Tag Archives: Embedded
Five Design Tips to Deliver a Seamless Embedded Analytics Experience
In Managing Up, we give product managers and their teams actionable guidance on how to build, test, and release programs that will delight users and stand the test of time. From development to execution, our industry experts and seasoned pros can help you work smarter, not harder.
Delivering additional value to your customers by providing data-driven insights is a sure-shot way to improve your competitive advantage and monetize your data. It’s not a question of if but when this functionality becomes basic table stakes.
Several steps are required to turn data into revenue and go even further — including creating the right data strategy and identifying data opportunities, building your profit and loss statement and use cases, getting buy-in, finding a partner, implementing and launching, and finally growing post-launch through an iterative innovation process.
Zooming in on design and implementation: Delivering value means combining the right features, KPIs that drive action, and visualizations that tell a compelling story.
In addition, there is an important but oft-overlooked aspect to successful data monetization: user interface (UI) design (or visual design). While UI design is a vast topic, a common challenge when delivering analytics to customers is to create a consistent and seamless experience, since you are embedding a third-party application in your product.
Simple UI is good UI
Consistent design is intuitive design. It should be immediately obvious to the user how to accomplish whatever they want to do. Expectations of users today are higher, so you also need a beautiful design that intuitively directs a user to take action.
From a branding standpoint, it should also always be clear to the user that the product is unified: Typography, logo, image styles, brand color schemes, etc., should be reflected across the application (in the core offering and the analytics), just like the rest of the brand’s properties.
When embedding analytics, you are bringing two different applications into one space, so it is critical that your end-user seamlessly experiences both the core product and analytics.
Let’s take a deeper look.
Tip 1: Maintain consistent branding elements
Your brand matters! Consistent visuals resonate more effectively with your audience.
This is no different when you are delivering analytics to your customer. Remove any trace of third-party applications by changing any external logo or text to align to your organization’s brand. Leveraging a drag-and-drop UI-based interface to change the settings makes this process faster and will allow you to get to market rapidly without worrying about custom development.
Tip 2: Consider visual hierarchy
Colors can convey powerful messages, so it’s crucial they be used mindfully and consistently. It can be unharmonious to have your brand colors be teal and black, for example, and part of your application or the analytics you deliver be yellow and white.
Leverage your corporate brand guidelines, if they exist, to customize the analytic application’s primary brand colors, primary and secondary text colors, etc.
If brand guidelines don’t exist, use easily available color palette selectors (there are tons of options to choose from) to select a color palette. Build your palette with a primary color in mind (usually your main brand color). Most products that are well designed use a visual hierarchy of fonts and color palettes to help ignite the experience.
In the palette, make sure to have a couple of options:
- Primary brand color
- Highlight or accent color for banners or toolbars
- At least two light and two dark colors for contrast between background and texts (dark background, light texts or vice versa)
Once you have the colors, you can add them to “favorites” and make it easier to pick and choose.
In addition to the application colors, also make sure to change the visualization color palette to match the application brand color palette. You can either use one of the several out-of-the-box color palette options or create a custom visualization palette. Again, you can customize your palette to match your brand colors.
Tip 3: Build branding with typography
Typography is a crucial element that uplifts a design and gives it a personality. Typography is also an important element that should be part of your brand. Typography conveys personality and grabs a user’s attention while establishing the tone of your brand. Certain fonts can also improve scannability, legibility, readability, and even navigation. It’s important that the font or fonts you use are consistent throughout your digital experiences.
Tip 4: Minimize the steps to insights
It’s a fundamental tenet of UI/UX design that the fewer clicks a given action takes, the better. When embedding analytics, be sure to keep actions like filtering, exporting, etc., in one place in the host application so users don’t have to repeat the actions twice.
Think like a user: Would you want to first filter on the host application and then repeat the action again in the embedded analytics? Of course not. A seamless user experience (UX) means that when you do something once, it affects the entire application without you having to repeat steps.
Explore how you can implement these experiences and get inspired in the Sisense Embedded Playground.
Tip 5: Focus on ease of implementation
While good UI design makes a difference, it’s also important to be mindful of the cost, effort, and complexities of implementing seamless and consistent experiences.
Customizations that require developer skills and coding take considerable effort and increase time to market. These are also difficult to maintain over time. For example, upgrades can become a hassle due to backward compatibility issues. Changes to underlying infrastructure can break the code, requiring complex updates.
