Kyligence has announced its cloud data analytics predictions for 2021, focusing on the rapid growth rate of cloud-native data warehouse and data storage services that will enable the massive acceleration of analytics adoption.
According to Luke Han, co-founder and CEO at Kyligence and co-creator and PMC chair of the Apache Kylin project, the last decade in technology has laid the foundation for embedded intelligence in every sector of society. This is the result of a combination of highly automated cloud infrastructure, rich open source software innovation, mature data engineering and data science disciplines, and the general adoption of distributed computing techniques and technologies.
The following major trends guide his predictions for 2021:
Analytics Above All – Multi-Platform, Multi-Cloud: Chief Data Officers (CDOs) and Chief Analytics Officers (CAOs) will increasingly view their datasets and analytics beyond the boundaries of individual cloud and data platforms. While the expense of data movement will motivate data teams to leave data where it is, many will pursue ways to engineer their analytics pipelines to source data from multiple public and private cloud platforms, and across cloud storage, data warehouses, and data lakes.
Accidental Citizen Analysts: While some have cast doubt on the idea of Citizen Analysts/Data Scientists, there is an increasing desire by executives to push down machine enhanced decision making to a much broader population of information workers. The resulting flow of curated data and actionable intelligence will create a large pool of accidental analysts who are able to benefit from data driven insights without unsustainable retraining requirements.
Cloud Costs: Metrics and Mitigation: Virtually every cloud vendor now provides cloud cost forecasting tools to their customers. But there is a growing number of companies that are taking a harder look at the numbers and algorithms to provide first and foremost more accurate predictions, but also some intelligence on how to mitigate cloud costs to reduce the frequency of cloud cost overruns.
Data Factories for Machine Learning: Organizations whose early successes in machine learning have spurred them to expand their programs are finding that a fast moving production line of high quality datasets are the fuel that will drive that expansion. This will elevate Data as a Service to a higher priority for data engineering teams.
Clueless Infrastructure Components Will Struggle: The inexorable march of embedded intelligence in nearly all aspects of IT and Analytics infrastructure will raise the bar for vendors and platforms hoping to become an integral part of their prospects cloud stack. Increasingly, If IT can’t ask its infrastructure how it’s feeling via AI-Augmented functions, IT might see that black box component as a vestigial organ.
Data Gravity and Vertical Clouds: If data gravity attracts applications, data, and attention, then the formation of vertically aligned professional clouds are the inevitable next step in the evolution of industry exchanges, marketplaces, and trading systems.