The definition of embedded analytics
What is embedded analytics?
Embedded analytics is the integration of analytical capabilities and data visualizations into another software application.
Embedding real-time reports and dashboards allows the end user to analyze the data held within the software application into which the analytics platform is embedded. With this analysis, the end user can identify and mitigate issues and spot opportunities to maximize.
For example, Kodak has embedded analytics into their printing press software. Kodak’s customers purchase the Prinergy software to help them centrally manage their digital printing systems, and access self-service reporting and dashboard tools directly in their application.
The analytics embedded into Prinergy allows the customer to monitor, for example, their ink usage, the number of print passes, and their production trends and volumes using the one application. This will enable them to more accurately forecast resource needs and help them increase the efficiency of their ink usage without having to use a separate analytics platform.
Embedded analytics is a window for the end user into the data that is held within a particular software application.
The analytics platform that is embedded into a software application may be unrecognizable to the end user as a separate piece of software. This is called white-labelling - where the analytics platform is completely rebranded to blend in with the look and feel of the rest of the application into which it is embedded. This allows enterprise applications to market the analytics as their own. Alternatively, the analytics platform may be grey-labelled - the platform provider’s brand name will remain. For example, it may say ‘powered by Yellowfin’ in the footer of the analytics module.
5 benefits of embedded analytics in your software application
Embedding analytics into your enterprise software application can bring huge benefits to your product. From increasing user engagement to providing new revenue channels to decreasing client churn, embedding analytics provides you with business opportunities. Here are five of the greatest benefits you’ll reap as a software OEM from embedding analytics into your app.
1. Sustainable competitive advantage over your peers
If you don’t have modern analytics embedded within your application that serves up actionable insights, you are falling behind your competition. Clients are coming to expect analytics on everything. More data is being produced than ever before and organizations want to capitalize on the wealth of insights locked in the software applications they purchase through analytics.
By embedding the right modern analytics platform, you enable your end users to leverage new technology to get to the heart of data - insights - fast. When you choose to embed an analytics platform that focuses on innovation, like Yellowfin, you will always have the latest technological advances available to you and your customer base. This gives you a sustainable advantage over your competitors.
2. The ability to create an amazing analytical experience for your customers
When you embed a modern business intelligence platform, you are able to deliver new capabilities that will transform how your customers use your application. They will be able to code their own capabilities into their dashboards, for example, or get instant, automated insights without the manual graft of data discovery. They can collaborate over data and share insights in a governed platform. Modern embedded analytics also offers an enhanced user experience with improved UI and flexible design options.
3. Additional revenue streams via upsell opportunities
Because of the huge value brought to your customers through modern embedded analytics, you will gain the opportunity to add revenue streams and upsell them. The analytics module itself could (and should) become an additional income stream. Or, you could, for instance, include dashboards and embedded reports as standard in your application and reserve automated insights as an extra that your sales team can upsell to clients. A similar thing could be done with data storytelling capabilities like Yellowfin Stories and Present.
4. The ability to get to market fast
If you build your own analytics capabilities, you will inevitably run into build and deployment issues, set-backs, and delays. The longer it is before you get to market, the longer it is before you generate revenue from analytics. But with the right embedded analytics partner (note: you’ll be in business partnership with your analytics provider as you’re embedding their software into yours, so choose wisely), you will be able to get to market fast.
For example, at Yellowfin, we offer the Quick Start package that walks you through how to do all the integration and building best practice dashboards and reports in the Yellowfin platform. In addition, we will walk you through how to market your application and prepare your sales team for selling the new benefits that come from embedding analytics into your application.
5. Allows you to focus your resources on uplifting your core product
Embedding business intelligence and analytics rather than building it yourself will also free up your developer resources to focus on your core offering. Analytics isn’t your endgame.
Because analytics is our expertise, we are constantly improving Yellowfin, adding new features, and integrating the latest technologies so you and your clients get an ever-better product and experience. That’s just not something you could dedicate resources to without it being detrimental to your core product.
What to prepare before embedding analytics
There's a considerable amount of benefits to embedding analytics into business applications, but ensuring it realizes value for your users comes with additional planning.
Determine your software's maturity for analytics: An exceptional embedded analytics offering is underpinned by the right framework, strategy and vision, which neccesitates the need to recognize and address where your application may need to improve its BI maturity level. That's what the Embedded Analytics Maturity Curve is for.
