Executive Summary: In 2024, big data is defined by a shift towards real-time processing and AI/ML integration, enabling faster, more informed business decisions. Investments in AI and machine learning are revolutionizing data analytics, while Data-as-a-Service (DaaS) offers scalable, cost-effective management solutions. With the rise of data volume and privacy concerns, ethical data governance has become paramount. Across various industries, AI/ML automation is driving growth and innovation, redefining consumer services and operational efficiency. This big data evolution is laying the groundwork for future advancements in business intelligence and strategic decision-making.
AI-Fueled Data Management: The Key to Transformation
As we enter 2024, the big data and AI landscape is not just changing; it’s changing fast. The main reason for this is the shift to real time data processing. Batch processing is no longer enough to handle the real time analysis demands of the massive amounts of data coming in from the Internet of Things (IoT), social media and a variety of digital channels. The shift to stream processing is allowing businesses to make faster, better decisions and optimize the use of the vast amounts of data that have previously been underutilized. According to a survey, businesses have traditionally been able to utilize around 57% of their collected data, leaving a large portion idle. Advances in technology are now allowing businesses to process data in real time, increasing the percentage of data that can be utilized. ("Stream Processing Enables Real-Time Big Data Insights" - (https://explodingtopics.com/blog/big-data-trends)).
AI and machine learning are also playing an increasingly important role in automating and simplifying analytics.
More than 60% of IT executives say they plan to increase investments in AI and machine learning, reflecting growing recognition of their centrality to big data analytics. As the focus shifts to data federations and distributed data computing, it’s not just about simplifying data architectures and reducing attached operational costs. It’s also about increasing investments while leveraging more flexible, data compliance-friendly technologies. These technologies aren’t just tools for analyzing data, they’re the catalysts for generating faster, more actionable insights that are changing the way businesses manage big data.
The ethical collection of customer data has also become a major focus. As a large proportion of big data includes consumer data, companies are now focusing on software and practices that enable ethical data collection. By doing so, they can ensure compliance with regulations such as the GDPR and maintain data integrity. This is especially important at a time when consumer confidence is at an all-time high. I see an additional shift towards more compliant technologies, like federated data processing or distributed data storage and analytics tools in favor of central data lakes ("Ethical Customer Data Collection" - (https://www.datamation.com/)).
Data-as-a- Service, accelerated by the increasing numbers of digital data channels, IoT, social media, AI is on the rise as a scalable, cost-effective solution to data management challenges. The sheer amount of data generated on a daily basis means that DaaS platforms provide cloud-based tools to collect, analyze, and manage data efficiently. By taking advantage of DaaS, businesses benefit from big data analytics without the need for large infrastructure or expensive storage platforms, making big data analytics accessible to everyone.
The effects of big data can be seen across a wide range of industries
The phenomenal growth of big data – yes, it’s not dead, and it’s gaining traction again thanks to the AI hype – has also brought with it a strong need for governance. As the focus shifts to privacy and regulatory issues, the importance of data governance increases. As companies grapple with compliance challenges, it becomes evident that effective data governance is strategic. It’s not just about maintaining data integrity and availability; it’s about succeeding in an increasingly regulated and privacy-conscious digital landscape. The focus here shifts also to more regulation compliant technologies, let many CxO's wonder if a data lake strategy is really future proven, especially in combination with real-time data processing. The effects of big data can be seen across a wide range of industries. From the banking sector to the healthcare sector, the combination of big data and automation is driving business growth, technological innovation, and strategic transformation.
The synergy between big data and AI/ML powered automation is particularly striking. It’s not just about improving consumer-facing service offerings and internal operations, it’s about reimagining them. It’s about driving businesses toward a future where real-time predictive analytics and predictive decision-making become the norm, not the exception.
Scalytics empowers organizations to leapfrog in their digital transformation journey.
To sum up, as we enter a new era of a data driven society, where AI will have a significant impact, it is clear that real-time data, AI/ML, federated data processing and data governance is more than just a trend, it is the foundation on which the future of B2B intelligence and decision making is built. The road to this data-centric future is paved with innovation, productivity, and strategic growth. It’s an era where data isn’t just an asset; it’s the catalyst for change and success. As we embrace this era of digital transformation, it's clear that the right tools and platforms are key to harnessing the full potential of these big data trends. Scalytics empowers organizations to leapfrog in their digital transformation journey, keep them on track in the evolving landscape of real-time data processing, AI/ML integration, and data governance. Ready for the future?
Thank you, have a great start into 2024
Alexander, CEO & co-founder
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