Four Trends Impacting Business Priorities in 2023

Extra tags: Artificial Intelligencehybrid cloudZero-TrustCloud Migrationmachine learning

by Daniel Hand, Field CTO, APJ, Cloudera

In today’s business environment, cloud computing is regarded as one of the most strategically relevant and important technologies in business operations. However, we are seeing a shift in how organizations are approaching and implementing their cloud strategies. Progressive organizations are moving away from the “public cloud-first” digital transformation strategies to a more measured and balanced approach to the cloud, distributing workloads where they make the most sense.

As more industries mature digitally, the mobilization of emerging technologies like the hybrid cloud, artificial intelligence (AI) and machine learning for improved operational efficiencies and cost reduction will increase. As such, we believe that 2023 will be a pivotal year for organizations looking to harness the vast potential of the hybrid cloud. Here are four key tech trends that will greatly impact business priorities in the coming year.

Trend 1: The increasing move to hybrid architectures for data and applications
We are seeing organizations’ cloud strategies starting to evolve from on-premises clouds with a single public cloud provider to on-premises with multiple public cloud providers. Not only that – the way organizations are looking at cloud service providers is changing too. Today’s organizations are looking for neutrality in cloud providers and avoiding vendor lock-in as they adjust their cloud strategy to align with business needs.

This gives rise to an increasing need for data fabric architectures, where organizations can have the ability to orchestrate and manage disparate workloads across multiple places and have shared security governance across them. This will be important as organizations are becoming more open to the idea of adopting hybrid architectures.

Having the ability to run workloads where it makes the most sense for the organization, both today and in the future, will give organizations the flexibility to pivot quickly to execute strategic changes and the agility to effectively navigate today’s landscape.

Trend 2: Increased interest in data federation, particularly in low or zero-trust environments
We are seeing increased interest from progressive organizations on things like data federation, particularly in low or zero-trust environments. While public sector organizations have been the forerunners in data federation, more progressive commercial organizations are also starting to take a keen interest in monetizing or sharing insights from their data without losing control of their data.

Enriching data gives greater insights, which adds complexities as the analysis of the data needs to be done while data is still in the encrypted domain. Therefore, collaborating, sharing, and learning from data in a federated manner will be increasingly necessary to organizations in both the public and private sectors as countries continue to push through digital agendas.

Trend 3: Accelerated migration from traditional data warehouse architectures to more open data lakehouse architectures on-premises and in the public cloud
As more organizations seek to be data-driven, the ability to derive real-time insights from any data, in any cloud, to make informed decisions is critical. To successfully deal with the changes of today and the challenges of tomorrow, organizations require the tools that enable them to turn data into strategic business asset. Tools with modern data architectures will allow organizations to locate, manage and analyze their data quickly and securely where it resides.

Open data lakehouse architectures are progressively becoming viable alternatives to the more traditional on-premises data warehouses. Modern data architectures and technologies, such as Apache Iceberg, enable great performance at a massive (petabyte) scale. Its open-source and cloud-native table format removes lock-in to a limited number of analytic tools and reduces unnecessary data transformations or data movement across tools and clouds to extract insights out of the data.

This will help to improve quality and reliability, as well as efficiency through support for multi-function analytics, and reduce data staleness along with the total cost of ownership. In addition, with the growing number of data protection laws and regulations, this architecture gives organizations the ability to go back in time and run reports and analysis on what the data used to look like.

Trend 4: Introduction and development of new AI/ML algorithms
We have seen some incredible new developments in AI/ML algorithms emerge recently, like DALL-E, an AI system that can create realistic images and art from input text descriptions, as well as improvements in speech recognition, like Whisper, that allow for accurate real-time voice-to-text translations. AI chatbots like ChatGPT are so effective that even Google is reportedly feeling the heat.

Over time, the improvements and developments in this cutting-edge field will cross over into the enterprise, and further drive the consumption of machine learning and processing. These can be used in a myriad of different use cases like fraud transactions and fighting financial crime, and ultimately help developers code more efficiently.