Do you know that by 2028, the amount of data globally generated is projected to be 394 zettabytes. Now, you can imagine the amount of data we will have by 2030. You may ask how the data volume is increasing at such speed leading to Data Explosion? The answer is increasing number of data generation sources. Autonomous vehicles are generating data, drones are generating imagery data, IoT devices, and even AI is generating synthetic data. Managing such huge volume of data coming from dispersed channels will be a challenge for enterprises until we have something to automate and accelerate the analytics processes.

How are Data Analytics and AI shaping the future of Analytics for enterprises?

AI-Powered Analytics

AI is already in everything; it will be deeply integrated into our daily lives by 2030. AI is already integrated with the data analytics processes, but emerging new tech trends will drive major developments in AI powered analytics by 2030.

However, our analytics process calls for some major human intervention and sometimes leads to discrepancies in the insights. By 2030, we will notice autonomous analytics powered by AI agents doing everything from start to finish, without human intervention, and delivering insights and patterns.

Self-learning algorithms would learn new, evolving patterns, leading to more accurate recommendations and forecasting and reducing the time between collecting data and delivering insights.

NLP for Easier and Simpler Data Interaction

Recently, I have seen more non-technical users speeding up their work with AI and data tools. How are they doing it? Simply with advancements in NLP, it’s now possible for non-tech users to solve complex queries with simple language. It makes us think of an easier way, demanding more advancement in NLP to make it easier for users to use for their data analytics solutions. By 2030, NLP will enable users to ask highly complex queries in very simple language; instead of requiring technical knowledge to build queries, users can simply post the questions to get insights. It will remove the barrier that stops non-technical users from accessing insights. The conversational interface for data insights democratizes access to all users, leveraging insights to make informed decisions across all departments of enterprises.

Quantum Computing will enhance the Data Processing

People are talking about Quantum computing, and there is a huge possibility that one of the biggest breakthroughs by 2030 will be the adoption of Quantum AI in the mainstream data analytics process. We all know that data explosion is the real challenge that enterprises are experiencing today, and it will be huge by 20230. Tasks that usually take days to compute will be done faster with quantum AI computing. Quantum algorithm development for data analysis will open new realms for AI in data analytics.

Is data visualization going to be more advanced by 2030 with AI integration

Data visualization is no longer limited to charts and dashboards. Users now expect personalized contextual visualizations to meet their specific needs. By 2030, AI tools will automatically generate contextual reports and visualizations according to users’ preferences and objectives.

Can AI-powered analytics not only predict but suggest the best actions to take?

We expect to have more powerful prescriptive insights by 2030. Enterprises are already leveraging predictive analysis to predict trends. Now, we will have more advanced AI-driven models to recommend actions and the optimal course of action based on the given predictions. With the help of real-time data, prescriptive models will help enterprises with more optimal solutions.

Are we going to see more ethical usage of AI ensuring Data Privacy and security?

As data analytics capabilities expand, so will concerns about data security. As the volume of data grows, we can expect this to be more prominent by 2030. AI integration will help enterprises secure data while delivering actionable insights.

Unified Data Ecosystems

Recently, Microsoft introduced Fabric, a data unification tool. It indicates that by 2030, we will need a more integrated unified data ecosystem because data will be dispersed in various channels and locations. Microsoft Fabric features AI tools that enable enterprises to break down traditional barriers, enabling holistic insights and better collaboration between various teams.

Conclusion

AI and data go together to help enterprises achieve their digital transformation goals. Can we expect some industry experts to help us in exploring the right AI-powered data analytics? Yes, some leading AI solutions providers provide AI-powered solutions to their customers. Now you have a clear idea of what is coming to you; you can be prepared to get it done perfectly.