Can embedded analytics prove as a better alternative to BI platforms?
Today, disruptive technologies like artificial intelligence (AI), big data, the Internet of Things, cloud, and blockchain have the potential to revolutionize almost every aspect of our professional and personal lives. And this disruption is fueled by data. Dubbed as oil of modern technologies, today, data is generated by every kind of device, interaction, movement, and transaction – and is multiplying at an exponential rate. To derive meaningful insights via extraction and analysis, we need a proper analytics tool. In the business domain, these insights can help to observe market trends, make informed decisions, identify shortcomings, and more. This where embedded analytics technology embraces the latest developments in terms of data mining, processing, visualization, and storage to help the decision-making process.
What is embedded analytics?
Embedded analytics, according to Gartner, is a digital workplace capability where data analysis occurs within a user’s natural workflow, without the need to toggle to another application. Moreover, embedded analytics tends to be narrowly deployed around specific operations such as marketing campaign optimization, sales lead conversions, inventory demand planning, and financial budgeting. Disruptive technologies like artificial intelligence, machine learning empower embedded analytics even further to find patterns, connections, and themes in the business data with enhanced accuracy and speed. Further, its features aren’t limited to software vendors; rather, its benefits and use cases expand beyond the world of software development to power business processes and teams of all kinds. It also allows users to authenticate users through those apps while maintaining data permissions directly from the analytics tool.
Why is it better than BI?
While we have traditional business intelligence platforms, embedded analytics can be an upgrade in some cases. With traditional BI, only those with technical expertise and SQL skills can participate in modeling, querying, and creating visualizations. However, in the case of embedded analytics, users of all types can participate, including the line of business teams, partners, vendors, and even customers. Moreover, it easies the integration of BI platform capabilities into users’ systems and applications. Additionally, reports and visualizations made available by embedded analytics within existing workflows, software, and systems allow non-technical users to work with and benefit from data insights easily.
On the contrary, traditional BI restricts real-time reports and visualizations to be viewed within the analytics tool. In short, embedded analytics offers benefits to all departments. For instance, it’s a valuable tool for end-users to explore their data and discover new insights. Meanwhile, product managers can enrich and cultivate a data-driven experience for their customers with the latest built-in analytical capabilities too. This further means, users don’t need to switch to separate BI applications for reports and visualizations.
Other benefits include embedded analytics tools generally doesn’t demand per-user BI software licenses (fixed price) and takes less effort on reports integration/modification/upgrades. Also, they guarantee that users will see only data that they are permitted to access. These tools can drive sales and increase conversions by adding widgets to landing pages. Besides, it enables the transaction process system (TPS) or the information system to provide analytical services without being dependent on any external or third-party analytical application or system. Companies also get the opportunity to add revenue streams and upsell them. Apart from that, choosing the right embedded analyst partner can help businesses penetrate the market faster than their rivals.
According to Logi Analytics, the business user adoption rate of traditional BI applications is 21%; it’s 60% for embedded analytics. The same study found that 84% of business users report spending more time in applications that feature embedded analytics. Additionally, trading spreadsheets for embedded analytics can boost productivity too. Lastly, for consumer brands, embedding analytics behind their customer-facing storefronts can lead to repeat sales, larger shopping carts, and happier customers.