8 trends that will impact data management, the cloud and AI strategies
Organizations can expect to see a number of dramatic trends regarding data management, analytics and artificial intelligence this year. Informatoin Management recently spoke with Couchbase Chief Technology Officer Ravi Mayuram for his predictions on which trends will have the greatest impact. Here are his top eight:
The database sprawl will continue as different types of databases proliferate
App developers are creating a lot of data in a lot of different ways, but it’s all bumping into each other without a servicized solution that offers flexibility to house and manage this data. As it stands, developers are using multiple databases for each individual application, creating a database sprawl as users cobble together multiple databases to plug different holes in the system. While the short-term gain of being able to use emerging technologies and have many choices seems great upfront, companies need to consider their long-term goals, rather than select a cobbled together, quick solution.
Multi-cloud implementations suffer new issues from lack of interoperability across clouds
As providers continue to innovate before standardizing processes and interoperability, problems will arise in multi-cloud environments because providers have created interfaces with slightly different ways of working. For example, Google and Amazon each have their own messaging systems, as does Kafka, and applications developed do not simply move to another without undergoing changes. In 2019, these issues will come to light – and users will experience many headaches before true interoperability is achieved across multi-cloud deployments.
The groundwork has been laid for AI/ML technologies, and now the real questions will surface
Over the past year, companies have been figuring out where and how to implement AI/ML technologies, and many are still refining the “how.” While that’s true, the groundwork has been laid and the mentalities have been shifted, and 2019 will be a big year for questions in AI/ML – literally, in the sense of how organizations determine what questions to use to train their AI/ML algorithms. There are also broader conversations that have been sparked around ethics and biases, and 2019 will see the conversation continue, with academia and business working together to develop a trusted approach to developing AI/ML for the future.
Today, data remains a difficult part of AI and is a barrier to effective training methods and truly trusted outcomes.
