Using Our Modern Data Analytics Platform and Azure for Future Growth at Maas Energy Works

Maas Energy Works (MEW) is primarily focused on biogas, a form of renewable natural gas created from animal extracts. While their organization has grown over the past 10 years, MEW needed a more reliable IT and data strategy to meet their long-term growth plans.

MEW has specialized in the development and operations of anaerobic manure digesters for years and is known for its 10-plus-year track record of innovation. While they are experts in their business operations, their on-premise tools and manual processes made it difficult for employees to collect information and create reports, resulting in an overly drawn-out and costly process.

With increasing demand for renewable energy from businesses, utilities and communities, MEW knew it needed a more reliable data strategy, and a standardized, modern architecture to support increasing data needs.

Enter Centric: Developing a Strategy to Use Generalized Data

MEW recognized the need to partner with a firm with Centric Consulting’s expertise in their industry, energy and utilities, as well as the area where they needed the most help, data and analytics.

MEW knew that it could no longer rely solely on Excel sheets to run production operations. While Excel could play a significant role in ad-hoc analysis, MEW needed approximately six weeks to integrate operational data into their analytics environment. With the right guidance on creating modern data architecture and enablement, a new architecture could make insights available in real-time to help with future operational goals.

We listened to the client’s challenges, plans, and ideas and helped them to understand that, by combining a modern data solution with the right set of tools, they could manage their growth plans more effectively and efficiently. Additionally, we created a standard Microsoft reference data architecture for MEW that can store and process data from multiple sources.

This diagram shows a similar architecture that follows Microsoft’s guidelines. Within the diagram, a combination of Azure services helps ingest, process and store data from a variety of sources, including structured, semi-structured, unstructured and streaming. It shows how data and insights are ingested, stored, processed, enriched and ultimately served through Azure services to the business users for analysis and use in business decisions.

Image source: https://learn.microsoft.com/en-us/azure/architecture/example-scenario/dataplate2e/data-platform-end-to-end?tabs=portal

Thanks to the newly created cloud-based architecture, MEW now has better data transparency. As a result, we created a roadmap of successful delivery checkpoints using tools such as Synapse, Power BI, Azure Data Lake Storage, and Synapse pipelines. Additionally, we studied MEW’s ecosystem to create a proof of concept (POC) to help with gathering data. Combining tools such as Microsoft Azure’s framework principles and our accelerator solutions and audit framework allowed our client to expand their existing files and set them up for the future.

“The architectural design adeptly caters to present needs while ensuring scalability for rapidly growing organizations,” stated Pranay Shyam, Principal Architect of Data and Analytics at Centric Consulting. “Our focus on efficiency and total cost of ownership underscores our commitment to sustainable solutions. I am grateful for our collaboration with Tom Huth and his team and take pride in the innovative solution we’ve jointly developed.”

By providing the right leadership, delivery team and solution strategy, we helped MEW envision and orchestrate a business plan with readily available insights that deliver efficiency, transparency and significant growth.

The Result: Confidence and Ease with a New Data Strategy

The automated data quality engine we built allows MEW to identify and remediate data quality issues. It also allows them to immediately see sensor data when new production digesters go online. The newly created data quality engine now helps identify and programmatically remediate data quality issues. We also helped MEW create a roadmap that can:

• Process information automatically through Azure Cloud to move MEW from manual to automated work.
• Update data and information daily that is accurate and readily available in near real-time.
• Design automated alerts that generate warnings to the users as issues arise.

This new ecosystem allows data to become more available and provide trusted insights, which enables MEW, too, to continue to grow and scale while meeting its current customers’ needs. 

Conclusion

MEW wanted to excel at its business and knew it needed help investing in an infrastructure that would grow with it into the future. With our help, MEW now has a cloud-based architecture that provides better data transparency, a new way to automatically process information, and alerts that warn users as issues arise. The new roadmap is what MEW needs to grow for years to come.