8 trends affecting 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.
Data gets a makeover to support AI/ML algorithms
Today, data remains a difficult part of AI and is a barrier to effective training methods and truly trusted outcomes. Data quality and availability can vary wildly within an organization, and it can take time to determine what data is clean, up-to-date and trustworthy. 2019 will see data systems come under greater scrutiny within the enterprise as data grows in value, and we’ll see efforts to address data quality across the board to better leverage AI/ML technologies.
IoT data and edge analytics will prove more valuable than predictive analytics in 2019
Many organizations are currently focused on predictive analytics -- and for good reason, as it promises to solve issues before they actually become issues. But in the next year, analytics at the edge will deliver more business value than predictive analytics in the cloud. Edge analytics makes sense of the terabytes of always flowing IoT data to empower field workers, line of business decisions, and overall helps organizations to better serve customers in different ways. While it will ultimately complement maturing predictive analytics, IoT data and analytics at the edge will see a higher ROI in the coming year for its many applications.
Open Source fragmentation will lead to developer headaches
We’re beginning to see an inflection point with Open Source. For example, Oracle is taking a more commercial approach to Java, which will cause fragmentation for Java developers. Additionally, some database companies have changed their licensing terms and Open Source models, which will cause issues for cloud players who were previously benefiting from the software. In 2019, developers will be challenged to determine which Open Source platform to move to that matches all their needs and works cohesively with their current technology – without threat of changing.
Kubernetes keeps climbing as the contender to power Cloud 2.0
In 2018, Kubernetes emerged as the de-facto cloud orchestration layer after having organized the container chaos across the industry. But we’re still very much in the early days of Kubernetes—and as the software ecosystem around containers grows (i.e. performance, tracing, cloud monitoring) in 2019, Kubernetes will become more than just the orchestration layer. It will become the operating system as we move to Cloud 2.0, the next phase of cloud technology that is intelligent and business-driven—and that uses true multi-cloud strategies.
Serverless becomes less mysterious
Today, every CIO and CTO are evaluating serverless technologies, but a big constraint preventing adoption is the potential for vendor lock-in and unknown variables. In 2019, the mystery around serverless will slowly lift—and in the process, bring it to broader adoption. Today, serverless can lead to lock-in with certain cloud implementations, but we’re likely to see an emerging ecosystem of supporting technologies develop as microservices lay the foundation for a new type of cloud operating system.