care–simplifying overly complex communications between organizations–and applied it to solve
cross-industry problems and build something meaningful today.
www.humanebits.com
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