Wednesday, July 24, 2024

7 Data Analytics Trends

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Data analytics is a fast-moving discipline with cutting-edge technology evolving at a rapid pace. As this technology advances, data analytics trends shift to reflect the new functions and capabilities of the software and platforms as well as their impact on business operations. Today’s key trends in data analytics are the outgrowth of technology advances over the past several years that have made data more accessible to a wider variety of users, allow users to get more actionable insights out of unstructured data, and make use of the data generated by an increasing number of “smart” devices.

Here are seven current and emerging trends in data analytics to watch for the coming year.

Self-Service Data Analytics

Self-service data analytics platforms—a type of business intelligence software—are now common in the market, making it easier than ever for non-technical staff to perform data analysis that uncovers actionable insights from a company’s data. This democratizes data across an organization and helps contribute to a data-driven culture by making data more accessible to sales, finance, and C-level executives through the use of visualizations, dashboards, and natural language searches.

But one of the biggest challenges in self-service analytics continues to be developing data literacy among non-technical staff members, which is essential to the effective use of data. As more companies invest in self-service platforms, they will need to find ways to ensure that non-technical staff develop the necessary knowledge to increase their literacy and comfort with data.

Data Integration and Sharing

Tools that facilitate data integration and sharing are helping operationalize an even broader range of data across companies’ business units. In the past, much of the focus was on the rise of big data and the tools needed to store and process the vast amounts of data being generated. Now, the cutting edge of data analytics is on the optimization of these tools to reduce redundant data and increase speed and on the accessibility of the data to all users.

Data integration tools like Informatica PowerCenter, IBM DataStage, Qlik Replicate, Oracle Data Integrator, and Fivetran, to name just a few, help bring together data from far-flung sources, standardize it for use throughout the organization, and facilitate the development of effective data governance.

Hybrid Data Management Implementations

Over time, the scalability, processing capacity, and security of cloud-based data management software has driven many companies to invest in the cloud. As more varied sources of data become available and usable, data analytics infrastructure continues to evolve.

The result is often that different datasets live in different locations, including on-premises and in the cloud. In fact, Gartner estimates that almost half of the data management implementations combine both cloud and on-premises data sources. Tools that bring together widely distributed sources of data—like the data integration tools mentioned earlier—are making it easier to implement a hybrid data management solution.

Data Fabric

Data fabric is a metadata-driven data management design that unifies disparate data sources and makes them accessible to individuals across the organization. As hybrid data management becomes more common, data fabric architecture is the foundation that ties it all together.

The goal of data fabric solutions is flexible, reusable and augmented data—in other words, it makes data stored in a variety of different sources easier to manage and use. Because data fabric can support the scale of big data, it can be used for batch processes and real-time streaming, creating a thorough, current, and accurate dataset for analytics.

Data Governance

Data governance is the development of policies and procedures that specify how your business works with data. This covers data gathering, storage, taxonomy, availability, security, and preservation. Data governance also helps with regulatory compliance, ensuring that your internal policies and procedures comply with external privacy regulations.

Data governance improves the accessibility of data within your organization by reducing or eliminating data silos and making sure that all data is being cataloged and described using a universal set of terms. This in turn improves the accuracy of the organization’s analytics.

AI and Machine Learning

Artificial intelligence and machine learning continue to grow rapidly. Nearly all data analytics platforms have some form of embedded AI or machine learning to help with tasks like data cleaning, data transformation, natural language search and more.

AI is particularly important when it comes to preparing and analyzing unstructured data. As much as 80 to 90 percent of data is unstructured, and can yield even deeper insights into customer behavior and help companies identify areas where they can improve operations.

With that growth in AI, however, companies need to focus on managing AI risk through AI governance and responsible AI practices. These risks include ethical risks such as algorithmic bias, security risks and privacy risks.

Learn more about unstructured data.

Edge Computing

The continued growth of the Internet of Things (IoT)—smart devices that gather and use data—raises questions about how and where to collect, process, and analyze that data. Edge computing refers to the networks and devices that are closer to the end user, like point of sale systems, mobile devices, or sensors that can process data locally and connect to the cloud.

Research shows that, while 83 percent of C-level executives believe edge computing is essential to remaining competitive in future markets, only 63 percent are using it in some form today. These networks and devices can use online data and algorithms to improve customer service and augment worker effectiveness. Edge computing also increases the speed at which data can be processed by doing it close to the source rather than sending it back to the cloud and returning it.

Bottom Line: The Future of Data Analytics

As the amount and variety of data businesses collect and use grows, the tools, policies, and practices we use to manage that data evolve. Today’s data analytics trends leverage advances in AI, integration, smart devices and software that allow companies to make their data accessible to a wider variety of users and discover new ways to optimize their operations.

Read our Complete Guide to Data Analytics for Businesses to learn more about the processes and techniques enterprises use to turn data into actionable insights.

Featured Partners: Data Analysis Software

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