The phrase "agile data" is correct and usable in written English.
It can be used in contexts related to data management, analytics, or software development, particularly when discussing flexible and responsive data practices.
Example: "Our team is implementing agile data strategies to improve our response time to market changes."
Alternatives: "flexible data" or "responsive data".
Agile data warehousing techniques honed during the past 15 years have solved this problem.
Graph search and agile data modeling get analysts out of the manual data plumbing business – so they discover and iterate faster on more and more data.
In this work, a novel framework of agile data management is proposed to promote the transfer efficiency of product lifecycle data and facilitate business collaboration.
On the operations side, the company's capabilities are modular and agile; data is a strategic asset that is shared and accessible to all those in the company who need it.
The standard normal forms and conformed dimensions that undergird most data warehouses built until before agile data engineering became available unfortunately result in applications that are very expensive to modify once data is loaded into their integration layers.
At their root, agile data warehousing methods incorporate practices such as Scrum or Kanban to accelerate programming, but this strategy alone is not enough because poor software engineering practices in the phases leading up to or following application coding can fatally undermine an enterprise data warehousing (EDW) project.
The program imparts skills in seven categories: agile; data-driven; digital business models; digital communications; digital technologies; journey thinking; and risk and controls.
However, while the desire for an agile, data-driven culture was nearly unanimous, only 32% of those executives said they have actually achieved such an environment.
This agile, data-driven test-and-learn methodology enabled Zynga to roll out new features every 24 hours, a previously unheard-of release frequency.
A 2018 executive survey found that nearly 80% of top executives feared that their firms were at risk of disruption and displacement from highly agile, data-driven competitors.
Solving the huge demand for analytics drawn from vast amounts of data sources while delivering insights in time to be actionable, is a core challenge for enterprises that seek to compete with agile, data-driven competitors.
