Adaptive Analytics: A Highly Flexible Approach to Data Management

More than ever, the ability to manage torrents of data has become a foundational component of any company’s competitive strategy. However, it’s rare to find an organization in which data is both tightly controlled and flexibly used. There are typically complicated processes for managing the data from multiple data sources, with variable structure, ingestion, and transformation processes. Ultimately, the data becomes stranded in silos. Organizations need flexible data management, architectures, and environments that allow them to digest and consume data from multiple sources, as well as view, report and analyze that data in any number of different ways.

Enter Adaptive Analytics. With over two decades of experience in helping clients gather, integrate, analyze and visualize their data, Adaptive focuses on making data readily available to clients in a number of formats. Data can be delivered to clients as dashboards, files for BI tools to consume, API feeds to CRM systems and more. As businesses continue to witness an explosion of data availability, Adaptive Analytics addresses the challenge of extracting valuable insights by leveraging a data lake approach to offer full data management solutions to its clientele.

“Most organizations struggle to identify not only the data they believe they will need, but how they need that data to be available to their organization. Traditionally, most organizations have had a relational database environment, requiring a robust ETL process to put that data into a relational environment. Add to this the influx of various BI tools, and you have organizational needs that can’t be met through these traditional approaches,” states Parnell Woodard, Founder at Adaptive Analytics. “Harnessing a more flexible data architecture and environments, we enable enterprises to consume data from multiple sources and to view, report and analyze the data in different ways. We introduce the concept of a data lake that allows us to bring in any/all relevant data, be it internal data or external data for a client. That data can then undergo an ELT process based on the client’s or business analyst’s needs to provide them the data or analysis they need in their desired format,” he adds.