How to rethink your analytics stack – from financial, operations & revenue reporting to ML
PlayPower, Inc. is a world leading manufacturer of outdoor recreational products, specializing in commercial playgrounds, floating dock systems, boat lifts, and surfacing solutions. It is a company going through incredible growth with an aggressive merger and acquisition strategy, and expansion into new markets such as physical entertainment spaces with interactive experiences featuring real-time digitally rendered content.
At such a dynamic company, change is a constant, with new ERP/CRM applications and other data sources being introduced into their data and information ecosystem on an almost constant basis.
PlayPower’s IT leaders needed to bring all of the data together, over a dozen different systems for financial analysis alone, into a unified, single pane of glass. With new brands coming online and no existing BI infrastructure, the team had to work fast to be able to manage orders and manufacturing backlog across the sprawling organization. And then, COVID hit, bringing with it fresh challenges and uncertainties. The company now needed to carefully manage spend and their manufacturing pipeline, and this required a platform to run predictive analytics and correlation analysis. They didn’t have 2 years to build these systems – management needed answers in weeks.
Hear from Alfred Sobrinho, the Vice President of IT at PlayPower. He was tasked with handling the integration and analytic challenges of this rapidly changing environment. When evaluating technology options, he felt there had to be a better way – a way to short circuit the time and expense of traditional data engineering and data warehousing approaches – and keep up with the ever-expanding needs of the business.
In this session you will learn:
How PlayPower was able to harmonize and unify dozens of systems without ETL and without a traditional data warehouse.
How they slashed months off the implementation time, were able to do much more with less, and are able to remain agile as the company grows.
How advanced analytics and machine learning were able to run on the same platform, with the same data, without adding complexity to the data pipeline.
How they do month-end financial consolidation on OneStream while posting corrections to the general ledger, running 15-minute micro intervals for near instant updates.
