According to Market Research Future, the market for embedded analytics is forecast to grow by 14% per year to a $52 billion market by 2023.
So what is embedded analytics, and what’s behind this increasing interest?
Analytics in general refers to the data acquisition, data transformation, and business intelligence development required to convert raw data into actionable business insights. Traditionally, analytics capabilities exist in a stand-alone infrastructure. This requires users to switch back and forth between business workflow applications, which often leaves analytics solutions unused and their value unrealized.
But embedded analytics is changing this dynamic for the better.
Embedded analytics is “the use of reporting and analytic capabilities in transactional business applications.” Essentially, it’s the marriage of analytics and workflow applications that bypasses the problem of toggling between two separate systems, saving time and enabling greater control over the process. It merges employee workflow and contextual insight, which delivers the most timely and relevant insights into the user workflow with minimum disruption.
Embedding analytics into normal user decision-making ensures users are more likely to see and act upon analytic insights, so connecting users to context-specific analytics can have many wide-ranging benefits.
Here are 6 ways embedded analytics can improve your business.
1. Cultivate Data-Driven Decision Making
According to the Gartner Group, “A key characteristic of a data-driven culture is using data in a pervasive way. Data-driven companies establish processes and operations to make it easy for employees to acquire the required information.”
This is where embedded analytics shines.
Presenting timely and relevant data insights to users within their normal business applications conditions them to think analytically while doing their daily work. When implemented at a wide scale, embedded analytics hardwires data-driven decision-making into your organization’s cultural DNA.
Embedded analytics also provide the opportunity for agile analytics development. HealthCatalyst notes that a data-driven culture is a two-way street: “The analytics infrastructure also needs to be able to monitor application usage. These insights enable the team to refine the analytics initiative and increase utilization – this is an iterative process.”
In other words, embedded analytics not only provides business insights to users, but also provides user insights back to the analytics development team, which drives new and improved products.
2. Increase ROI on Data Transformation Investment
Developing stand-alone analytics products requires extensive work in order to acquire and transform data into useful and relevant insights. It’s common knowledge in the IT industry that the Extraction/Transformation/Load (ETL) phase of an analytics project is always “the long pole in the tent.” It’s simply time-consuming and expensive to source, profile, clean, test, audit, conform and validate data prior to analytics use. Given the time, resources and expense involved in ETL work, showing a return is paramount.
Presenting insights in a user workflow accelerates the value of the data transformation effort. According to Logi Analytics, the business user adoption rate of traditional BI applications is 21%; for embedded analytics it’s 60%. The same study found that 84% of business users report they spend more time in applications that feature embedded analytics.
Those are pretty compelling numbers.
The increased utilization and “stickiness” of embedded analytics can help win over the business user community and reduce the risk associated with a long ETL work effort.
3. Increase Productivity
Embedded analytics enables users to spend spend less time switching back and forth between business applications and analytics tools, and more time on value-added activities. Adding self-service functionality to embedded analytics brings another benefit: less work done in spreadsheets.
The legacy method of performing analytics is to use spreadsheets to prep, analyze, visualize and distribute analyses. While spreadsheets are easy to use, they come with productivity-killing drawbacks, such as:
- Static data
- Little data validation
- High susceptibility to data corruption
- Difficulty in pinning down a single source of truth when multiple versions of an analysis are emailed to multiple recipients
- Embedded macros that tend to malfunction for unaccountable reasons
Moving to embedded analytics avoids these pitfalls and leads to dramatic productivity gains. For example, SmartDataCollective found that one company increased its productivity by 43% by trading spreadsheets for embedded analytics.
And end users are not the only ones seeing productivity gains from embedded analytics.
Sometimes, analytics development teams become victims of their own success as analytics solutions invariably cause an increase in ad hoc requests. Over time, the growth of these one-off requests inhibits the development of new, value-added analytics products.