To avoid such hassles, find an analytics solution that makes rebranding and customization quick and easy. Using a solution that allows you to implement your changes via drag-and-drop, out-of-the-box capabilities that are always up to date with newly released versions helps you avoid some of these larger issues down the line. It also allows you to focus your efforts on data insights and application functionality instead of worrying about code, maintenance, or changes that can break your code.
Delivering standalone analytics solutions
Whether you choose to deliver analytics as a standalone product or service, or you embed them into your application, your analytics product should be an extension of your brand and visual story, and all the tips above remain true.
By white-labeling and customizing the look and feel of your analytic application, you deliver a consistent and great user experience! Sisense is equipped to allow you to put analytics where you need them and make them look and work the way you want. Whether you are creating your own solution from scratch or using ours, keeping these design tenets in mind will help you build an awesome product.
Shruthi Panicker is a Sr. Technical Product Marketing Manager with Sisense. She focuses on how Sisense can be leveraged to build successful embedded analytics solutions covering Sisense’s embedding and customization capabilities, developer experience initiative and cloud-native architecture. She holds a BS in Computer Science as well as an MBA and has over a decade of experience in the technology world.
Improve Your Product Differentiation with Embedded Analytics
In Managing Up, we give product managers and their teams actionable guidance on how to build, test, and release programs that will delight users and stand the test of time. From development to execution, our industry experts and seasoned pros can help you work smarter not harder.
Marketers are constantly communicating to consumers the advantages of their brand’s products—whether faster, cleaner, stronger, or cheaper. Having your product or service stand out in the consumer’s mind is the goal of a product differentiation strategy. If your products are not interchangeable commodities, such as sugar or soybeans, emphasizing the unique qualities of what you’re selling should result in less competition and more “stickiness” with your customers. This is where product teams come in: creating value for customers with unique selling points (USPs) that aid in product differentiation.
Horizontal vs. vertical product differentiation
One of the earliest product differentiation definitions came from the American economist Edward Chamberlain, who noted in his 1933 work, The Theory of Monopolistic Competition, that certain product features are more important than others to some consumers. Later, author and academic Michael Porter stated in the July 1980 issue of the Financial Analysts Journal that when a product is not a commodity, product differentiation “creates layers of insulation against competitive warfare because buyers have preferences and loyalties to particular sellers.” In other words, having a differentiation strategy makes business sense.
There are two ways that you can differentiate your products: horizontally—i.e., within the product line—and vertically, against external competitors.
Horizontal product differentiation is when a product stands out based on subjective characteristics. For example, there is no implicit benefit to having a blue Ford Mustang rather than a red one. The choice is purely subjective and often contained within the brand’s product line.
But while the color of the vehicle is subject to consumer preference, there are objective differences that a car company can highlight to set their vehicles apart from the competition. This is known as vertical product differentiation.
In a 1986 article for the American Economic Review, economist John Sutton noted that, with respect to vertical product differentiation, “if two, distinct products are offered at the same price, then all consumers prefer the same one (the higher-quality product).” However, the reality is that improved quality often results in an increased cost to the consumer. And the willingness of a subset of customers to pay for an objective increase in quality is what drives a company to vertically differentiate their products. So for sports cars, vertical product differentiation examples would include:
- Increasing horsepower
- Decreasing time from 0 to 60 mph
- Reducing carbon dioxide emissions
- Implementing environmentally sound manufacturing practices
- Manufacturing the vehicle in a specific location (e.g. “Made in America”)
In nearly all sectors, different attributes can sway different subsets of consumers. For example, one consumer may put the greatest weight on the environmental footprint of their vehicle (both in terms of emissions and manufacturing processes), while another individual may just have a “need for speed.” The key for product managers and product marketers is to target the unique qualities that consumers are willing to pay a premium for and emphasize those differentiators in the marketplace. That’s where an embedded analytics solution can help.
Embedded analytics: Driving product differentiation
For software businesses, analytics is often the differentiator between your application and the competition’s. The software-as-a-service (SaaS) market has exploded with the advent of cloud computing and the market has become crowded. Having embedded analytics built into your platform is one way to stand out.
Case in point with global supply-chain giant CTSI-Global. Their small analytics team helps provide insights to their clients and empowers end-users to build customized dashboards. Certainly, CTSI-Global would subscribe to the belief that every company is becoming a data company, and Sisense has been key in that transformation.
