Improving Customer Service on a Global Scale

Our customer supports an organization of employees operating out in the field at their customer locations which are distributed across the entire globe. The field employees are measured on site visits and new business generation; so, productivity is directly tied to information availability.

As in many organizations, the decision data was dispersed through a broad range of systems globally; including SAP, DB2 and SQL Server.

Creating a solution that would securely amalgamate the global data and turn it into actionable information would improve productivity, lower costs and create more business opportunities.

Solution: Merge Data from Multiple Sources into One User-Friendly System

Tail Wind worked with the key stake holders within the business to understand, assess and document the projects Key Performance Indicators (KPIs); and then analyzed the current state of all source systems.  

With this knowledge; Tail Wind proposed a Proof of Concept (POC) for building out the solutions which included migrating the On-Premise data into an Azure Cloud.

We architected and developed a POC solution meeting the KPIs and then delivered a user-friendly solution for navigating the Dashboard and suite of reports. The solution contained information from multiple systems and supported a broad base of constituents globally.

Tools:

• The main components of the project included developing processes around data ingestion into the Azure Data Lake, transformation and cleansing of data through Azure Data Factory and Azure Data Lake Analytics U-SQL. Towards the final stages of the project, Azure Databricks was introduced for expedited testing and validation as well as a self-service method used by Business Power Users.
• The entire process from data ingest to report delivery was automated through Data Factory; it leverages runbooks to refresh the loaded Microsoft Power BI Model and refreshes the data daily.
• The Data Lake was constructed in a way that data is landed in the same format it was pulled from the source system, versioning was done on specific tables deemed necessary by the customer during this step. A transformation layer allowed merging multiple sources or types of information into a more structured format for consumption in the Azure Analysis Service Layer.

Results:

Our customer has a global dashboard; that turns data into actionable information available 24 x 7 to users at all levels according to their row level security.

The solution was built in a way that data aggregates as it moves up the organizational hierarchy to ensure consistency of numbers being reported to all levels.

The dashboard offers insights that help our customer align its field and management teams to better serve their customers; and it provides daily insight for field teams to direct their efforts where they’ll be the most effective.

How It Works:

Moving data from multiple source systems into Azure allowed the organization a consolidated technology to control access to all data, reducing the need for users to be granted direct access to transactional databases to run queries. Having the ingested tables mimic their sources provide an easy way to compare results between legacy and newly developed reports.

Having all relevant tables sourced into a single Data Lake has reduced the cost for creating new reports through a reduction in ETL when delivering additional report requests.