A strong reporting and analytics offering could take your SaaS product to the next level. By giving your customers more insights into their data or product usage, they can make more informed decisions quickly. But just adding a few tabular reports or data exports won't always solve your customer's challenges.
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In this article, you'll learn about the top embedded BI features to make your customer-facing reporting have maximum impact! But first...
Embedded business intelligence (BI), also called embedded analytics, is the process of adding BI capabilities to web-based apps, SaaS products, or other business applications. It presents all sorts of metrics and data insights in a visual, interactive way to end-users, so they can make better informed decisions.
Developing those analytics capabilities in-house can take up a lot of time and engineering resources. Instead, you can also add BI to existing applications using an embedded analytics platform.
Adding embedded analytics to your software application offering comes with many benefits for your product users.
Besides end-users, internal product and development teams will also benefit from embedded analytics, rather than building custom dashboards from scratch.
If you’re looking to add analytics capabilities to your software, there are literally hundreds of functionalities you can add. So, getting started can be a little overwhelming. If you’re aiming for an excellent user experience, here are some of the top capabilities to look out for.
A staggering 88% of online consumers won’t return to a website if they’ve had a bad user experience. An intuitive, attractive user interface sets great SaaS apps apart, and your analytics should be nothing but a reinforcement of that.
Nothing is more annoying than landing on a website on your that only displays half of the content. 83% find it important to have a seamless experience across devices. So make sure your charts and graphs are optimized for all screen sizes.
Most BI software is responsive by now (and we advise to immediately skip the ones that aren’t!), but some are more adaptable than others. Luzmo, for example, lets you create different dashboard versions for each screen size. You can show a condensed version on mobile, or different chart types on a monitor, to guarantee the best UX on each device.
If your SaaS product operates globally, data analytics can get very complex, very fast. You’re not only dealing with users who speak different languages, but also use different currencies and timezones.
Let’s say you have a CRM platform, and you’re visualizing new revenue and pipeline for your sales team. You don’t want to show revenue in EUR to American users, nor would a European company be happy to see their pipeline value in British pounds.
And what if a sale happens on the 31st of December at 10 p.m. GMT? Would it count towards the new year’s target if your customer is located in Japan?
Great embedded analytics are aware of an end-user’s locale. They automatically serve users in the right language, the right currency, and the right time zones.
Besides localizing dashboards and getting real-time insights, some users may not find the same metrics as relevant as other users do. You may want to show different KPIs and dashboards depending on the user who is logged in.
If scalability is important to you, choose an analytics software vendor that offers multi-tenancy. With multi-tenant analytics, you can load a different dashboard template for each user profile.
For example, let’s say you have a customer success software. While the Head of Customer Success will be interested in global NPS and satisfaction metrics, a support rep may want more insights into open support tickets and resolution times.
You can adapt your data visualizations for each of those business users.
Most of your product users are probably trying to solve similar problems, whether it’s boosting revenue for sales users of a CRM, or increasing conversions for an marketing platform. However, they also have their own set of specific problems, for which they’ll need to dive deeper into their data.
With interactive reports, your product users will be able to delve into specific insights they need to see. You can add filters and let users drill down into a specific time period, or even into an entirely new dashboard with more detailed information.
If you want to go even further, some embedded analytics tools - like Luzmo - let you trigger actions in your product directly from a dashboard via powerful APIs. For example in an marketing tool, a user could filter in a dashboard to find the most engaged subscribers, and use that selection to directly trigger a new campaign in the product.
The key is to bring data visualizations and reports as close to a user’s normal workflows as possible. That way, data-driven decision-making becomes a natural part of their business processes, and they will naturally get more value out of your SaaS product.
Personalizing dashboards and adding interactive filters is already a great first step. But sometimes, filtering alone isn’t enough for your users, and you may get flooded with reporting requests that just don’t make sense at scale. So how do you handle those?
The answer is self-service BI. There are tons of easy-to-use BI tools like Klipfolio or Looker Studio that make it super easy to drag and drop a dashboard together. But unfortunately, most of these tools are stand-alone, directing users outside of your product which does not exactly streamline your operations.
By embedding a drag-and-drop dashboard editor into your SaaS product, you can make self-service BI part of your offering. Users can make their own edits or explore their data by building a dashboard from scratch. All from within your own software, which will boost your product engagement metrics.
Most analytics dashboards show insights into historical data or in real-time. However, thanks to the rise in AI and machine learning, more and more data visualization tools are also adding more advanced analytics capabilities, like predictive analytics.
You can find them in the form of forecasts with trend lines, for example, a line chart that shows how your revenue is expected to grow based on previous quarters.
You can use this in healthcare, for example. By getting access to historical and training model data, you can predict whether a patient will have issues in the future.
Another example is clustering, where the tool identifies patterns in big data, and then clusters it into groups with similar traits. For example, marketing tools could use this to cluster buyer personas with similar traits to enable more targeted campaigns.