“The almost boundless agility of Sisense, combined with an efficient process framework, let us deliver and organically scale a market-differentiating product,” said Todd Winton, Development Manager at CTSI-Global in a recent case study.
For another example, look at Erea Consulting, a company that has solved legacy inefficiencies in retail data sharing between suppliers and retailers by delivering real-time insights. The information has improved suppliers’ resource allocation and promotional efforts, helping their products to fly off the shelves. Retailers can sell back data insights to suppliers, and thanks to Erea’s implementation of Sisense, some retailers have quadrupled their revenue from data.
Learning and growing with embedded analytics
Analytics aren’t just a way for Product Teams to set their apps apart from the competition in a crowded market, they’re also a key way builders can learn about their target audiences and users.
When browsing a brand’s website or using an app, prospects and current users always leave a digital breadcrumb trail that can yield valuable insights. Historically, the data in that trail would have to be pulled from a database and recorded in a spreadsheet to derive actionable insights. Not only was this time consuming, it was also prone to error, often leaving product teams without a single source of truth. But a data and analytics platform, such as Sisense, allows product managers to make data-driven decisions without having to wrangle the data themselves because all the information is in a unified view.
Those product-level decisions can relate to anything from color to product bundles that encourage a shopper to make the purchase. BraunAbility, the market leader in wheelchair accessible vehicles and commercial lifts, is one company that has used embedded analytics to better understand customer preferences and to ultimately increase profit margins across the board. Reports that once took weeks for analysts to derive are now available on demand, helping the company with horizontal product differentiation that’s driving results.
Setting your product apart with embedded analytics, easily
Maybe you’re a technology company looking to stand out from the competition by delivering actionable insights from your customer’s data. Soon, product managers will be confronted by the age-old decision: build versus buy. But, the decision should be an easy one.
Attempting to develop a homegrown embedded analytics solution within your technology may not be your best move, since this means taking your development resources away from their core duties building your product. Instead of building your own embedded analytics, partnering with a provider like Sisense can help you save time and resources and see ROI faster. Then engineers and product leaders at Sisense live and breathe business intelligence and have developed a high-quality, embedded analytics solution that’s been included in the 2020 Gartner Magic Quadrant for analytics and business intelligence platforms.
Teaming up with a partner like Sisense makes adding embedded analytics to your product super-simple. The Sisense end-to-end BI platform empowers your builders — those with natural curiosity, a penchant for asking deep questions, and a passion for serving up deep insights to users — to create analytic apps that deliver highly interactive user experiences and powerful data. Watch the demo now and find out why Gartner has labeled Sisense as a visionary in its Magic Quadrant for analytics and business intelligence platforms.
Eitan Sofer is a seasoned Sisenser, having spent the last 13 years building and shaping our core analytics product, focusing on user experience and platform engineering. Today, he runs the Embedded Analytics product line which powers thousands of customers and businesses, making them insights-driven. Eitan is also an avid music fan and surfer.
How Embedded Analytics Help Tame Supply Chain Volatility
When it comes to managing your supply chain, volatility and uncertainty have become a fact of life. From Brexit to U.S./China trade tensions to weathering unpredictability in the age of climate change – it’s understandable if your supply chain planners complain that it’s next to impossible to properly prepare for what’s coming next.
To help meet the challenge of volatility, leading organizations are focusing in on three key pillars for a better supply chain strategy:
- Total visibility: In a globalized digital economy, supply chain visibility requires you to connect digitally to your supply chain partners. Today, many organizations connect only to their top suppliers. However, to achieve total supply chain visibility, you’ll need to connect to 100% of your partners, and, if possible, your partners’ partners. While suppliers of materials used in production are important, you shouldn’t stop there. A comprehensive approach to supply chain visibility means connecting with distributors, logistics service providers, end customers, and even assets by using Internet of Things (IoT) technology.
- Business sustainability: Maintaining sustainable business growth goes hand-in-hand with environmental sustainability. Less resource usage and waste, after all, means greater efficiency and lower costs. Leading organizations are revamping processes to hit their sustainability targets. Direct-to-consumer models can cut down on transport while building strong customer relationships. 3D printing can locate production closer to the consumer – reducing emissions in transportation, eliminating unnecessary inventory, and minimizing waste. Predictive maintenance can improve the performance, reduce the carbon footprint, and extend the life of equipment. These are just a few examples.
