To understand everything about their enterprise, clients and workforce, decision-makers are turning to current dynamics to make meaningful and rapid decisions.
They must have real-time access to high-quality information that can tackle a question from every possible perspective.
In fact, a Capgemini Research Institute report, Data-powered Enterprises 2024, revealed that investment in analytics tools and platforms surged from 54% in 2020 to 76% in 2024 and two-thirds of executives said their organisation leverage their data for new products or services. Analytics also forms a key part of a CIO’s role. According to an AWS survey, 2024 CDO Insights, Chief Data and Chief Information Officers spend more than a fifth of their time on enabling new business with analytics. Business leaders struggle to deliver the needed insight fast enough.
One cause of this is the rising complexity and overwhelming volumes of data that are not properly harmonised. Another reason is that traditional business intelligence (BI) and data visualization tools cannot deal with complex and unstructured data, leaving much of the context out of the picture. Since today’s business intelligence is no longer locked in charts or numbers only, standard insight delivery tools often fall short of taking full advantage of corporate data.
In our interconnected business landscape, it’s vital to have a deep understanding of data. Organisations must incorporate time-based, location-based and relationship-based data in their analysis to gain more comprehensive and actionable insight. Visualising interconnected data is the best way for data and analytics leaders and CIOs to deepen their grasp of a complex landscape and the relationships that shape it. This visualisation can benefit many industry sectors, from healthcare to finance, and can enhance strategic foresight and operational efficiency whilst managing risk.
The Importance of Visualising Connected Data
Capturing and visualising information in context can have a significant impact on strategy. Any situation in which a leader wants to “see the data” is usually the catalyst for an evidence-based, ad-hoc report to explain the scenario—whether that’s a decline in sales or a sudden spike in web traffic. In this circumstance, a chart or dashboard will suffice. But what if you are a healthcare insurance provider or a research company? Here, the need might be access to electronic healthcare records, previous treatments, medications and complications, family history or care coverage—all items related to a physical person. Or it could be discovering and analysing years’ worth of research data and reports that deal with domain-specific concepts. In these scenarios, retrieving information from unstructured sources and visualising how it is connected, when it was created or updated and by whom, can help you quickly determine causes and relationships and make timely and accurate decisions. That’s when access to comprehensive intelligence from inside and outside your organization is needed.
Visualising the progression of connected data is also paramount. Since connected data encompasses information interlinked through networks, meaning and hierarchies, all of these require different techniques to be visually presented. Visualisation can elevate traditional data analysis approaches by revealing hidden patterns and trends, informing decisions and facilitating communication.
- Deeper Understanding: Visualisation promotes a more nuanced understanding of relationships between data entities such as people, locations and time. It is superior at identifying trends and irregularities that might escape standard visualisation tools.
- Intelligent Decision-Making: By monitoring entity interactions over time, data teams can identify improvement areas, process optimizations and potential risks or opportunities.
- Effective Communication: Having a visual representation of complex data enables greater access to insight by diverse stakeholders, particularly less tech-savvy business users, which can improve collaboration and business outcomes.
Complex Data Visualisation Challenges
Standard insight delivery mechanisms have some limitations that render them inefficient for visualisation. Most data exploration and BI tools can’t deal with more than one aspect of data at a time. Many use charts and binary data and can only offer a limited representation of the landscape. Even the more advanced tools that combine geospatial or network with time-based data in one graph don’t offer a 360-degree view of all relationships in your data. This leaves organisations pursuing DIY solutions to find the critical context they’re lacking.
Some data scientists have been developing make-shift solutions with open-source software to present time-related data relative to real-world locations. These might be plotted on a single graph, hacking custom code or integrating different tools to respond to the need for complex data exploration. It’s an arduous task to gain the right level of insight.
Alternatively, with vast amounts of data in a single graph, data teams face a cluttered interface where they can’t decipher a clear pattern. A powerful search tool or LLM integration is necessary to help fetch and filter the data to yield useful information.
With multiple data sources to draw from to contextualise this information for intelligence, tech experts face a complex data and enterprise stack while the growing costs of integration and maintenance add to budgetary pressures.
Creating Scalable, Interactive and Visually Compelling Data Exploration Applications
Organisations need a comprehensive data exploration tool that:
- Integrates with their data stack and can work with the full breadth of data formats and sources
- Facilitates large-scale and context-rich data exploration through interactive, dynamic search and visualisation
- Enables data users and decision-makers to uncover unique insights and relationships through visually rich, user-friendly applications
A native integration is critical as it enables organisations to accelerate the development of their intelligence applications and solutions on their existing data stack. Support for structured and unstructured data would provide organisations with a flexible way to work with all their data and extract value and insight from anywhere the data resides. A user-friendly interface that employs multiple visualisation and filtering techniques will empower non-technical users with easy access to the information they need and the ability to easily sift through vast amounts of data and quickly make sense of it. Finally, visualising complex data in a single interface allows users to see the full picture and explore problems and questions from various perspectives.
Next Level Data Visualisation
Today’s enterprise organisations need a clear understanding of their business world and their organisation to enable critical operations and agility. Connected data visualisation is the way forward, but within this, finding a user-friendly approach that gives context to data can turn complex data into critical business-building decisions.
About the Author
Matthieu Jonglez is SVP Product and Engineering – Application & Data Platform at Progress. Progress (Nasdaq: PRGS) empowers organizations to achieve transformational success in the face of disruptive change. Our software enables our customers to develop, deploy and manage responsible AI-powered applications and experiences with agility and ease. Customers get a trusted provider in Progress, with the products, expertise and vision they need to succeed. Over 4 million developers and technologists at hundreds of thousands of enterprises depend on Progress. Learn more at www.progress.com.
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