Post-M&A Data Management: How Pennant Services Built Trust Across Systems

The Pennant Group, a healthcare services company, operates across the United States in home health, hospice, and senior living. Serving as the organization's central support unit, Pennant Services provides IT, HR, accounting, and other umbrella services to support these operations.

With over 250 individual operations nationwide, Pennant delivers life-changing services to patients and residents, emphasizing local leadership and centralized support. The company's data-driven approach supports local leaders in making informed decisions about profitability, turnover, care, and patient outcomes.

500+

tables deprecated

minutes to refresh data

Industry:

Healthcare

Company size:

$500M - $1B revenue

Integrations:

Snowflake

dbt

Tableau

Challenge

Navigating a Complex Data Ecosystem

As Pennant Services modernized its data infrastructure including adoption of Snowflake, dbt, and Fivetran, the company encountered increasing complexity in its data management. With disparate operations spread across the country and a diverse range of healthcare services, Pennant's data team found itself managing a vast array of datasets and models.

The data services team, consisting of 12 members encompassing data engineering, data science, and integrations, was responsible for supporting local leaders with internal applications and predictive analytics to enhance client care. As the company grew both organically and through acquisitions, the complexity of their data ecosystem increased. The team faced several key issues:

Complex Data Architecture: Understanding and tracing data pipelines was painstakingly difficult, impacting the team’s ability to maintain data integrity and help end users comprehend data origins. The complex table dependencies made adding new fields or modifying existing reports a complex and time-consuming process.
Data Redundancy: Multiple instances of redundant datasets made it challenging to maintain consistency and efficiency. Identifying and eliminating these redundancies was essential for saving both time and money.
Limited Visibility: Business users and analysts often struggled with visibility into what data existed and how it should be used, slowing them down when trying to make data-driven decisions in their day-to-day work. Due to inconsistencies in the data, end-users were starting to lose trust and relied heavily on the data team when looking for answers.

These challenges highlighted the need for a comprehensive data discovery and governance solution. Pennant needed a tool that could provide visibility into their data lineage at a granular level and identify redundant datasets efficiently.

Before Select Star, the same metric — like revenue — could show up in multiple places with different calculations, leading to conflicting reports.

Kolby Nelson

Data Services Manager at Pennant Services

Solution

Automated Metadata Analysis for Enhanced Data Discovery

Pennant Group collaborated with Select Star to address these challenges, leveraging its robust data discovery and lineage features to enhance their data management practices. The implementation included several key components:

Column-Level Lineage: Select Star provided detailed insights into data transformations and usage across tables, enabling users to trace the origin and journey of each data element.
Data Deduplication: The intuitive search capabilities of Select Star allowed Pennant to swiftly identify and remove redundant datasets, ensuring a more streamlined and efficient data environment.
Enhanced Visibility: Business users and engineers gained the ability to easily see what analyses already existed and where that data was being used, increasing trust for stakeholders and improving decision-making around data changes.
Tagging and Sensitive Data Identification: Select Star's tagging feature facilitated better data governance, helping ensure compliance with regulatory requirements and internal policies.
Impact Analysis: Select Star enabled the team to perform comprehensive impact analyses when making changes to their data models or deprecating old tables, allowing the team to reduce data sprawl while preventing data outages.

Pennant found Select Star's user-friendly interface catered to both technical and non-technical users, helping bridge the gap between data teams and business users. The tool's automated documentation and reporting features further simplified data management tasks, allowing teams to focus on strategic initiatives rather than routine maintenance.

With Select Star, I can instantly trace a Tableau metric back to its source table — making updates faster and easier.

Kolby Nelson

Data Services Manager at Pennant Services

Result

Significant Improvements in Efficiency and Data Trust

The deployment of Select Star at Pennant Group led to notable improvements across various facets of their data management processes:

‍Improved Efficiency: The data services team reported a 50% increase in efficiency when identifying candidates for data deprecation. Tasks that previously required manual query log analysis could now be completed much faster using Select Star.‍
Faster Development Time: Adding new features or columns to reports became significantly quicker due to the clear lineage provided by Select Star.
‍Reduced Redundancy: Pennant successfully identified and removed numerous redundant tables, streamlining their data environment and reducing maintenance efforts.‍
Faster Data Refreshes: With a clear understanding of data lineage, Pennant was able to modify their pipelines to reduce their overnight batch processing time from 2 hours to just 25 minutes. This improvement not only saved time but also helped build trust with end users that data was accurate and up-to-dates.‍
Enhanced Data Governance: The ability to easily track data lineage and usage improved data governance and minimized errors in data handling. This improvement was crucial for maintaining the integrity and security of sensitive healthcare information.

With Select Star, Pennant noticed an important shift in their data management practices. Teams became more proactive in their approach, leveraging the insights provided by Select Star to drive innovation and efficiency. The improved data governance and visibility also contributed to higher confidence in data-driven decision-making across the organization.

Looking ahead, Pennant is expanding its use of predictive analytics, deploying models into internal applications to improve patient care. Select Star continues to play a crucial role in this process, providing visibility into the complex web of intermediate tables created during the machine learning process and helping optimize storage and compute costs.

Overall, Select Star transformed Pennant’s data management, making it more efficient, transparent, and manageable, ultimately supporting their mission to deliver high-quality healthcare services across their diverse operations.

Post-M&A Data Management: How Pennant Services Built Trust Across Systems

Challenge

Solution

Result

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