- Customer-centricity: Putting customers first means putting them at the center of your supply chain processes. The objective is to stay on top of changes in demand, offer up goods and services that meet this demand, and then orchestrate the entire supply chain to deliver the goods when, how, and where the customer wants them.
Built-in insight
Across all these areas, embedded analytics can play a critical role in helping your organization deal more effectively with the challenge of supply chain volatility. Think of embedded analytics as insight built directly into your business applications and processes, instantly accessible to users in their daily activities.
While working in your transactional system, users can access in-the-moment insight and decision-making support based on a mix of live and historical data, both structured and unstructured. But how exactly do embedded analytics help your organization in the areas of total supply chain visibility, long-term business sustainability, and improved customer-centricity? Let’s have a look.
Embedded visibility
Starting with the visibility question, let’s say a typhoon hits your manufacturing plant in Southeast Asia and takes out a month’s worth of product, severely impacting your ability to meet customer demand. As everyone in your supply chain scrambles to ramp up new production, embedded analytics can assess the ability of potential partners to deliver on time – based on current data regarding available capacity mixed with previous performance data regarding on-time delivery under tight deadlines. This can help you choose the right supply partner to recover from disaster and meet customer expectations.
Embedded sustainability
What about business sustainability? Let’s say you’re an asset manufacturer – a maker of HVAC systems that heat and cool factories. If you fashion these systems with sensors that feed out asset health and emissions data – and then evaluate this data in real time with embedded analytics – you have the power to completely transform maintenance processes.
Now you can move to predictive maintenance with abilities to see what’s coming next. Now you can act proactively to prevent downtime. You can even transform your entire business model – for instance, providing temperature-controlled facilities as a service.
If you consider taking the service-provider approach, you now have the power to reimagine your relationship with your customers. As a service provider, your company becomes a partner in your customer’s business success, with the potential to build a relationship that’s solid, long-term, and even more profitable.
Add to this the benefits for environmental sustainability, such as greater machine efficiency – leading to less waste, optimized processes, and reduced carbon emissions. On the sustainability front, embedded analytics delivers the goods.
Embedded customer-centricity
Embedded analytics can also help drive customer-centric processes. In the supply chain planning stages, it can be used to detect demand signals from point-of-sales systems, social media, and other sources. At the point of purchase, it can be used to offer tailored pricing based on supply and demand dynamics, order timing, competitor pricing, or past purchase patterns.
Once the order is placed, embedded analytics can be used again to provide an accurate estimate of the delivery time based on current supply chain metrics. If bottlenecks occur along the way, you can set your system to provide automated delivery updates. And if you encounter further snags in the last mile of delivery, your connectedness to logistics partners across the network can help you optimize delivery routes based on real-time traffic conditions. The result is on-time delivery and happy customers.
What to look for
If your organization is looking to transform its supply chain, look for approaches that bring analytics as close as possible to real-time operational processes. More than ever, users need insight on demand, supply, and environmental events in real time. Embedded analytics can provide that insight and even streamline processes by automating decision-making. Ultimately, you’ll have the understanding you need to successfully navigate an increasingly volatile world.
For more information, download the March 2019 Forrester Consulting opportunity snapshot, “Optimize Business Intelligence Efforts With Embedded, Application-Driven Analytics,” to examine the challenges, best practices, and recommended actions that can help your business turn your supply chain into an intelligent, competitive force.
Follow me at @howellsrichard
And please join our “Pathways to the Intelligent Enterprise” Webinar Tuesday, June 11, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling. Register here.
Power BI Embedded session at Power Platform Summit Europe: What’s new in Power BI Embedded
Join the Power BI Embedded team at the Power Platform Summit Europe for a 2-hour session. During the session, we will cover the newest features and capabilities in Power BI Embedded such as:
- Control all visual menu actions programmatically – Options and Context Menus APIs has been extended to provide full control for each visual in the report on built-in commands, and custom commands. Built-in commands can be hidden, or grayed-out per visual and the position of custom commands in the menus can be controlled as well. Users can try the new ‘Insight to action’ showcase in the Microsoft Power BI Embedded Playgroundto experience the new feature!