Build or buy embedded BI: The first debate in analytics adoption journeys, but not the only one - here are 8 key considerations for choosing an embedded analytics solution.
Recognising the signs when it's time to upgrade: If your product’s analytics can’t keep up with what your users want today and tomorrow, they will look outside of your product to fulfil their BI needs. This is why it's time to update your embedded analytics.
Common capabilities of embedded analytics platforms
There are some things that are standard in almost any platform, regardless of how good the platform is. But the leading platforms will deliver more, like automated options and AI capabilities. You should check out the five key things you need to know before buying analytics but for the basics, here’s a list of common features in embedded analytics platforms:
For any analytic project to be successful, organisations need their data to be ready for analysis. Data preparation capabilities provide what you need to connect and extract data multiple data sources while providing easy access to data outputs so end users consume accurate data. In embedded analytics scenarios, this capability is usually used by the software provider to ensure their customers have the most accurate, clean data to work with.
A dashboard is the best way to visualise multiple data reports in one place to help drive action. Dashboards allow you to group multiple reports on the same topic so your end clients can gain an overview of the business’ performance. Modern dashboards are even enabling developers to code actions, such as order buttons, into dashboards so users never have to leave the dashboard to complete their workflow. Embedded analytics platforms should also enable the end user to build dashboards themselves as well as customize the look and feel of the dashboard to match their brand.
To better understand their business, your customers need to explore their data, discover patterns and outliers, and share their insights with others. Data discovery capabilities within embedded analytics enable your clients to uncover insights in the data held within the application and let everyone share and disseminate the insights in a governed way. The rise of augmented analytics has brought automation into data discovery. Machine learning algorithms can continuously search for patterns in the data and automatically alert relevant users when there is a statistically significant change.
Reports should be easy to build and interactive. Embedded analytics end users may want to build their own reports, but even if they don’t, they will want to drill into further detail, visualise the data in tables and charts, query the data, and compare the data with other data points. Machine learning has enabled more powerful interactive capabilities such as the ability to click on a data point and have it automatically explained through related data and natural language explanation points. This gives greater clarity on the data faster so action can be taken sooner.
Being able to provide your customers with their dashboards and reports on mobile devices is critical in some industries. For example, being able to see machine performance, downtime, and production rates from anywhere on the factory floor via a tablet can be invaluable. In addition, being able to receive automated data discovery alerts to a mobile can be the difference between success and failure in highly reactive, fast-paced industries.
Governed collaboration over data insights and report creation within the embedded analytics platform saves your clients enormous amounts of time and provides added stickiness in your application. If your clients can collaborate where the data exists, rather than copy and paste screenshots into emails and instant messaging platforms, they will remain in the platform longer, will have live vizibility of their data, and can take action instantly.
The future: using augmented in embedded
With the rise of augmented analytics - the use of machine learning (ML) algorithms and artificial intelligence (AI) in analytics - more and more of the manual labor in business intelligence and analytics is being automated.
This automation is a huge benefit to you and your clients if you have embedded analytics into your software application. Augmented analytics allows you to market an AI-enabled product and provide your end users with data insights faster than ever before.
Automated data discovery, like Yellowfin Signals, will scan your client’s data for trends and patterns and instantly alert the relevant end user of any statistically significant changes. These could be dips, spikes, trend direction changes, or step changes. With instant alerts, the end user will be able to act immediately to fix issues or maximize successes. No more manual data discovery. (That also means that they don’t need as much internal analyst resource either - you’re saving them salaries as well as time.)
Machine learning can also assist the end user in uncovering insights as they query data. Capabilities like Yellowfin’s Assisted Insights allows users to click a data point and choose ‘explain’ or ‘compare’. Algorithms then populate the explanation or comparison with related analysis and even natural language explanations in bullet points so your users can get the most out of all their data by seeing it in context and with descriptions they understand.
The augmented analytics trend is only going to increase with automation expanding into more of the traditional analytics capabilities and assisting analysts and end users in their quest for insights and explanations.
How to Choose the Best Embedded Analytics Solution to Modernize Your Application
Download the comparison guide to uncover the different analytics vendor types, the benefits of each, and what you need to look for in an analytics partnership.