June 21, 2024

Rohit Amarnath is CTO of Vertica, a unified analytics platform that enables predictive business insights based on a scalable architecture.

There’s no debate: Data is one of the most valuable assets for businesses today.

While some organizations build entire business models surrounding data, others regularly capture, store and analyze massive amounts of it for drawing up conclusive patterns, capturing insights, predicting business outcomes, tracking consumer behaviors or improving customer engagement.

Gartner has found that businesses increasingly prefer data-driven decision-making to intuition-based decision-making, which probably accounts for why the data analytics market is growing at a compound annual rate of nearly 30%.

With these factors in mind, let’s look at five macro trends that will likely shape data analytics in 2023.

1. Analytics will become more pervasive, democratized and composable.

As demand for business intelligence (BI) and situational awareness continues to increase, analytics adoption will also keep pace.

Arguably, analytics and BI are already omnipresent across all major business sectors. This demand for insights across all business units is challenging and will continue to challenge analytics leaders to keep up with the demand—and the technologists behind them—to build systems that can expand and shrink with the cycles.

The “self-service” or “democratized” analytics model continues to be the holy grail toward which data practitioners strive. This model, where all business units (even non-technical ones) will have access to data and intelligent insights, can be hard to set up and scale. That does not mean the industry has been unable to evolve to satisfy this need. Cloud architectures, on-demand analytics platforms, continue to grow and deliver functionality to meet the demand. Albeit, the cost to do this at scale and for everyone in the organization can be daunting. Managing these costs can also lead to more composable technologies.

This is going to be an interesting trend to follow as a majority of large organizations find themselves adding more than one analytics or BI tool, according to Garnter. Gartner also believes 60% of organizations will use analytics technologies that are composable. In other words, organizations will fuse components from multiple analytics solutions to build business applications that provide a richer view of their data. Without a clear strategy, this may result in more cost overruns due to duplication of effort and data.

2. More businesses will operationalize AI.

Most organizations struggle to analyze the ocean of data they collect. This is because nearly 90% of data is unstructured or has no defined schema.

AI and machine learning (ML) technologies will allow businesses to analyze this unstructured data in a smarter and faster way. These technologies will also find patterns and trends in structured data that aren’t readily apparent.

By embedding or combining AI and ML technologies with data analytics and business intelligence (BI) tools, organizations should be able to tackle the most complex data types and uncover the hidden value of unstructured data at scale.

Today, AI/ML capabilities are already able to locate and extract data from unstructured documents with nearly 95% accuracy. It’s not hard to predict AI tools will continue to mature and gain popularity in 2023. It remains to be seen however whether Large Language Model applications (like ChatGPT) will have any impact on the analytics space, but we have already seen some interesting innovations that leverage these models for generating SQL queries from natural language.

3. Meta-data-driven data fabric will continue to rise.

As organizations integrate and automate disparate systems and leverage AI/ML technologies to analyze vast pools of data, they are combining traditional data sources and modern capabilities, which is where the concept of data fabric comes in. Data fabric helps organizations process and analyze data from systems that are both physically or logically different—such as on-premises, multiple clouds, social media, IoT devices, mobile applications, etc.—under a unified set of objects.

That said, data owners and analysts often ask, “Is this data in the right context?” By enriching the data fabric with metadata, analysts can gain a deeper, more meaningful understanding of data. This means adding context to data so that it provides meaning; understanding its relationship with other kinds of data which can lead to more holistic business insights and, finally, making judgments or actions that help extract data’s full potential.

4. Analytics will continue to extend to the edge.

The world is witnessing an explosion of machine-generated data from internet of things (IoT) and industrial internet of things (IIoT) devices.

The volume of this data is so vast it causes a major strain on traditional models of computing where everything is controlled and analyzed centrally. As a result, organizations are gravitating to a more decentralized computing model (a.k.a. edge computing)—where analytics, AI and decision intelligence is built into edge applications.

This model enables organizations to analyze data in near real time and provide more actionable data to decision makers. Edge computing also significantly boosts the speed of analytics. To cite several examples: errors or irregularities in data can be identified in milliseconds; factories can deliver predictive maintenance; banks can spot fraudulent transactions in real time; wearable devices can monitor changes in vital signs. Privacy concerns will also result in certain kinds of analytics being performed locally to prevent data leakage. As a result, compute on demand at the edge will likely facilitate the growth of the micro analytics closer to the end-customer.

5. Analytics will continue to enable more adaptive and real-time decision-making.

As analytics becomes more contextual and continuous, it should also become more adaptive, thanks to AI and ML technologies. Analytics, therefore, should no longer just focus on historical data but instead will process data in real time, understand context and adapt its behavior accordingly.

The core benefit of adaptive analytics is that organizations will be able to make decisions based on real-time data with an extremely high degree of accuracy. Because data is analyzed continuously in real time, the system itself shouldn’t become outdated or obsolete.

Conclusion

In short, data is the new oil, but one needs a powerful engine to extract, refine and harness it efficiently. Organizations that build a robust analytics foundation and a strong analytics culture and competency will certainly be able to innovate and make decisions more wisely.


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