Organizations are facing a number of challenges in managing legacy data warehouses, even as the market undergoes significant changes such as the advent of real-time analytics; the emergence of autonomous platforms that automate backup, recovery, administration and security; and an ongoing move to cloud-based services.
One of the biggest challenges is the massive increase in data volume.
“Traditional structured data continues to grow rapidly, slowing down legacy data warehouse systems and affecting analytics and timely insights,” said Noel Yuhanna, principal analyst at Forrester Research. “Regulatory requirements now mandate storing compliant data for several years, and business growth is generating more data at a faster pace than ever before.”
At the same time, organizations can't share data quickly enough.
“With increasing big data comes a major challenge for any enterprise: knowing what to look for and where, and then making sense of it,” Yuhanna said. “Firms are realizing that traditional data warehouses fall short when it comes to real-time analytics.”
In addition, older data warehouses were built for a limited set of uses, providing answers to known questions, Yuhanna said.
"Processes using traditional EDWs [enterprise data warehouses] don't scale well when you introduce ambiguity or add new and dynamic questions,” he said. “EDWs need to ingest, process, and curate data continuously and support dynamic insights.”
And the variety of data is making it harder to support new requirements.
"Business users can't easily spot patterns and trends in content such as documents, email, images, audio, and social media,” Yuhanna said. “In addition, storing, processing, and accessing unstructured data in data warehouses pushes the limits of traditional technologies and architectures, which were not designed to handle such data types.”
