What is Embedded Analytics?
Embedded analytics software delivers real-time reporting, interactive data visualization and/or advanced analytics, including machine learning, directly into an enterprise business application. The data is managed by an analytics platform, and the visualizations and reports are placed directly within the application user interface (UI) to improve the context and usability of the data for business users.
What is Embedded Analytics used for?
Embedded analytics tools can be used in many different industries, allowing businesses to collect and analyze data for various purposes. The main usage of the embedded analytics is to provide that data and up-to-date business insights in the simplest way possible so that any user or application can use it and act on it.
When you integrate analytics into your business applications, you can achieve many benefits, such as:
- View, edit and create sales, KPI dashboards, and more directly from your app
- Provide dashboards in context with other features in your application
- Connect your different data sources for easy access
- Turn on and off features for your end users or customers
- Total control over the security and look and feel of the data in your app
- Customize actions based on your customers’ interaction with dashboards and visualizations
- Organizations are taking advantage of these benefits by integrating embedded analytics into their web pages, business applications, commercial software products, and external portals
What is the Difference Between Embedded Analytics
and Traditional BI?
In contrast to traditional BI, which requires users to leave their workflow applications to look at data insights in a separate set of tools, embedded analytics lets users view data visualizations or dashboards in context—while in the application itself. This immediacy makes embedded analytics much more intuitive and likely to be viewed by users.
In other words, when using traditional BI users are forced to switch between different apps to gain insights and take action, which results in frustration, waste time, and decreased efficiency. A December 2016 report from Nucleus Research found that using BI tools, which require toggling between applications, can take up as much as 1-2 hours of an employee’s time each week. Whereas, embedded analytics users use only one application, which saves time, increases productivity, and delivers better analytics experience.
Another difference between traditional BI and embedded analytics is that sometimes BI fails to deliver the intended value because it is not integrated with the user’s workflow. Due to that, it doesn’t give the context and the insights needed to act. On the other hand, embedded analytics helps the decision-making process by providing insights on a dashboard – users can use it to take immediate action and report at any time.
What is a Modern Embedded Analytics Platform?
Not all analytic products have been designed to be embedded. Many of today’s embedded analytics and BI vendors built their standalone applications first so they are not purposely designed to be embedded into applications.
Modern embedded analytics platforms don’t deliver a set of monolithic tools. Instead, they support a full stack of integrated analytic functions — from reporting and dashboards to self-service analytics, alerts, collaboration, data preparation and machine learning on a unified, scalable architecture with common administrative and management functions. And unlike more restricted analytics platforms of the past that limited what users could do, newer embedded platforms give end users the freedom to edit visualizations or dashboards or to create their own.
Also, they are designed from the ground up for the web, cloud and mobile delivery. Modern embedded analytics platforms also make it easier for developers to create custom analytic applications.
Benefits of embedded analytics in your software application
Embedded analytics tools offer many advantages to business. From seamless user experience and revenue growth to cultivating data-driven decision making, we gathered 3 fundamental benefits of using embedded analytics in your software application.
Seamless user experience
Users won’t waste time switching between apps, but instead focus on the value that the embedded analytics software provides. Having their answers and key insights right in front of them leads to increased productivity and increased customer satisfaction.
In our recent survey, we found out that one of the top motivators for developers to embrace embedded analytics is the increased customer satisfaction (36%), followed by the ability to make their app more visually appealing (23%), and gaining competitive advantage (22%).
As per another research published in AnalyticsWeek, of the 500 project managers, software developers, engineers, and executives surveyed, 96% said that embedded analytics contributes to their overall revenue growth, and 92% reported an increase in competitive differentiation.
On top of that, embedded analytics tools can provide additional revenue streams. Thanks to its huge value for the business, some of the features and functionalities could become extra and your sales team could upsell them to new and already existing customers.
Cultivate data-driven decision making
Embedded analytics provide insights to users, but on the other side it also provides user insights to your team. Presenting accurate and up-to-date data enables analytical thinking that could ultimately drive to innovative ideas and improved products.
In-context analytics enables your users to make better, faster decisions that are based on the information available at that moment or visible on the specific screen they are viewing. When people can better understand the impact of their decisions, they tend to feel more confident in making decisions.
What are the Key Elements of Embedded Analytics?
These are some of the key functions that are included with embedded analytics software:
Dashboards are embedded analytics tools that visually display data patterns for analysis, presentation and easy understanding. Dashboards can consist of pie graphs or charts, bar or line graphs, scatter plots, color-coded maps, or any other kind of visual data presentations.