- Personalize reports with Themes API – users can apply a custom theme to their embedded report, such as corporate colors, seasonal coloring, or other custom styles. The custom theme can be defined using a JSON file like in the Power BI Service, and be applied when the report is loaded or changed in a loaded report. Users can try the new ‘Personalize report design’ showcase in the Microsoft Power BI Embedded Playgroundto experience the new feature and get the code to implement it.
- Best practices for multi-tenancy with Power BI Embedded analytics – many of our customers build SaaS applications that manage multiple customers (tenants). When integrating Power BI embedded analytics into their SaaS application, they must carefully choose the tenancy model that best fits their needs. A tenancy model determines how each tenant’s data is mapped and managed within Power BI and within the storage account. The choice of tenancy model impacts application design and management. We can help users choose the best model for their needs and their customers, and weigh the different options across several important evaluation criteria.
What will users take away from this two hours Power Series session at Power Summit?
Users will experience how in few easy steps, they can embed Power BI visuals in web portals and applications for their internal organization or external customers and allow them to adopt decades of analytics expertise in minutes. They will also learn about our newest features and capabilities and will get an opportunity to look into our road map moving forward.
Learn more about these new features and connect with Microsoft staff onsite in Amsterdam at Power Platform Summit Europe. Register today!
How an embedded PowerApp enables faster sales quoting
Creating sales opportunity in Dynamics 365 which involve multiple products can prove repetitive and time-consuming.
That’s because default controls force users to select one product at a time. For busy sales professionals creating multi-line quotes this is a drawn-out process.
Thanks to the Microsoft Power Platform, flexible solutions have emerged to save time and create better user experiences.
To enhance this workflow, a canvas PowerApp is embedded with a model-driven opportunity form. This provides a new interface enabling users to quickly build opportunities by adding multiple items.
In this scenario, a sales rep works for a company supplying horse trailers. A new opportunity needs to be tracked for the supply of a trailer and this will be customized with a series of add-ons.
To quickly complete this, an embedded PowerApp enables multiple items to be instantly added.
Building Multi-Product Opportunities
First, we’ll create a new opportunity record and reference the appropriate Price List.
Next, we’ll switch to the Opportunity Product PowerApp tab. After selecting an option to add multiple products, an option set lists products in the price list.
From this view, users can scroll through to select and add multiple products.
For organisations working with complex product catalogues, a search dialog can still be used to quickly find the relevant items.
In this example, we’ll search for the base trailer that the selected add-on’s will be applied to. Typing ‘trailer’ filters the list so the user can immediately select and add the correct product.
The list now consists of 6 items representing the base trailer and add-ons. As shown below, individual items can easily be removed by clicking the bin icon.
To progress, we’ll select Create All.
Individual product lines are then customized to update volumes, pricing and discounts by selecting the appropriate item.
In this instance, we’ll increase the quantity of feed buckets that will be supplied. Upon clicking ‘Save’ the current opportunity total is updated. Individual products can be deleted if these are no longer needed.
Once product quantities and other variables are confirmed, users can switch to the regular model-driven interface.
This shows the completed detail enabling them to progress the opportunity and create a quote.
If you are looking to significantly reduce time and effort in configuring opportunities and sales quotes, to find out more about this embedded PowerApp.
About Preact
is a UK based Microsoft Gold Partner implementing and supporting customer engagement solutions for SME’s since 1993.
for more information about Microsoft Dynamics 365 and CRM.
How to Empower Healthcare Providers with Embedded Analytics
21st-century trends in analytics and BI point to the growing importance of big data across industries. And with access to rapidly expanding quantities of data on patient care and physician performance stored by medical software, the healthcare industry has much to gain from the adoption of embedded analytics solutions in that software. Medical software providers, it follows, are well poised to contribute to a revolution in healthcare analytics that empowers healthcare providers, tangibly impacts the quality of patient care, and provides opportunities to monetize vast amounts of unwieldy data transformed into actionable insights by a powerful BI solution.
The Healthcare Industry’s Duty: Data-Driven Insights For All
As a Product Developer and BI expert at GeriMedica, a multidisciplinary electronic medical record (EMR) servicing the elderly care market, I would argue that meaningful (and ultimately successful) embedded analytics solutions are not executed first and foremost for financial gain. They are executed first and foremost in the spirit of providing insights that will empower your customers—healthcare providers—to impact the lives of their people—their patients.
