PMsquare: Data Analytics Made Easy

Back in the late 18th century, coal fueled the rise of the world’s first industrial machinery and led to the first Industrial Revolution. Today, owing to the increase in tech-driven deployments, data has replaced coal as the prime driver for the current innovations in industrial operations. Unlike the early 2000s where business leaders and C-suite executives did not know how to leverage the data gathered by their technology, currently, businesses across all market sectors are extracting meaningful insights from their data. However, as most conventional data management processes involve slow and cumbersome paper-based calculations and/or Excel spreadsheets, a majority of organizations are seeking ways to enhancing the efficiencies of their data management and analytics. Helping such firms with their extensive experience and all-in-one portfolio of data and analytics services and solutions is Chicago-based PMsquare. “We focus on helping our clients make better decisions by enabling them to better understand, organize, manage, structure, and report off of their data with our one-stop-shop portfolio,” says Dustin Adkison, Managing Partner at PMsquare.

Traditionally, when an employee at large digital organization wanted to see data on a specific business unit, they would have to collaborate with the IT department and wait for them to grant access for the requested data. With PMsquare, users can gain a holistic and summarized view of their data and analytics posture via dashboards that allow C-suite executives to seamlessly manage data and deliver it to the business units that need it most. For an organization’s leadership, the company also enables ad hoc data reporting and wrangling to simplify the extraction of critical insights that can drive better decision-making. “We help C-level users design their financial planning, budgeting, and forecasting frameworks with data-driven insights,” adds Adkison. At the same time, PMsquare also performs data consolidation and reporting for clients with multiple data analytics teams, thereby eliminating the need to employ tools with overlapping features and maximizing cost savings.