Nowadays, developers and product builders can whitelabel a plethora of tools and integrate them tightly into their SaaS products. That way, they can focus all their engineering staff on improving their core application, without compromising on other value-adding features.
You can integrate payment services with Stripe, communication with Intercom, or onboarding experiences with Userpilot, and it’s no different for analytics.
Luzmo, for example, offers full platform white-labeling when you embed their analytics capabilities into your SaaS product. Colors, fonts, logos, border styles, alert branding,... Even the loading spinners of a dashboard can be customized to your brand’s look and feel.
The way you store your customer data will affect how you load it into a customer-facing dashboard. You may be using a data warehouse, or more complex data modeling that involves separate databases for certain customers.
In either case, you need to make sure that you can easily load that data into usable datasets in your embedded BI solution of choice.
What’s more, it needs to be easy to display the right data to the right product user. Your best bet is an embedded analytics solution that supports the data sources you need, or lets you easily connect them via API and set up user access rules.
If you’re a product manager or a developer, this should be one of your main concerns when looking for a new BI/embedded analytics tool.
Generative AI is everywhere nowadays, and it’s starting to make its introduction into business intelligence too. The first “chatbots” are popping up where you can ask for insights about a specific topic or metric, which then automatically generates a dashboard based on the prompt.
Tools like ChatGPT can be a huge help if you don’t know yet which data to visualize, or if you need help finding interesting patterns in your data.
By hooking up generative AI to your dashboard, the possibilities for automation are endless. This AI dashboard builder demo is just one of many more examples to follow in the near future.
Building useful reporting with a great user experience will become much easier if you’ve got the right BI tool to support you.
Contact us to discuss your requirements of embedded module. Our experienced sales team can help you identify the options that best suit your needs.
While traditional BI tools like Microsoft Power BI, Tableau or Sisense are perfect for internal reporting use cases, they often leave something to be desired when you use them embedded in your platform.
If you’re looking for a tool that was built with embedding in mind, look no further than Luzmo. Their embedded analytics solution, purpose-built for SaaS companies, integrates seamlessly into any SaaS or web app with just a few lines of code.
With Luzmo, you can add these powerful analytics features - and more! - to your SaaS product in a matter of days, not months.
An embedded system is a combination of computer hardware and software designed for a specific function. Embedded systems might also function within a larger system. These systems can be programmable or have a fixed functionality. Embedded systems are used today to control numerous devices. For example, they're used in industrial machines, consumer electronics, agricultural and processing industry devices, automobiles, medical devices, cameras, digital watches, household appliances, airplanes, vending machines, toys and mobile devices.
Embedded systems typically contain a microprocessor -- or a microcontroller-based system, memory and input/output (I/O) devices, all of which share a dedicated function within a larger system. While embedded systems are computing systems, they can range from having no user interface (UI) -- for example, on devices designed to perform a single task -- to complex graphical user interfaces (GUIs), such as in mobile devices. UIs can include buttons, light-emitting diodes (LEDs) and touchscreen sensing. Some systems use remote user interfaces as well.
According to Global Markets Insight, the embedded systems market was valued at $110.3 billion in and is predicted to grow to more than $190 billion by . Chip manufacturers for embedded systems include many well-known technology companies, such as Apple, IBM, Intel and Texas Instruments. The expected growth is partially due to the continued investment in artificial intelligence (AI), mobile computing and the need for chips designed for high-level processing.
Embedded systems are used in a wide range of technologies across an array of industries. Some examples include the following:
Embedded systems always function as part of a complete device. They're low-cost, low-power consuming, small computers that are embedded in other mechanical or electrical systems. Generally, they comprise a processor, power supply, and memory and communication ports. Embedded systems use the communication ports to transmit data between the processor and peripheral devices -- often, other embedded systems -- using a communication protocol. The processor interprets this data with the help of minimal software stored in the memory. The software is usually highly specific to the function that the embedded system serves.
The processor might be a microprocessor or microcontroller. Microcontrollers are simply microprocessors with peripheral interfaces and integrated memory included. Microprocessors use separate integrated circuits for memory and peripherals instead of including them on the chip. Both can be used, but microprocessors typically require more support circuitry than microcontrollers because they're less integrated into the microprocessor. The term system-on-a-chip (SoC) is often used. SoCs typically include multiple processors and interfaces on one chip. They're often used for high-volume embedded systems. Some examples of SoC types are the application-specific integrated circuit (ASIC) and the field-programmable gate array (FPGA).
Often, embedded systems are used in real-time operating environments and use a real-time operating system (RTOS) to communicate with the hardware. Near-real-time approaches are suitable at higher levels of chip capability, defined by designers who have increasingly decided the systems are generally fast enough and the tasks tolerant of slight variations in reaction. In these instances, stripped-down versions of the Linux OS are commonly deployed, although other OSes have been pared down to run on embedded systems, including Embedded Java and Microsoft Windows IoT -- formerly Microsoft Windows Embedded.
Embedded system designers often also use compilers, assemblers and debuggers to develop embedded system software.
