Top 8 Data Analytics Trends: Turning Insights Into Action

As the year draws to a close, world-renowned consulting firms and tech enthusiasts are wrapping up 2022 with their take on future data analytics trends. Following the example of Gartner data and analytics trends, we want to provide you with some actionable data analytics insights. However, unlike other reviews, we decided to focus on data analytics industry trends available in the short-term perspective and now rather than some far-fetched promises. 

Data analytics offers businesses the opportunity to gain valuable business insights and act on them at full tilt, making business intelligence consulting as crucial as ever. The technology continues to evolve, and so do the opportunities available to companies that leverage data in new and innovative ways. Staying abreast of the most recent future trends in big data analytics is essential to capitalize on these opportunities effectively. 

In this article, we will look at how various trends in data analytics will impact the business landscape as a whole and figure out how organizations can follow data analytics current trends to gain a competitive edge.

Data analytics trends that bring value to your company

Here are the top 8 data analytics trends that will shape how organizations compete with each other. 

1. Giving more users the possibility to generate data-driven insights

Organizations’ data systems are often organized up to top-down standards, effectively favoring C-suite and the organization’s main decision-makers when it comes to data access and leveraging business intelligence tools. 

However, harnessing data analytics services is no longer the purview of a few select individuals. As organizations look to capitalize on the potential of their data, they are beginning to realize the need for more users to be able to make evidence-based decisions on a daily basis. 

Let’s take the electronics manufacturing industry as an example. While engineering a new product is highly dependent on the availability and specs of certain materials, many engineers at manufacturing firms lack the means to access such data and, even more so, the tools to make sense of it. With a more democratized approach to data access, engineers will use self-service analytics tools to optimize and accelerate decision-making by quickly analyzing supply and procurement data. 

Granting access to data to more users is just one part of current trends in data analytics. Presenting insights clearly and concisely is just as vital as democratizing data. What makes information immediately actionable is engaging and comprehensible data visualization. With data visualization tools at hand, all users, regardless of their technical background, can understand complex datasets and make data-based decisions with confidence. 

2. Further development of open-source solutions

The reasons for the inevitable proliferation of open-source software as an emerging trend in data analytics may not be immediately apparent in a commercial sense. Still, its growing adoption continues to benefit both users and providers and the data analytics field as a whole. In very simple terms, the more people use open-source data analytics solutions, the more valuable these tools become. 

For example, by spreading access to an infinite number of data scientists, who share a drive for curiosity and exploration, R and Python remain on top of the latest trends in data analytics. Like a universal language, open source fosters innovation and collaboration and activates collective intelligence. 

In a more applied sense, open-source BI software will continue to rise in popularity because it offers a noticeably less costly way of building highly customizable analytics solutions without compromising quality. Open-source BI tools allow companies to avoid vendor lock-in and licensing fees, increase interoperability, and get efficient with data quickly.  

3. Refining the algorithms of data processing and analysis 

While it may sound trivial, the latest data analytics trends aim at making data analytics models more precise. By going through rigorous training and a whole lot of data cleaning, modern machine learning models can achieve never-before-seen prediction accuracy. 

However, when it comes to applying AI and data analytics, there is a crucial difference between an accurate forecasting system for now and a forecasting system that can remain accurate over time.

Given that customer expectations, business trends, and market conditions are in a state of constant flux, the prediction accuracy of an AI model that has been trained once inevitably diminishes over time. The only way for AI models to stay on top of the ever-changing business landscape is to learn continually from a never-ending stream of new data. 

In the coming years, we will experience the evolution of AI systems. Instead of relying on humans to adapt to new circumstances, self-sustaining AI systems will autonomously adjust their learning methods according to changes in the real world. Importantly, adaptive AI is also instrumental in achieving hyper-personalization. 

4. Seamless integration of data analytics solutions into the workflow

Organizations across industries recognize embedded analytics’s value for enabling more informed decision-making and driving better business outcomes. Embedded analytics solutions allow businesses to integrate data analysis directly into existing applications, creating a unified user experience that can dramatically improve decision-making speed, accuracy, and agility. 

Access to data analytics directly within the application allows users to rely on data when making their subsequent decisions. For example, with embedded analytics in place, high school teachers can see each student’s strengths and how effective previous teaching methods were on a particular group of students to adjust lesson plans considering students’ unique qualities. Digging through this gamut of data in a separate system is inconvenient and takes a lot of time, often discouraging users from applying data analytics.  

