7 trends that will reshape data analytics in 2022

By Sarita Digumarti, Chief Learning Officer at UNext Learning

Since the outbreak in 2020, organisations, be it small-scale or in the big leagues, are increasingly leveraging data & analytics to reduce costs, improve customer experience, optimise existing processes, and achieve more targeted marketing. One year later, many businesses have adapted to the sudden shift in the world’s dynamics. Those not broken by the previous year’s challenges have grown stronger, owing primarily to technological advancements, especially in the data analytics space.

Given the high stakes in 2021 and 2022 right around the corner, it becomes imperative for organisations to keep pace with the future trends in data & analytics to remain abreast in the fast-evolving digital business world.

Here are seven key trends organisations should look out for that will reshape data & analytics in 2022.

1. Augmented Analytics
Augmented Analytics utilizes Machine Learning & Natural Language Generation for automating data insights for data preparation, discovery, and sharing. The insights gained by leveraging Augmented Analytics improve the process of making business decisions. The insights are available throughout the organisation, reducing the workload of Data Scientists & Machine Learning (ML) professionals allowing them to invest their time in more significant business goals. By 2025, the global Augmented Analytics market size is projected to reach $29,856 million, as per an Allied Market Research report.

2. Data-driven Consumer Experience
Whether it’s a product or service, customer experience is an essential part of any business. The current market conditions have a lot to do with why customer service is directly related to branding. Data-driven customer experience is about how companies collect consumers’ data and leverage it to provide increasingly worthwhile or enjoyable customer experiences. From AI chatbots to Amazon’s cashier-less convenience stores, consumer interactions with businesses are becoming more digital. This means that almost every aspect of customer interactions can be measured, analysed, and transformed into insights to understand how processes can be smoothed out or made more enjoyable. Thus, there has been a push to increase the personalisation of products and services that businesses provide to customers. Its demand will continue to grow in the coming years.

3. Rise of Predictive Analytics
Predictive analytics is a practical application of Big Data and Business Intelligence. Many organisations are successfully leveraging various Big Data analytics features to forecast potential future trends. This includes utilising oceans of market data, new customers, cloud applications, social media, or product performance data to perform predictive analytics. A recent Facts and Factors report predicts that the Global Predictive Analytics market will reach USD 22.1 billion by 2026.

4. Self-Service Analytics: Critical to Man-Machine Synergies
A modern data analytics service is the ideal amalgamation of technological and human intelligence, and it has manifested as self-service Business Intelligence (BI) tools. These tools will enable organisations to extract actionable insights from robust BI platforms. They guide where to look and which areas to address first by generating real-time reports. In recent years, the automation of Data & Analytics has made self-service BI tools even more important in lowering operational costs. This method of decision-making based on facts is a wise business move, making data interpretation easier for both technical and non-technical teams. A recent Mordor Intelligence report suggests that the self-service BI market will grow at a CAGR of 15.5% by 2026. Based on the increasing demand, it is undeniable that in 2022 companies will continue to adopt self-service Data Analytics solutions to support more fact-based daily decision-making.

5. Cloud Native Analytics Solutions
The COVID-19 pandemic compelled enterprises and SMEs to go remote rather than rely on traditional systems. Cloud-based technologies, tools, and solutions came into play at this point. These technologies not only facilitated remote work but also helped reduce costs associated with traditional technologies and bottlenecks. However, as the cloud has become more widely adopted, an increasing number of businesses will seek cloud-native analytics solutions to streamline Business Intelligence. This will enable organisations to gain a competitive advantage over their competitors by utilising flexible and efficient BI and analytics. Cloud-native analytics solutions will be of great importance in the coming years.

6. Increased Adoption of BI in Industries
Recent years have seen increased adoption of Business Intelligence (BI) tools in various industries, including consumer services, technology, manufacturing industries & business services. It will continue to do so in the coming years. As per the latest survey by Beroe, Inc., the global BI market is estimated to reach USD 30.9 billion by 2022. The key factors driving this trend are big data analytics, demand for data-as-a-service, and self-service BI capabilities.

7. Mobile Data Analytics: To Navigate Future Data Analytics
Mobile Data Analytics will be a critical component of future Data Analytics services. Because of enhanced security features such as bookmarks, widgets, and face ID, mobile data analytics will help close business decisions faster than ever before. Furthermore, the application of Augmented Reality will facilitate the viewing of datasets and dashboards in interactive real-world simulations. As a result, working on smaller screens will become more convenient and intuitive.

The most noteworthy takeaway from the aforementioned Data Analytics trends is that Data Analytics is no longer just an option for business success. Companies will have to prioritise it as an essential business function in 2022 and beyond, correctly identifying it as a must-have for sustainable business growth. Organisations will need a more data-literate workforce that can effectively work with Data Analytics tools and help them make data-driven decisions that optimise business strategies and drive productivity.