All about cloud native, Kyligence's prediction for data analytics

Luke Han, Co-founder and CEO, Kyligence
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Luke Han, Co-founder and CEO, Kyligence

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.

In the same time, all kinds of technology and innovation has already changed the way we run a business. How to catch the trend, adopt the cloud native technology for data analytics and support the decision-making? How to achieve growth in post Covid-19 era and stay ahead of the competition? Every company need to think about it.

As a leading AI augmented data platform provider, Kyligence have our own insights of industry. We summarize 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.

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.

Aligned with new technology and innovation, Kyligence is committed to the development and application of the next generation of intelligent big data analytics platforms. Both on-premise and in the cloud, Kyligence can extend data analysis to petabytes with sub-second query latency, accelerate mission critical queries, and enable the delivery of big data applications to massive sets of concurrent users.

Kyligence ‘s AI-Augmented data platform makes that possible by providing analysts and end users a unified, consolidated, and optimized view of data across the organization. Helps to identify and manage the most valuable data from on-premises to multiple clouds, simplify the complexity of massive data management, and boost the experience of analytics.

Conclusion

Data is next oil, and every business is transiting to data-driven. But the data industry today is still in 90s with huge human efforts to collect, manage and analyze data. Kyligence believes the power of machine learning and AI will automate the entire flow of data management and analytics. Just like Tesla's automotive factory unlocked hidden flexibility and innovation in manufacturing, Kyligence believe we can build an Intelligent Data Warehouse to fully unleash the potential of data intelligence with every one of you.

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Machine Learning

Big Data

Data Management

Data Warehouse