Contract Manufacturer Moves To The Data Cloud


Pandata Group directed the implementation of a unified and integrated cloud delivered data platform for a major Wisconsin-based manufacturing company. The solution brought together formerly disparate data stores and enabled a common analytic platform for all enterprise data sources.


The client is a publicly traded metal fabrication and manufacturing operation serving the heavy and medium duty commercial, construction and recreational vehicle industry. The company is publicly traded and has been rapidly expanding through M&A activity.


Because of its recent rapid growth and diversified acquisition, the client was reluctant to invest in a single ERP system to manage its multiple subsidiaries. Purchasing information was siloed in disparate systems that required an extensive, inefficient manual process and led to management questioning the data presented to them.


Pandata Group implemented a modern data cloud platform of data storage and integration. This platform consists of a suite of cloud-hosted products: Matillion for data integration and transformation, and Snowflake for data warehousing and querying. The business was then able to use a variety of self-service tools for unified data analysis. The implementation processes consisted of four phases: planning, building, managing and scaling. After doing a full data landscape assessment, an initial use case was identified. An integrated foundation of data modeling, sourcing, loading and storing was then built out and tested against the use case. Finally, staff training and change enablement were introduced to maximize the “return on data” and help foster a data-driven culture.


The value created from the client’s data cloud implementation was fourfold:

  • Increased operational efficiency by enabling a 360-degree view of all interrelated purchasing and inventory data.
  • Cost savings by mitigating a centralized ERP investment and capturing unified enterprise data “downstream”.
  • Improved data quality by eliminating redundancy and enforcing a “single version” of data definitions and usage.
  • Emphasis on data literacy among stakeholders and across business units

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