Data teams and their business-side colleagues now expect—and need—more from their observability solutions than ever before. Modern data stacks create new challenges for performance, reliability, data quality, and, increasingly, cost. And the challenges faced by operations engineers are going to be different from those for data analysts, which are different from those people on the business side care about. That’s where DataOps observability comes in.

A recent study on call center statistics found that 91% of consumers reported poor customer service in 2021. Providing high-quality service is essential, especially today, to retain customers and drive more business. Quality service is only one important metric in running a profitable call center. No matter your goal, the first step is understanding what's going on in your call center.

From cost-effectiveness to what adds business value — what Autodesk considers critical when deciding whether to buy versus build data pipelines.

Business intelligence (BI) involves converting data into valuable insights using software and services. It influences a company’s strategic and tactical business preferences. BI tools access and analyze data sets and show analytical results in the form of reports, graphs & charts, dashboards, summaries, and maps. This process provides in-depth insight into the situation of the business.

Business analytics is the practice of using data and statistical analysis to help businesses make better decisions. This can involve analyzing data to identify trends, patterns, and relationships, and using that information to help businesses make better decisions about their operations, marketing, and strategy.

The move to the cloud continues at a fast pace and if your organization embraces the future of operational reporting, then you need a plan to ensure consistent enterprise-wide reporting during your cloud journey. A top challenge of cloud migration is the need to produce consolidated reporting and analytics that cover all your Oracle ERP instances.

There is a long history of clustering architectures with respect to building distributed databases for two primary reasons. The first is scalability. If a cluster of nodes has reached its capacity to perform work, adding additional nodes are introduced to handle the increased load. The second is availability. The ability to ensure that if a node fails, let’s say during ingestion and/or querying, remaining nodes would continue to execute due to state replication.