While visualisations remain at the heart of many modern analytics and BI tools, business challenges are becoming increasingly complex. As a result, organisations can no longer rely on legacy platforms but must instead focus on unlocking analytic insights beyond visualisations.
Unlocking analytic insights beyond visualisations
As a whitepaper from Pyramid Analytics observes, many business intelligence tools reduce analytics to a visualisation exercise. Indeed, these tools rarely give attention to data access, governance and scalability.
Although these tools “tout ease of use or advanced technologies,” many neglect vital collaborative and practical functionality. Nevertheless, it is evident that businesses now require more than self-service applications that produce “descriptive visualisations based on limited data.”
When self-service tools merely focus on visualisation, this minimises the importance of data governance and data integrity. In turn, this limits an enterprise’s ability to gain true insights from its data when making meaningful business decisions.
Adopting a holistic approach is key
In order to take analytics beyond data visualisation, Pyramid Analytics delivers insights in four critical ways. First of all, the company insists that data integrity requires a BI environment that balances self-service analytics with centralised management and governance.
As the whitepaper notes, data silos are the “invariable result” of ungoverned analytics. Pyramid therefore provides an administrative framework that “gives IT complete telemetry on data and users, but doesn’t require them to take an overly hands-on approach.”
Next, connecting end-users to the appropriate data is integral when making decisions. Pyramid thus offers an agnostic architecture that enables governed access to data across all organisational data silos.
In addition to this, Pyramid provides end-to-end analytics in order to deliver insights across the enterprise. Using a single platform, users can collaborate and share data, models, reports, dashboards, and publications across the enterprise.
Finally, the company brings advanced analytic capabilities to everyday users by blending familiar analytics and business intelligence with a pervasive machine learning (ML) toolset. In turn, users can apply sophisticated ML algorithms, store the output of those as part of an analytic model, and build complex calculations to derive new insights from existing data.
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