According to thought leaders in the data management sector, we'll be using some new terminology when talking about enterprise data in 2022.
According to thought leaders in the data management sector, we'll be using some new terminology when talking about enterprise data in 2022.
CTOs and IT managers at all levels will be defining and testing terms like "data as code" and "just-in-time" data analytics for their own production use cases.
AI will be working overtime in the data management space, enabling call centers to mine more cogent information from customers, patching gaps in supply chains, and bolstering healthcare services, both locally and in the cloud.
Here are some cogent predictions about what we can expect to see on the data management side of IT in 2022:
We will begin to hear 'data as code' frequently
The Infrastructure as Code movement, in which infrastructure is automatically deployed, is gaining traction. Infrastructure and applications, however, deliver little value without data. Organizations will need to be able to dynamically clone, distribute, and destroy data copies on demand, so they can develop, test, analyze, build AI/ML models, and meet regulatory requirements. As machines generate more data, IT organizations will not be able to manage data by hand. They will need to make data as dynamic and automatic as infrastructure and applications.
AI will be reading between the lines with customers
A major trend in customer service data management for 2022 will be the use of AI to unlock data that is kept in all of the conversations that customers have with contact-center agents. The technology used to review all of these conversations has been in place for a while.
AI services will play a major role in generating revenue
Adoption rates and revenue generated from artificial intelligence services are projected to skyrocket as ongoing issues, including the healthcare crisis, labor shortages, and supply chain problems, continue to present considerable risks to businesses. For one example, AI-based chatbots and virtual agents are reducing the pressures on businesses from labor shortages. In health care, AI-based solutions allow care teams to manage a wider patient population and do so with a personalized approach at a patient level. Health and human services agencies are keen on implementing whole-person health initiatives, which require access to high-quality and accurate clinical, social determinants, and public health data for developing customized care programs at an individual level.