Key features include:
- Drag-and-drop Capability
- Cloud-based Dashboard Software
- Responsive Design
- Data Blending Features
- Database Plug-ins
Drill-down takes a user from general overviews to more detailed analysis with a single click.
Modern embedded analytics software lets users connect seamlessly to many different data sources and then combine these data in one place for comprehensive analysis. Data sources may include Azure Synapse, Google BigQuery, Box, Sharepoint, Google Drive, One Drive, Microsoft Analysis Services, Microsoft SQL Server, CRM, and many more.
Some embedded analytics platforms also offer a feature called “in-memory data source”, which lets users directly connect to a data source that the software doesn’t support out of the box.
Visualizations refer to a range of chart types and the best embedded analytics solutions lets you choose from many pre-built templates. These range from column, doughnut, and funnel to bubble, scatter or sparkline charts, to more advanced ones such as tree map or geospatial mapping. Users can also combine these various visualizations to make a beautiful integrated dashboard.
What are Embedded Analytics Statistical Functions?
Wikipedia defines statistics as the study of the collection, analysis, interpretation, presentation, and organization of data. In terms of data analytics, this can include key statistical functions such as outliers detection, time series forecasting and linear regression, as well as the ability to embed these interactions into visualizations or allow features such as dashboard drill-downs and dashboard linking.
Outliers Detection lets users easily detect points in their data that are anomalies and differ from much of a data set. They can show or hide these outliers from view, so they’re always showing or so they don’t interfere with an analysis.
Time Series Forecasting
Using Time Series Forecasting, users can make predictions on future values based on historical data and trends. This is useful in any number of applications, such as sales and revenue forecasting, inventory management, and many others.
Linear Regression lets users visually see trends in their data by finding the relationship between two variables and seeing a linear approximation of the data – including future trends. Along with Linear Regression, other algorithm trend lines include Linear Fit, Quadratic Fit, Cubic Fit, Quartic Fit, Logarithmic Fit, Exponential Fit, Power Law Fit, Simple Average, Exponential Average, Modified Average, Cumulative Average, and Weighted Average.
Embedded analytics – Build vs Buy
Organizations that consider investing in embedded analytics software have two options: either to build their own data analytics platform or buy and embed an existing solution into their product. Depending on your business needs, resources, and budget there are pros and cons to both decisions.
Why build embedded analytics
Building your own embedded analytics solution might be the right option for those who could afford a higher budget and the human resource to do that. It gives you total control over the software, as well as it has more options for customization.
Also, building your own embedded analytics platform allows you to solve any security problems internally. This is important and might be a great bonus to those who are working with sensitive data that requires higher security levels.
Pros of building your own embedded analytics:
- Total control
- Greater customization options
Cons of building your own embedded analytics:
- It’s more expensive
- You need a dedicated team of specialists to build it
- You need to keep up with industry standards
Why buy embedded analytics
Most organizations decide to buy an already established embedded analytics solution to integrate with their existing software. One of the top reasons why this is the preferred option is that buying it instead of building it saves you both time and money. It also allows you to free up your developers’ resources and time so that they can focus on your core competency and what your business was initially designed for.
Pros of buying embedded analytics software:
- Cost predictability
- Higher ROI
- Faster time to market
- Reduced maintenance costs and resources
- Maximum security guarantees
- No need to worry about keeping up with industry standards
Cons of buying embedded analytics software:
Depending on the solution provider, you may be limited to basic dashboards and reports, as well as have limited customization options.
How to choose embedded analytics vendor
So, you have decided to accelerate your time to market and bring the power of analytics into your application. Now comes the question of which vendor do you partner with? Here are the top 7 questions you should consider when contemplating your embedded analytics vendor:
1. Is the application experience consistent on every platform?
Consistency across all platforms should be the goal of every application. Make sure that your vendor offer includes:
- Beautiful visualizations and dashboards across every platform and device size.
- Dashboards that are intuitive to view, edit, and share.
- User interactions that are instinctual on all visualizations and dashboards in the app.
2. Is the solution built on modern architecture?
Look for native SDKs that utilize the specific features of each platform, robust APIs for dashboard rendering, dashboard creation, deep linking in dashboards and custom UI for data source acquisition & modern API design with multi-channel distribution capabilities.
