Kyligence CEO Identifies Big Data, Cloud, AI and Data Analytics Predictions for 2022December 16, 2021
Kyligence, originator of Apache Kylin and developer of the AI-augmented data services and management platform Kyligence Cloud, today announced its predictions for the important trends in data analytics for 2022. The theme of this year’s predictions centers around automation and empowering more individuals to do more with data, with less effort and fewer impediments.
According to Luke Han, co-founder and CEO at Kyligence and co-creator and PMC chair of the Apache Kylin project, the data analytics world has not stood still in the past year. Han predicts that the post Hadoop world will have more refined cloud data services for a growing array of citizen participants, and the need for low code/no code building blocks in the form of Data APIs will increase.
The following trends guide his predictions for 2022:
Life after Hadoop
In 2022, we can expect the continued decline of the Hadoop platform. Expect CIOs and data teams to continue to de-emphasize Hadoop and to continue the process of removing it from their production data stack.
Also look for IT departments to continue to make their on-premises implementations look and function like the public cloud. In the near term, organizations may continue to use the Hadoop File System (HDFS) as a storage platform until a better private cloud storage solution can be devised.
To protect existing investments, and to comply with local government regulation, organizations can’t simply move all existing workloads and applications built on top of on-premises Hadoop to the public cloud. The on-premises data stack will continue to exist. A hybrid solution across the public cloud and private cloud will be a more practical approach.
As more technology organizations look to drive greater automation of analytics processes and greater developer productivity, the monetization of APIs and the pursuit of an API economy will naturally affect data engineering, data management and analytics. In 2022, an increasing number of businesses will push data driven decisioning and predictive analytics into the mainstream with a factory-like Data-as-a-Service approach.
The systematic implementation of APIs that deliver data, metadata, and essential intelligence will not only be used for public, customer facing processes, but also for internal usage. This will put DaaS APIs at the center of a hub for all enterprise processes, workflows and management.
Low Code/No Code for Analytics
In today’s world of continuous integration and continuous delivery (CI/CD), taking a week or less to create a meaningful data application is becoming increasingly common. But shrinking the development pipeline for a larger population requires a lower bar to entry into the application creation process. The growing popularity of low-code/no-code platforms in the coming year promises to accelerate that trend.
Because low-code/no-code platforms enable non-coders to build their own data applications organically, the logical next step for such platforms is to tackle human curiosity and creativity as it applies to analytics. Fundamental to making this happen is the creation of modular workflows for data management and data pipelines, and a greater adoption of robotic process automation (RPA) for data services. This is the next wave of automation for analytics, machine learning and AI.
Metrics Stores Eclipse Business Intelligence
Although business intelligence will continue to grow at a healthy clip in 2022, it is not for everyone. Its use is skewed toward decision makers, dashboards, and reporting. While tracking KPIs can also be a BI use case, the ubiquity of key metrics and their relevance to virtually every facet of the business and every user begs the creation of a metrics store.
If you consider that BI is bounded by the notion of creating and depending on a single source of truth to analyze business operations, sales, and marketing, the broader more universal use of metrics can be thought of as a single source of reality. This reality applies to every user, not just executives and leaders. Therefore, it will be metrics stores that will drive digital transformation, not BI.
Data Consumers Will be Augmented by AI
While some are skeptical about the notion of a citizen data scientist, it is easier to predict the rise of a citizen data analyst: someone who consumes and leverages data, metrics, and insights as a natural extension of their job description.
Better APIs and low-code platforms are a great step in the right direction. AI will increasingly empower those who lack the technical skills of a power user. To achieve this, software vendors will need to improve their understanding of how data consumers use and gain advantage from data.