Embedded self-service analytics can help with this challenge by giving end users a quicker avenue to the insights they need while freeing up development teams to focus on new products that help grow the business and create differentiation.
4. Enhance Competitiveness
Embedded analytics is transforming consumer applications. Widely-used mobile apps like Amazon and FitBit have raised consumer expectations for analytic-enabled apps. These apps embed analytics so seamlessly that the user almost doesn’t notice they’re there, though they effectively influence user behavior. Adopting a thoughtful, embedded analytics approach is becoming a survival strategy for any consumer app.
Combining traditional workflow with contextual insights can reinforce your commercial applications’ unique value proposition with customers, depending on the depth to which you embed analytics in your applications. According to Logi Analytics, there are 5 levels of embedded analytics you could pursue, each with progressively increasing costs and benefits:
- Standalone: Analytics live in a separate environment from business applications. This is the lowest cost but least useful option.
- Bolt-On: Integrating analytics and business applications into the same single sign-on framework. This approach can marginally decrease the switching costs of the standalone approach.
- Inline: Analytics appear within the business application as a separate pane or portlet like a “Reports” tab. Most analytics tools support this level of integration, so it’s relatively easy to do.
- Infused: Analytics are embedded into the “core” functionality of the business application and provide context-specific insights that can deliver significant user experience and productivity gains.
- Genius: Infused analytics with integrated, self-service capabilities. This model empowers users to pursue value-added insights on their own within the context of a business application.
Logi Analytics also found that moving to more deeply-embedded analytics opened the door to premium pricing opportunities.
5. Improve Customer Satisfaction
As B2C giants Amazon and Netflix have learned, embedding analytics behind their customer-facing storefronts can lead to repeat sales, larger shopping carts, and happier customers.
Built on the viewing habits of its 125 million users, Netflix combines an impressive array of big data management platforms, content curation and data science tools to deliver targeted viewing recommendations within its customer user interface. Netflix researchers claim the analytics behind its recommendation engine has decreased customer churn by several percentage points, which increases the lifetime value of existing subscribers, producing $1 billion a year in value from customer retention.
Equally impressive are Amazon’s point-of-sale recommendations. The analytics embedded in their customer portal, such as ratings, reviews, and product suggestions, are almost unnoticeable. They’re presented right as the customer is about to make a purchase. When combined with Amazon’s one-click purchase functionality and efficient distribution network, these analytics have contributed to Amazon outpacing its competition in customer satisfaction for the last 7 years, according to the American Customer Satisfaction Index.
6. Increase Revenue
Embedded analytics can help you win more sales, retain customers and expand product offerings. BI vendor Sisense noted 4 distinct revenue opportunities from embedded analytics:
- Increased win rate: Adding analytics capabilities can “freshen up” existing applications, which can pique the interest of new users.
- Decreased churn rate: In addition to drawing in new users, analytics-enabled applications can secure existing users by providing new problem-solving abilities and demonstrating a commitment to improving the application.
- Expanded product licensing: Adding embedded analytics can help you “cast a wider net” to different types of users.
- Feature monetization: Compelling embedded analytics can provide new opportunities for existing customers to buy new, value-added functionality.
Wearable fitness device company FitBit has put those revenue opportunities into action. The device transfers user activity data to the cloud, which then feeds personalized analytics designed to optimize exercise performance over time, which helps decrease user churn.
FitBit then monetizes this user data by creating personalized benchmarks and fitness plans that can be sold to its user base. This strategic use of embedded analytics helped drive a $6.5 billion IPO market valuation in 2015, and today FitBit owns approximately 70% of the wearable fitness device market.
As you can see, embedded analytics presents your business with tremendous opportunities. Built on a solid foundation of data transformation tools and best practices, the thoughtful implementation of embedded analytics can help you change your corporate culture and set the stage for increased productivity, strengthened competitive positioning, and improved customer satisfaction – all of which will help you drive revenue and grow your business.