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Call One deploys best of breed data warehouse to meet strategic business goals

Updated: Nov 17


Call One is a telecom industry leader that provides custom communications solutions to help companies nationwide serve customers, collaborate, and stay connected. Call One’s innovative technologies, 99.999% network availability and hands-on support has been the driving force behind their 99% customer retention.

Business Problem

To support Call One’s rapid national growth, the leadership team wanted to better understand the unique needs of each of their customer segments and determine what types of services best fit their customer base. Unfortunately, it was impossible to understand their customer segments as Call One’s data was dispersed among eight different cloud and on-premise applications and did not share standardized fields making it difficult to assess their customers’ needs.

Scalesology in Action

Scalesology went into action by designing and deploying a dynamically scalable and expandable data lake to ingest all Call One data. Scalesology then architected a modern data warehouse environment using Microsoft Azure and Snowflake to handle current and future data consumption over the next 3-5 years.

To guarantee the data was constantly up to date, Scalesology followed present-day DevOps methodologies and developed custom data import and replication tools in Python to refresh the data from Call One’s applications daily. Scalesology then created a Continuous Integration/Continuous Deployment (CI/CD) pipeline through GitHub Actions to ensure seamless test and push changes to an Azure Function App. To ensure that the import process continued to operate without issue, Scalesology also hooked in automatic emails and notifications that would trigger on import failures, as well as audit the total number of records imported for each data sources.

After building the data lake and data warehouse, Scalesology tackled the second issue of matching observations across the different applications using unstandardized fields. Scalesology wrote a combination of regular expressions and fuzzy string-matching algorithms in SQL and Python to identify and match records across the disparate data sources. Another benefit of this initiative was that it uncovered several data quality issues that Call One now could resolve before the analysis began.


Result

With the data consolidated, the fields within the disparate data sources matched, and a daily refresh of the data in the data lake and data warehouse, Scalesology and Call One could now build custom reports as well as begin customer segmentation analysis. As a result, Call One can now identify their least and most profitable customers along with developing new offerings relevant to what specific customer segments want.

Call One was thrilled with the project results.

Joe Johnson, IT Director at Call One, stated, “The (Call One) Board of Directors hailed the project as a tremendous success. Scalesology and our Data Science team completed the project a month earlier than anticipated, under budget, and delivered a strategic report outlining revenue conversion targets and customer segmentation scoring models to the Board of Directors”.

Conclusion

Today, Call One’s data is refreshed daily in its custom dashboards, tactical business reports and customer segmentation scoring models. Scalesology’s rigorous process of meticulously running through countless scenarios of potential issues while they were designing the data lake/warehouse and the CI/CD data pipeline resulted in a leading-edge data warehouse running in Snowflake, virtually maintenance free, with 99% uptime.

Whether you are considering building a data lake or data warehouse, or need to develop analytic models, Scalesology has the team, processes, and expertise to aggregate and parse your data to turn hindsight into foresight. Ready to find out how you too can gain insights from your data? Empower your business to grow just like Call One did. Contact us today and let’s figure out together where to get started on your analytics journey.

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