Data-driven decision making lies at the heart of departments across organizations. But to facilitate these decisions, analytics can no longer remain the purview of a small number of people who mete out reports as time allows — decision makers need direct access to analytics.
Embedded analytics has emerged as one way to meet this demand.
Business intelligence (BI) vendors are looking to drive user adoption through a focus on building systems that can handle the data demands of the modern world while democratizing analytics.
With this in mind, here are five trends to watch in embedded analytics:
The Public Cloud Surge Continues
Gartner’s recent report, Forecast: Public Cloud Services, Worldwide, 2013-2019, 4Q15 Update, showed a growth surge in the public cloud services market. The valuation of the public cloud services market is expected to reach $204 billion in 2016, up 16.5 percent from $175 billion in 2015.
Many software vendors employ public cloud platforms, including Microsoft Azure, Amazon EC2 and Google Compute, to run their applications, store and retrieve data. Some of these same software vendors have turned to embedded analytics to complement their solutions on public cloud platforms.
Multi-Tenancy Leads the Future
SaaS apps include a multi-tenant architecture, which allows for greater control, ease of maintenance and shared infrastructure. The high demand for this architecture comes from the benefits of cost saving and ease of management. Embedded analytics vendors following this trend towards SaaS as the de facto delivery model for software apps are building their products to work in these environments.
Internationalization for a Global BI Presence
The expansion of large multi-national corporations and businesses into different regions makes localization of software applications critical for widespread adoption.
Embedded analytics vendors are working to ensure that both the ability to customize and implement different languages in every facet of the application is possible. The ability to customize the look-and-feel of the application is as important for different cultures and business units as language localization features such as national language support (NLS). NLS ensures that the appropriate language pervades every aspect of the embedded analytics solution, from the data to the UI, for a seamless international experience.
Custom Visualizations and Precision
We will see a greater emphasis on tools that deliver powerful visualizations with great customization depth.
Specific industry verticals demand visualizations unique to their domain, leading vendors to offer greater levels of customization. Web-based applications also will be pushed to meet this demand for customization.
Demand for greater analytic precision will rise as organizations seek increased data source options, customizable drill paths, formulas, aggregations and other features that grant users analytical depth.
With the internet of things and the ever increasing levels of social media data adding to the growth of big data, the demand for analytics at scale becomes louder.
Organizations need to manage larger volumes of data while at the same time mitigating the contribution margin. Large-scale SaaS and on-premises applications that serve thousands of concurrent users with large data volumes require an analytics solutions that scales to achieve high usage while tolerating single points of failure.
In turn, embedded analytics vendors will look to scalable architectures and clustering features such as load balancing and distributed resource storage to manage increasing data volumes.
Title image Davide Ragusa