The main characteristic of embedded systems is that they're task-specific. They often include the following additional characteristics:
Embedded systems vary in complexity but, generally, consist of the following three main elements:
In terms of hardware, a basic embedded system consists of the following elements:
The sensor reads external inputs, the converters make that input readable to the processor, and the processor turns that information into useful output for the embedded system.
Embedded system types differ in their functional requirements. They include the following:
Embedded systems can also be categorized by the following performance requirements:
There are several common embedded system software architectures, which become necessary as embedded systems grow and become more complex in scale. These include the following:
Very large-scale integration (VLSI) describes the complexity of an integrated circuit (IC). VLSI is the process of embedding hundreds of thousands of transistors into a chip, whereas large-scale integration (LSI) microchips contain thousands of transistors, medium-scale integration (MSI) contains hundreds of transistors, and small-scale integration (SSI) contains tens of transistors. Ultra-large-scale integration (ULSI) refers to placing millions of transistors on a chip.
VLSI circuits are a common feature of embedded systems. Many ICs in embedded systems are VLSIs, and the use of the VLSI acronym has largely fallen out of favor.
Embedded systems differ from the OSes and development environments of other larger-scale computers in how they handle debugging. Usually, developers working with desktop environments can run both the code being worked on and separate debugger applications that can monitor the embedded system that programmers generally can't.
Some programming languages run on microcontrollers with enough efficiency that rudimentary interactive debugging is available directly on the chip. In addition, processors often have CPU debuggers that can be controlled and, thus, control program execution via the JTAG industry standard or similar debugging port.
In many instances, however, programmers need tools that attach a separate debugging system to the target system via a serial or other port. In this scenario, the programmer can see the source code on the screen of a general-purpose computer, just as they would in the debugging of software on a desktop computer.
A separate, frequently used approach is to run software on a PC that emulates the physical chip in software. This essentially makes it possible to debug the performance of the software as if it were running on an actual physical chip.
A simple way to debug embedded applications is to use a general-purpose I/O pin. This verifies that a specific line of code in an application is being executed.
Another basic debugging tool is a source-level debugger, which enables users to walk through their code, pause and check program memory or variables.
Logic analyzers are another common and useful debugging tool. They can read waveforms from multiple signals at a time, while also being able to decode that data from various standard interfaces.
Broadly speaking, embedded systems have received more attention to testing and debugging because numerous devices using embedded controls are designed for situations where safety and reliability are top priorities.
Embedded systems date back to the s. Charles Stark Draper developed an integrated circuit in to reduce the size and weight of the Apollo Guidance Computer, the digital system installed on the Apollo Command Module and Lunar Module. The first computer to use integrated circuits, it helped astronauts collect real-time flight data.
In , Autonetics, now a part of Boeing, developed the D-17B, the computer used in the Minuteman I missile guidance system. It's widely recognized as the first mass-produced embedded system. When the Minuteman II went into production in , the D-17B was replaced with the NS-17 missile guidance system, known for its concentrated use of integrated circuits. In , the first embedded system for a vehicle was released; the Volkswagen used a microprocessor to control its electronic fuel injection system.
By the late s and early s, the price of integrated circuits dropped and usage surged. The first microcontroller was developed by Texas Instruments in . The TMS series, which became commercially available in , contained a 4-bit processor, read-only memory and random-access memory, or RAM, and it initially cost around $2 each in bulk orders.
Also, in , Intel released what's widely recognized as the first commercially available processor, the . The 4-bit microprocessor was designed for use in calculators and small electronics, though it required external memory and support chips. The 8-bit Intel , released in , had 16 KB of memory; the Intel followed in with 64 KB of memory. The 's successor, the x86 series, was released in and is still largely in use today.
In , the first embedded OS, the real-time VxWorks, was released by Wind River, followed by Microsoft's Windows Embedded CE in . By the late s, the first embedded Linux products began to appear. Today, Linux is used in almost all embedded devices.
Throughout the s and s, processing power increased due to the transition from 8- and 16-bit microcontrollers to 32- and 64-bit processors.
The s saw an increased focus on security features in embedded devices, possibly driven by the rise of IoT and connected devices.
Today, due to technological advancements, embedded systems have also begun to integrate with AI and machine learning (ML) systems. Also called embedded AI, this is the integration of AI into resource-limited devices such as smartphones or autonomous vehicles.
While some embedded systems can be relatively simple, others are becoming more complex and can either supplant human decision-making or offer capabilities beyond what a human could provide. For instance, some aviation systems, including those used in drones, can integrate sensor data and act upon that information faster than a human could, permitting new kinds of operating features.
The embedded system is expected to continue growing rapidly, driven in large part by IoT. Expanding IoT applications, such as wearables, drones, smart homes, smart buildings, video surveillance, three-dimensional printers and smart transportation, are expected to fuel embedded system growth.
Other embedded system trends include the following:
Embedded systems perform specific tasks efficiently and reliably in almost any modern device. Learn more about how embedded systems work together with IoT devices.
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