For medical software providers, in particular, I believe it is our responsibility to embark on embedded analytics projects not in pursuit of a higher-value product but in pursuit of a new philosophy on how end-users should look at data, glean insights, and use those insights to serve their patients in the best way possible. Lead with your heart, and the money will follow.
The following nine-step framework details the steps a mindful medical Saas provider must follow to ensure their embedded solution is first impactful—and then, ultimately, profitable.
The Problem Space
1. Define a clear customer segment
A solution for everyone is a solution for no one. Before creating a BI solution, Gerimedica had to decide who our solution was meant to empower. We knew that office managers, administrators, and financial departments weren’t lacking in access to BI insights. Those suffering from a true BI blindspot were the doctors, nurses, and physiotherapists—the generators of the data. They spent their days generating data in our system, but they weren’t able to glean insights from all that data they generated on a day-to-day basis.
We wanted to empower the caregivers to make decisions based on insights. And so our clear customer segment was defined.
2. Identify the real problems at hand
Prior to embedding a BI solution, Gerimedica’s software solution gave caregivers the ability to create care paths based on common use cases and select a care path for each patient based on their diagnosis. These care paths defined the treatment types and number of treatment hours associated with each diagnosis and allowed caregivers to track each patient’s progress in the system.
The problem? Without analytics, caregivers weren’t able to identify trends in the success of these care paths or view their patients and common care paths on an aggregated level. What was the success rate of each care path? Was there a care path with a low rate of success that needed to be reevaluated? If end-users could access this data, they could tangibly impact a patient’s level of care and personalize it according to clearly visible trends.
And so we defined the real problem we sought to solve with our BI solution.
3. Validate your assumptions—talk talk talk!
Throughout the five-month implementation process, I learned more by speaking to an end-user for two-minutes than I ever could have learned by conducting research behind my computer screen.
Once our customer segment and problem were defined, it was time to validate our assumptions on the ground. And our validation process drove home the fact that we were on the right track. We learned that most of the caregivers on our accounts gleaned insights by exporting reports out of our system and trying to filter them manually to learn something. Because it was so labor intensive, nothing was updated as frequently as you would expect in the healthcare field—which meant no one had access to real-time data.
Users were inundated by access to 400 different dashboards, a result of the fact that they lacked the ability to drill down and filter dashboards to find the insights they needed. A new dashboard was created for every new query, and then promptly forgotten in a sea of hundreds of other dashboards. And creating each new dashboard? That required an external data consultant on-site.
Problem: validated.
4. Numbers are your friend
Were the pains real enough that our customers would be willing to pay for a better solution? Only after defining our customer segment, identifying the problem, and validating our assumptions was it time to think about the financials.
We needed to make sure that our solution was a better and more affordable solution than the current patchwork solutions. And that meant we needed to estimate the size of the problem to help contextualize it and price our solution accordingly.
By continuing our on-the-ground reconnaissance, we realized all that manual labor was costing our customers money—because every hour that an employee spends not doing his primary work is money lost. When we had our customers break it down, we realized that they were spending roughly 15-20K per year on patchwork BI solutions. With this research behind us, we knew that our solution was cheaper and could also solve the problem of misused labor.
We knew we were on the right track.
The Solution Space
5. Brainstorm
With the knowledge that we had identified a problem and a solution with a positive business case, it was time to brainstorm what our actual embedded analytics solution would look like. And it was important to us not to fall into the “different colored apple” trap—a solution that mimicked the functionality of other BI solutions offered by our competitors. We wanted to offer something truly unique in its functionality, that solved problems in a way that none of our competitors were currently offering.
A thorough brainstorming session with Gerimedica team members and market experts was essential to ensuring that our solution was something different, while still incorporating all elements requested by our end-users.
6.The one-minute pitch
Just as important as our embedded analytics solution was how to convey the value of our solution to our end-users in a meaningful, compelling way.
The pitch: Our solution is for care treatment teams in the elderly care sphere that are dissatisfied with their lack of insights. Our solution improves productivity and treatment quality by bringing insights to all aspects of care. These insights are available with one click of a button—and you don’t have to undergo extensive training on how to use the dashboards to gain insights yourself. You are skilled in giving care—not analyzing data day in and day out. This solution brings the insights to you.
7. Create an MVP (Minimum Viable Product)
It’s critical to understand that the validation process isn’t a one-time event. We validated our assumptions in step three, but now it was time to validate our solution. We started by creating and embedding a few test dashboards that provided high-priority insights within our software environment as a demo and took it to our end-users.