By granting immediate access to data at the very point of need, users will make critical decisions faster, reduce error, and become more encouraged to rely on data when performing day-to-day tasks. This data analytics market trend is particularly valuable for data-driven organizations that need to collect data quickly, analyze it in context, and make decisions based on real-time insights. 

5. More intuitive functionality of big data analytics systems

Due to rapid advances in cloud computing and exponentially growing volumes of data, analyzing big data has become both more important and economically feasible. Big data has often been perceived as an immensely complicated art accessible only to data scientists, and in the coming years, this will change. 

Current big data analytics trends hinge on making the interface of big data analytics systems more intuitive and easy for people with no technical background to use. This will cut the time between analyzing data and deriving insights, which is crucial in a modern business landscape. Currently, companies that provide big data analytics solutions are reimagining their interfaces to cater to the needs of a broader range of users. 

For example, in the past, establishing a robust ETL system in Qlik View required users to have solid scripting skills. With the release of Qlik Sense, creating an ETL system has become possible for users of all skill levels thanks to the intuitive visual interface. 

6. Returning to the roots: small and wide data

Big data’s impact on the business landscape can’t be overestimated. For example, for a digital financial exchange, tracking millions of customer data streams in real time and understanding their patterns is an essential source of competitive advantage. However, as the hype train around big data loses steam, companies have begun to realize that not everyone needs to analyze data at such a large scale to make business-critical decisions. 

Big data allows organizations to understand far-reaching trends that often reflect the general tendencies of their respective industries. It’s voluminous and variable and calls for a specific stack of statistical modeling techniques to be analyzed. Small and wide data, on the other hand, is more useful at drawing insights from small individual datasets that are useful for solving more specific and short-term business issues. 

Small data is usually found within an individual organization and is easier for humans to comprehend and visualize. In the coming years, we expect more organizations to focus on smaller, contextualized datasets that will help them make well-informed decisions. 

7. Easier data consumption

The exponential growth of big data also urges companies to rethink their approach to data governance. Traditional data management methods are no longer sufficient to handle the inherited complexities of orchestrating flows of data streams. Using outdated data governance practices results in data silos, compromised data security, and slows down decision-making overall. 

Data fabric is a way for organizations to create a comprehensive view of their data and make it more accessible, reliable, and secure across the enterprise. For example, well-thought-out data fabric architectures streamline data movement across the organization by sourcing only the metadata needed for data virtualization. The latter, at some point, might serve as a substitute for an extensive ETL process, as it provides access to data in real time without having to move or replicate it. 

Data fabric connects different types of distributed data sources and allows for creating a single, unified interface through which all relevant data can be accessed. It provides users with complete visibility into their entire data stack, simplifying the process of combining and aggregating valuable insights.

8. Data processing at greater speeds and volumes 

Edge computing has emerged as a significant data analytics trend, offering organizations increased agility and flexibility. By pushing processing and computing power closer to the point of data acquisition, edge computing allows for faster data analysis and reduces latency while improving scalability. 

Edge computing is especially useful when dealing with real-time IoT applications; wherein analytics are performed on the device itself rather than on a remote server. As such, edge computing allows organizations to reduce bandwidth costs and improve security for their data analysis processes.

Additionally, edge computing makes it easier for organizations to create distributed applications across multiple devices or platforms by routing data processing and analytics to the edge nodes. Going forward, we expect to see an increased focus on edge-based analytics and processing as more organizations look to take advantage of this technology. 

How to leverage future trends in big data analytics for your current needs?

As data analytics become increasingly sophisticated, understanding and following current trends in data analytics are pivotal to unlocking its benefits for businesses. However, acting on these trends requires vast technical expertise, strategic planning, and a deep understanding of the intricacies of data management. 

At *instinctools, we are dedicated to helping our customers future-proof their business by leveraging the power of data analytics in new and innovative ways. Our consultants can help you start off right with resources, insights, and strategies on how to design a robust analytics architecture. 

Embrace trends in data analytics to innovate faster and handle changes efficiently

Embracing the change in data analytics offers new opportunities for companies to remain competitive amid the increasingly more volatile business landscape. If we look at the bigger picture, the general vector of data analytics future trends revolves around cutting the time it takes to convert insights into action. Generating data-driven insights is now accessible to everyone. To get ahead of the competition, it’s crucial to make insights immediately actionable for the right user at the right time.