3. Was the solution purpose-built for embedded?
Most embedded BI and analytics vendors started out building a web or desktop-based dashboard tool. In time, many of these vendors decided to mold their standalone app option – into being an embedded offering. The problem – creating an amazing embedded experience is hard. Keep this in mind when talking to your embedded analytics vendor:
- Was the embedded experience an after-thought? Or was it designed from the beginning?
- Does the embedded user get the full app experience? Can the user go beyond simply viewing dashboards, and be able to edit existing dashboards and add new ones as well?
- Do you see limitations in the embedded product when compared with the SaaS or desktop offering?
4. Does it offer a great mobile experience?
When considering an embedded analytics vendor, a mobile SDK is more than just a “checkbox” on a requirements sheet. Ask your vendor the following questions:
- Are the mobile SDKs native per-platform, with the expected UX and app experience for Android, iOS or Windows?
- Did the vendor simply “wrap” a responsive web site? Is their mobile SDK simply a web page in an HTML iFrame linked in your app?
- Do you get the entire app experience in the mobile SDK, including dashboard creation, editing and sharing?
5. Is pricing 100% transparent?
You want to know upfront what your costs will be and be confident that they won’t escalate as your app sales increase. Ask your vendor what the cloud usage and per-user fees are & are there other fees you should be aware of? Also, don’t forget to ask if the solution can be deployed to Windows or Linux containers for local or cloud-based hosting.
6. Is localization handled seamlessly through your app?
Your apps are used globally, in many regions and countries. Delivering culture- and language-specific deployments are important. It’s also critical to have developer guides and documentation in native languages for your global development teams. Ask your embedded BI vendor the following:
- Does the application automatically handle locale-specific rendering like date/time and numbers?
- Does each SDK give me the ability to insert message strings based on region and locale?
- Is the documentation translated to different languages so my development teams can successfully use the SDK?
7. Is the product roadmap public?
Having a public, up-to-date roadmap gives you the peace of mind and assurance that your vendor is working just as hard as you are to deliver value to your customers. Look for the following:
- Is a public roadmap prominently posted?
- Is the roadmap up to date?
- Do they show success in delivery on previous roadmap commitments?
Examples of Embedded Analytics
A recent 2019 survey report produced by Infragistics found that the most popular applications that development teams were either actively embedding analytics into or were planning to do so soon are shown in the graphic below:
Some leading vertical sectors include:
Financial – Allows users at financial companies to aggregate vast volumes of data about borrowers for benchmarking and to better assess risk through intuitive visual dashboards that can be sliced, diced, and explored to granular levels.
Healthcare – Hospitals, doctors groups, and other healthcare groups use embedded analytics to Improve performance by delivering data-based quality care. They’ve been able to:
- Reduce patient wait-times by measuring and leveraging scheduling and staffing procedures.
- Improve patient satisfaction and quality of care by streamlining tedious processes related to making appointments, processing insurance, and providing referrals.
- Provide patients with more personalized treatment and improve overall patient experience.
- Reduce readmission rates by leveraging population health data against personal patient data to predict at-risk patient.
Manufacturing – A plant floor manager is responsible for a manufacturing plant’s entire production process, from when raw materials enter the plant to when the product exits the plant for distribution. The plant manager could use data analytics to see operational KPIs related to how the plant is performing, such as:
- Am I meeting forecasted production? (their most pressing need)
- Where are my production line inefficiencies? With people, processes, or machines?
- What do I need to change to get back on track?
- How do I reduce or eliminate production line downtime?
Social Media – Native metrics from social media platforms give you limited information, that is why most of these networks use embedded analytics to help them see their campaigns from a broader perspective. Emebedded analytics tools help them to predict campaign performance, suggest content recommendations, recommend best time for publishing based on time zones, offer various paid ads analytics, and more.
Your Facebook, for example, always knows what you have bought online and uses this data to up-sell related items each time you log in into the app.
Transportation & shipping – Allows transportation managers to track the path of an order: order fulfillment, shipping, and delivery tracking, and if there are any issues that require attention and action. Furthermore, transportation and shipping companies use embedded analytics for their business to increase the productivity of their workers and so that warehouse managers could keep track of key indicators such as average time or shipping and preparing the order.
Grocers – In the grocery industry, data analysis is key to discovering important insights related to sales, inventory, customers, and operations. When in place, the data analysis can forecast future sales and calculate efficient inventory policies to optimize stock levels. It can optimize pricing strategies and improve customer service. As you can see, whether you produce an app or a service, and regardless of in which industry your business lies in, embedded analytics comes with tremendous opportunities to expand your business, add value for your customers, increase