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6 Data Analytics Trends Set to Reshape Business in 2024

5-people-collaborating 

There's no debate: Data is the most powerful resource for businesses today.  

While many companies build their business models around data, others garner, store, analyze, and use complex, bi-directional data in order to draw up precise, irrefutable patterns, decipher actionable insights, predict business outcomes, monitor and track consumer behaviours, and improve customer lifetime value. 

As per a study by Gartner, businesses prefer data-driven decision-making over intuitive decision-making. Intuitive decision-making that involves harnessing the gut feeling to make decisions is, more than often, incomplete and inaccurate. 

This probably accounts for why the data analytics landscape is increasing at a rapid rate of nearly 30 percent. 

With these factors in mind, let's identify the six macro technological trends that will likely revolutionize data analytics in 2024.

1.  Automation will be an organization-wide initiative 

IT leaders have relied on automation for data analytics for some time now. Deloitte report indicates that 53% of organizations have already begun implementing robotic process automation (RPA), and that's expected to rise to 72% over the next two years. However, studies have found that many automation use cases serve a limited purpose and remain isolated, creating governance issues and limiting scale. 

2024 will be the year when top-notch organizations will shift from leveraging isolated pockets of automation to more strategic, enterprise-wide automation. In other words, they will drive hyper-automation initiatives to analyze and use data to make quality decisions and deliver value. 

What's more, hyper-automation will lower costs. According to Gartner, by 2024, hyper-automation will help organizations reduce operational costs by 30 percent. Amid growing economic uncertainty, hyper-automation will transform data analytics, speed digital transformation, and accelerate growth to help organizations manage disruption and deliver value. 

2.  Composability will be a core business pillar

Data silos have been a consistent roadblock to connectivity. Using analytics that is pervasive, more democratized, and composable will be key in 2024 and beyond. 

Gartner predicts that 60 percent of organizations will use analytics solutions that are composable. In other words, organizations will become more composable and fuse different components from a variety of analytics solutions to gain a complete, richer view of their data. So, composable analytics adoption will be on the rise. 

It's obvious that businesses will prioritize composability. This will enable teams to reuse existing capabilities for integration and automation, thus improving time to value. As a result, organizations will have the flexibility they need to adapt with agility to emerging market demands in order to increase customer loyalty and drive growth in 2024 in a cost-effective and strategic manner.

3.  More businesses will shift from big data to big AI

Many organizations fail to analyze the large streams of data they collect. This is primarily because the majority of data (almost 90 percent) is either unstructured or has no defined schema.

With the help of AI and machine learning technological solutions, organizations will be able to analyze unstructured data much more smartly and quickly. These solutions will also identify patterns and trends present in structured data that aren't visible. By leveraging AI and ML solutions with data analytics and business intelligence capabilities, organizations will handle complex, bi-directional data types and unravel the value of unstructured data at scale. Today, AI/ML-powered solutions can locate and garner data from highly unstructured files or documents with nearly 95 percent accuracy. It's not difficult to predict artificial intelligence will continue to transform and mature and gain popularity in 2024. 

4.  More businesses will adopt meta-data-driven data fabric architecture 

Now, organizations are integrating and automating different data systems and relying on AI/ML technologies to analyze the ocean of data. And for that, they need to combine conventional data sources and modern capabilities. Here the concept of data fabric comes in. 

Data fabric empowers organizations to process and analyze complex, bi-directional data streams from systems that are both logically different and physically different, including multiple clouds, social media, on-premises, IoT devices, etc. 

However, the data has to be in the right context. By enriching the data fabric with metadata, analysts can garner deeper insights into data. By adding context to data, the meaning can be collected easily. Also, it helps analysts understand its relationship with different kinds of data, which can help organizations get holistic insights, and finally make informed decisions. 

5.  Analytics will facilitate adaptive and real-time decision-making 

Clearly, analytics has become more contextual and continuous. To manage data better, analytics should become more adaptiveall thanks to artificial intelligence and machine learning technological solutions. 

Analytics, therefore, cannot afford to just focus on historical data. Instead, it will analyze and process data in real time, comprehend the meaning, and adapt its approach accordingly. 

The fundamental benefit of adaptive analytics is that organizations will feel empowered to make proactive business decisions based on real-time data with accuracy. Because data is analyzed in real time, the system shouldn't turn obsolete. 

6.  Edge computing will transform data analytics 

There is a steep rise in machine-generated data from the internet of things (IoT) and industrial internet of things (IIoT) devices. 

The volume of this data is so big that traditional computing models, where everything is controlled and analyzed centrally, fail to perform and deliver. Both accuracy and speed suffer. 

To manage such large volumes of data, organizations are opting decentralized computing model (a.k.a. edge computing). In this method, analytics, AI, and decision intelligence are used in edge applications. It enables organizations to analyze data in real time and deliver actionable insights for decision-makers - quickly, securely, and accurately. 

Edge computing helps organizations deliver faster insights by performing analytics where data is created, rather than transmitting it back to a centralized database for processing. However, this will lead to increased security risk. Organizations will need to mitigate the risks by adopting cybersecurity mesh approaches, underpinned by API management. 

Conclusion

Simply put, data is a new resource, but one needs a powerful engine to harness its true potential. Organizations that take advantage of these trends to build a strong analytics foundation and a robust analytics-competent culture will be able to turbocharge their innovation strategies and decision-making. 

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ABOUT THE AUTHOR

Chandra Shekhar 

Chandra Shekhar is a digital marketer at Adeptia Inc. As an active participant in the IT industry, he talks about data integration and how technology is helping businesses realize their potential.

Published Wednesday, December 06, 2023 7:36 AM by David Marshall
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