Spectrum Science

Automated Data Integration Improves Decision-Making

How Orases helped Spectrum Science overhaul their data

Spectrum Science is an integrated marketing, communications, and media agency hyper-focused on science. We support biopharma, biotech, consumer science, health tech & clinical trial recruitment, with everything from advertising to marketing to communications.

Client

Spectrum Science

Project Name

Data Overhaul

Services

The Challenge

Hours lost exporting data that was inaccurate

Executive leadership at Spectrum Science relied on a series of operational reports to make critical decisions based on the organization’s health. The reports contained a consolidated view of accounting and project management data that was sourced from two independent applications that lacked referential integrity. A team of resources was required to manually transform the data into reports using Excel. The process lacked efficiency, consistency, and accuracy.

These issues caused the leadership team to call into question the integrity of the operational reports. As a result, Spectrum Science sought the expertise and resources that Orases could bring to the table as a custom software company with data experts.

The Main Challenges

The weekly process was inefficient, necessitating the full-time effort of one employee.

The discrepancies and inconsistencies between the two datasets prevented the accurate balancing of the reports.

The report inaccuracies stemmed from stale data due to the project management tool not being synchronized with the accounting system.

Solution

Harmonizing disparate data sources

Orases worked with Spectrum Science to harmonize and integrate their operational data, creating a unified and comprehensive view for reporting. The project was developed by creating a middleware solution that integrates seamlessly with the SaaS product Domo. This solution extracts data from both the accountability and workflow systems and transforms it by eliminating duplicate entries, aligning data points, and running processes to correct inconsistencies, ultimately consolidating the information to streamline data processing and reporting.

The solution provides a consolidated data view for reporting in Domo, which was selected for its graphic-rich reporting capabilities and suitability for non-technical users. The dashboard includes key reports on budgeting, capacity, scheduling, and burn rates, allowing the C-Suite audience to make informed hiring and budgeting decisions.

Solution Features:

Data profiling to examine and analyze the data to understand its structure, quality, and content.
Data cleansing and standardization to detect and correct errors or inconsistencies in the data to ensure it conformed to a consistent standard.
Data mapping to match and link data elements from each source based on their semantics or relationships while reconciling any differences.
Selection of a Business Intelligence (BI) tool to meet Spectrum’s data-driven decision-making capabilities.

Results

Orases was able to save Spectrum Science over 20 hours a week generating reports.

The solution delivered an accurate and consolidated view of operational data, radically reducing report generation time and standardizing data through automated ETL processes. By enforcing referential integrity, the data became cohesive and reliable, leading to full adoption by the executive leadership team and empowering them to make informed decisions that improve business operations.

Due to this project with Orases, Spectrum Science transformed their data management process from a manually intensive, inconsistent system to an automated, standardized solution. The new system eliminated the need for a full-time junior employee to extract, clean, and compile data from disparate sources, instead implementing automated ETL processes that ensure reliable, consistent, and balanced reports. By enforcing referential integrity, the solution produced cohesive and accurate data, leading to full adoption of the operational reports by the executive leadership team.