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  • Writer's pictureBrad Hoyt

Liberating Data from Legacy Systems: A Case Study

As we look forward to the upcoming year, it’s important to take a moment to reflect on the last twelve months. The unprecedented level of change that the Covid-19 pandemic demanded has forced companies to adapt faster than ever before. One of the most significant changes we’ve seen across industries is a shift towards holistic digital transformations. With customers and workforces navigating remote work, data agility and privacy have become paramount to company success. Companies have needed to transform legacy systems to safely and effectively make use of their data in our new world.

Over the past year, the team at Stratizant has worked with several companies to transform their digital systems and improve data streaming processes. Through data liberation, our work has optimized workflows, improved employee and customer experiences, and created transparency for companies who rely on data-driven insights.

One project stands out as exemplary of the possibilities that are gained through effective data liberation: transforming the data management of a large public utilities company.

We partnered with another company to help implement our Quest Streaming Intelligence Platform and Kafka streaming so our client could source data from various legacy systems and make it available for other systems in order to create holistic data management and applications aligned with business outcomes.

Prior to working with Stratizant, our client relied exclusively on several legacy systems for their daily operations, from an SAP resource planning system to their core billing system, causing data to be locked up and disconnected. In the event of emergencies like gas leaks, fieldworkers had to use outdated, large, and inefficient laptops to access critical information, taking up valuable time and disrupting the workflow of emergency response.

Reliance on legacy systems is not uncommon in industries like public utilities which are highly regulated to protect consumer and city data. This project, therefore, needed to improve the flow of data both internally and to fieldworkers while upholding stringent security standards. Stratizant was able to accomplish this in only 3 months using the Quest Streaming Intelligence Platform and Kafka. Though Kafka was already part of the client’s architecture, they hadn’t implemented it because no one on the team had sufficient knowledge of the tool.

Stratizant was able to configure and implement the transformations needed to get data from one system to others in near real-time. Once data was liberated from legacy systems, it became available to fieldworkers, allowing them to perform their duties more productively and improve customer service. The team at Stratizant accomplished this by building the data streaming pieces on top of cloud infrastructure their partner company had implemented. Use cases were then used to prove that the Kafka toolset could take data from various systems like the SAP resource planning system and legacy billing system and make it securely available for other systems like the customer experience program in real-time.

Stratizant also built an employee experience mobile platform which used the Kafka tool set to liberate data from their legacy systems and bring important information to workers in the field.

The open source nature of the Kafka toolset allows the utility company to stream data from various sources securely and in near real-time, so only those workers with the right to that data are able to access it, allowing them to utilize all the information needed to effectively perform without compromising privacy.

The new system is now fully in production and providing critical information to work orders and public utility emergency responders.

The public utilities project is exemplary for how open source Kafka toolsets can be configured to liberate data from SAP and other legacy systems that lock data up, forcing clients to utilize SAP tools for data management. The project proved that the bind of legacy systems can be overcome, facilitating data agility across enterprises in order to optimize data-driven decision making and workflows.


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