Friday, April 19, 2024

7 Future Data Visualization Trends Beyond 2024

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Data visualization trends keep pace with changing technology, leading to new innovations in how complex information can be conveyed in a broad array of visual formats. Over two decades, trends in data visualization have evolved alongside the widespread adoption of cloud computing, the emergence of big data systems that mine insight from unstructured data, and an almost insatiable demand for real-time data integration and analytics capabilities.

But emerging technology is on the verge of causing upheaval in the data visualization field, which sits squarely in the midst of the business intelligence (BI), data analytics, and AI. Here are seven predictions for the trends that will reshape data visualization over the next couple of years.

Featured Partners: Data Visualization Software

Visualization is Becoming a Feature

Early implementations of data visualization captured the popular imagination by presenting esoteric BI concepts in ways that could be easily assimilated by non-data roles. Dashboards and other big data visualization tools made data accessible to the sales manager, CEO, or HR director, for example.

The technology tended to overshadow the bread and butter of BI and analytics—the gathering and interpretation of accurate metrics—and data visualization tended to replace more in-depth BI and analytics tools. Who needed a data scientist or expensive data analyst when visualization software appeared to provide the data you needed? Data visualization aided in the democratization of BI, but it also played a role in relegating the entire field into the provision of easy-to-read charts and dashboards.

But as the novelty of visualization wears off, the pendulum is swinging back toward more fully fledged BI and analytics tools. In turn, visualization is returning to its rightful position as a feature, though one vital to many BI and AI use cases.

More Organizations Seek Multicloud Visualization

Organizations are increasingly adopting hybrid and multicloud infrastructures to leverage the scalability and flexibility of cloud computing while maintaining control over sensitive data. This shift is prompting advancements in data visualization technologies to seamlessly integrate with diverse cloud platforms and on-premises infrastructure, enabling unified access to distributed data sources. Advances in metadata collection, storage, and distribution are facilitating this shift, which in turn opens up new employment opportunities for those skilled in data visualization.

Robin Thomson, Global Product Manager for Connectivity at data and analytics provider SAS, said the desire for more and more in-depth metadata has made data catalogs integral to the evolution of data visualization. Data catalogs function as large, centralized repositories for metadata and data lineage information and streamline data discovery, enhance governance, and foster collaboration.

Further, increased integration between data visualization tools and data marketplaces provides streamlined platforms for buying, selling and sharing data assets while highlighting monetization opportunities.

Rising Compliance Burden is Driving Governance

Legal requirements such as the European Union’s General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) have significantly influenced data visualization. These policies and others like them require far greater emphasis on privacy, security, and compliance—as a result, data visualization platforms are prioritizing such features as encryption, access controls, and audit trails to ensure adherence.

In turn, this is driving innovation in such areas as data governance and security. Expect more data visualization tools to come onto the market over the next year or so that take compliance, governance, and security capabilities to a higher level. This will include greater emphasis on transparency, accountability, and consent management within data visualization ecosystems to safeguard privacy rights.

GenAI is Creating New Possibilities

Artificial intelligence and machine learning—including generative artificial intelligence (GenAI)—already play a pivotal role in augmenting data visualization platforms with intelligent capabilities. GenAI enables systems to generalize knowledge across domains and tasks. The next wave of GenAI/data visualization integration will feature automated metadata management, enhanced data profiling, and laser-precise anomaly detection, bias detection, and query optimization.

As a result, data visualization platforms can achieve greater efficiency, scalability, and agility while deriving far greater value from their data. A little further up the line, synthetic data generation will address data scarcity, privacy concerns, and model-training requirements.

Techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs) can create realistic yet entirely artificial datasets that mimic the statistical properties and relationships of real data, letting organizations mitigate privacy risks, reduce reliance on limited or proprietary datasets, and accelerate model development and testing cycles.

Businesses Need More Organized Data

As enterprises develop, revise, and solidify their AI and GenAI strategies, it will become abundantly clear that there is a necessity to get their data houses in order and make the flow of data as seamless and reliable as possible. GenAI tools need to be able to tap into more types of unstructured data and larger quantities of data than ever to unlock trends and visualize consolidated data for the first time.

More and more types of data and greater volumes of information are being accessed for analysis. As a result, data visualization is becoming more important than ever as a way to create a single representation of trends and patterns.

Vendors are Collaborating Against Silos

In the face of more and more users demanding use cases for their data, the traditional approach to data movement infrastructure design is proving difficult to scale, leading to silos. Breaking them down to unlock the full potential of data is becoming a top priority for enterprises. Progress has been made reducing the quantity of silos, consolidating data architectures, and providing centralized access to structured, unstructured, and semi-structured data for analysis.

But the problem has not been eradicated. Even those platforms that appear to consolidate information silos typically have lots of background processes running that mask countless integration and sharing challenges. These manifest in such forms as extensive data movement, processing that eats up overhead and adds latency, and cumbersome workflows that reduce agility, increase costs, and inhibit the results that can be derived from visualization and AI tools.

Data management, storage, cloud, BI, analytics, and the top AI companies are collaborating to remove any remaining barriers to achieving unrestricted information access regardless of the repository, type of data, format, or location.

Data Lakehouses are On The Rise

Databases led to data warehouses and data lakes. The next phase is likely to be data lakehouses that promise to manage data of all types in accessible, open formats while also providing a control plane that can manage, govern, and optimize access. For example, Google Cloud’s BigQuery data platform works across clouds and scales with data growth while allowing multimodal data to be processed for gaining insights at scale.

The real promise of the lakehouse is the ability to derive far more value from large volumes of data across diverse environments courtesy of significant advancements in data management and analytics. The goal is to unlock the full potential of data assets and derive actionable insights that drive business innovation and growth.

Bottom Line: The Future of Data Visualization

While the trends covered here are already becoming reality, it won’t happen overnight. It takes a lot of work to break down silos, rethink data habits, and implement new technologies. AI-based automation needs to improve to the point where it can augment data visualization capabilities and enhance metadata management, shortening the time needed to discover, classify, and tag data assets for improved searchability and be used to identify anomalies, inconsistencies, and errors.

The first signs of progress in this area are already showing up among the big IT infrastructure vendors and hyperscale cloud providers, and enterprises are beginning to put them to use. How enterprises use data visualization will continue to evolve, as will the tools and technologies behind it, but as data burdens grow, the importance of data visualization will only continue to grow in parallel.

Read about the 10 Top Data Visualization Tools to learn more about the software enterprises are using to share their information in useful ways and to get our expert recommendations for which tool to use for a range of use cases.

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