The Covid-19 pandemic has accelerated the adoption of advanced analytics and evolving technologies. Businesses are in a constant quest to predict the customer demand, gain visibility into the supply chain, engage customers through multiple channels, and support workers in the shifting work environments.
The global advanced analytics market will grow from $33.8bn in 2021 to $89.8bn by 2026, as reported by ReportLinker. The demand for data science professionals is also exploding—almost every enterprise values data as their critical asset and analytics as to the most needed competency.
What do you think are the catalysts for such growth? Let us look at a few trends that will dominate the industry in 2022.
Table of Contents
Unstructured data analytics
Over the years, data science and analytics models have mainly focused on structured data. But 90% of the data generated by organizations is unstructured. With the increasing adoption of machine learning, natural language processing, and AI, businesses should consider unstructured data for deeper insights.
The skill sets of data scientists also need to evolve to include unstructured data for effective analytics and AI. Also, data management to gain new frontiers while monetizing unstructured data across every business function.
Data fabric – the new strategic initiative
The shift to cloud storage solutions increased the need for data fabric. With the pandemic, the work culture changed drastically. Organizations had to deal with a diverse ecosystem of devices, tools, applications, and data infrastructure. Storage now spans across on-prem, public cloud, multi-cloud and hybrid clouds. Also, there are too many choices like data lakes, data warehouses, etc.
Data fabric, the new architecture knits together the data across cloud and others seamlessly. It automates data exploration, ingestion, and preparation activities to reduce delivery time significantly. As per Gartner, data fabric deployments to quadruple efficiencies by 2024.
Real-time data processing
The business scenarios are changing rapidly. Time and speed matter a lot while acting on the customer needs in this digital world. Organizations now face challenges in processing the data in real-time to cater to the immediate customer needs.
For example, consider the retail industry; personalized product recommendations need to happen in real-time to understand buying patterns and preferences. Similarly, there are many such scenarios across industries where real-time data processing is a pressing need. So, organizations continue to focus on real-time data processing in 2022 and beyond.
Hybrid and multi-cloud solutions to become prominent
The shift to the cloud is not a new trend. Organizations continue to move the data analytics solutions to the cloud to reduce costs, improve scalability and simplify storage. According to Gartner, the spending on public cloud services will increase by 21.7% in 2022 compared to that in 2021. Also, by 2026, public cloud spending may exceed 45% of the overall IT spending.
As the data volumes grow exponentially, cloud computing provides faster access to broader data and manages these volumes for improved decision-making. Hybrid and multi-cloud solutions became the need of the hour in such demanding scenarios.
More focus on data quality and governance
Data quality and governance were always most critical to ensure trusted insights. With increasing AI and ML models adoption by businesses, it is vital to ensure data quality. The insights derived from these advanced analytics models may not impact business operations.
Proactive data quality measures and best data governance practices are the best remediation solutions in the current scenario of rapid changes. Though from the outside, it looks like a process issue. But, there is a lot to do by leveraging the existing MDM solutions and data quality tools available.
Edge Computing for more actionable insights
IoT devices now play a vital role in the connected ecosystem. The insights are now not limited to the data within the businesses. But there is more to do with the data from distributed applications, devices, and people. Hence, to cater to such growing needs, organizations are shifting their focus to edge computing to effectively store data closer to the physical assets.
According to Gartner, more than 50% of the data to be created outside the cloud by 2025.
Rethinking about small and wide data intelligence
With the pandemic, most businesses understood that large historical datasets might have less impact on future predictions. Small and wide data sets involving different data formats like structured, semi-structured, and unstructured may bring meaningful insights. The future AI models may be data-centric, with small and wide data defining the effectiveness.
According to a Gartner report, around 70% organizations may shift focus to small and wide data by 2025.
As the talent crunch continues in the data and analytics space, organizations now seek alternatives to effectively leverage advanced analytics and AI. Augmented analytics aids in the democratization of analytics and AI models. It is about leveraging AI, ML, and NLP for data integration, data preparation, and insights.
With the rise in the need for data discovery tools, AI and ML capabilities have been implemented directly in the BI and analytics systems. In 2022, we will see organizations’ continued focus on augmented analytics.
Decision-centric AI and analytics
Many organizations now struggle to access timely insights from a variety of data. Leaders now need context, real-time insights, and automated AI and ML models to support their decisions. Decision intelligence focuses more on how decisions should be made and designed the best decision for future goals.
As per Gartner, more than one-third of organizations may practice decision intelligence by 2023.
Automated data cleansing
Access to data will not suffice for the comprehensive, advanced analytics needs. Inaccurate and duplicated data result in loss of time and effort in generating insights rapidly for businesses. Hence AI-enabled automated data cleansing routines can save a lot of effort and accelerate the time to value for organizations. Organizations may continue to look for such solutions in 2022.
As the reliance on data becomes mandatory for organizations, the focus is now on saving time and effort to generate insights. Additionally, businesses now look beyond AI and ML adoption to the quality of insights generated. Hence organizations continue to look for strategies to ensure complete control of data while accelerating time to value.