Because it’s one thing to pitch a solution. It’s another thing entirely to demonstrate it’s value in real time. And the response was amazing—customers wanted to buy it on the spot! Before doubling down on the solution, we wanted to show our customers that the solution we created was the right solution. By prioritizing this step in the framework, our customers knew that a revolutionary BI solution embedded in a software environment they knew and loved was incoming.
8. Co-creation of dashboards through beta-testing
Co-creation of dashboards allowed us to make sure we were answering the questions that our end-users needed. So we enlisted six organizations that would pilot the new solution for free—all we wanted in return was a commitment: show up and give good feedback.
During this step, we held workshops to discern the requirements we needed to include from key stakeholders. At each workshop, we’d end up with 60 post-its (business requirements) and eventually filtered that down to 20 post-its. These 20 post-its were translated into widgets within our dashboards.
The biggest takeaway from this step? We realized that 90% of the post-its we saw in these workshops were the same for every organization. This helped in marketing and selling our embedded analytics solution to new customers because our software could come inbuilt with dashboards that already answered 90% of their business questions.
9. Show me the money
“If you can show me the business case—it’s already too late.” – Bill Gates
When our embedded analytics solution was ready to roll out, we didn’t want to waste time deciding on a licensing structure—it was our pilot users who helped us determine the value and the data monetization strategies and licensing structures that would work in the market for our future customers.
It was important to Gerimedica that we didn’t deliver a stand-alone product or service, but a philosophical mindset shift around data that created a user community around data visualization and insights—built straight into our software offering. And this decision sparked one of my favorite parts of our final BI solution—today, our customers get designer accounts so they can create their own dashboards or tweak the base dashboards that come with the product.
We created a rich environment filled with documentation and training materials that empower end-users to get their own business insights as well as a Dashboard Marketplace so that every organization doesn’t have to start from scratch.
And this is how our original goal of improving healthcare and empowering caregivers came full circle: our users can (and actively do!) share dashboards between organizations, empowering each other to provide the best possible care regardless of their organization—based on a shared philosophy of insights for all.
About the Author
Hamza Jap-Tjong is CEO & Co-Founder of GeriMedica Inzicht, a GeriMedica subsidiary. GeriMedica is a SaaS company focused on delivering the best software and service for healthcare professionals operating in the elderly care sector.
3 Major Embedded Analytics Trends for 2019
Over the past several years, embedded analytics has been increasingly popular with organizations looking to digitally transform their business. But while embedded analytics may be the future, it is by no means new. Vendors have been around for nearly a decade and home-grown solutions existed even before that. Now a mature solution with evolved functionality, embedded analytics, is a key to turning insights into action.
From front-end user experience to back-end development to data science implementation, there’s a lot you can do with embedded analytics. But not everyone starts with all these functions built out, it’s important to understand each one’s purpose before deciding if it’s right for your business.
Here’s a brief overview of the three major trends in embedded analytics and how they can benefit your business in 2019.
1. Data as a Feature
Data as a feature really just means treating data as a core component of your application. Having the data built seamlessly into your application with a design that delivers the data users need in an insightful way.
This trend has largely been driven by demands from users. They want the data to be just as intuitive and easy-to-understand as the rest of the application experience.
In order for applications to be competitive today, user experience around the data must be a top concern for software designers.
2. Embedded Business Intelligence (BI) + Data Science
Embedded BI and data science are actually very complementary fields. One of the main challenges of data science has always been how to operationalize the findings that data science gives you.
How do you get the insights into the hands of business users that can then leverage them for better decision making?
Embedded BI + Data Science allows you to take the outputs from your data science models and distribute them as reports and visualizations that are embedded into the applications your users interface with on a regular basis.
3. Embedded BI Meets Modern Cloud Architectures
In a world of increasingly complex cloud architectures, how should you deploy embedded analytics?
As BI and analytics are being more readily embedded into applications, application architectures are also moving away from monoliths to decoupled, lightweight services or microservices.
The days of embedding heavyweight, monolithic BI applications into these new, distributed architectures are numbered. Look for BI solution providers to increasingly offer small, specialized BI services that can be purchased and deployed a la carte.
Watch this on-demand webinar to further explore embedded analytics and major trends that enterprises and software developers should be aware of in 2019.
Embedded Analytics: The Build vs Buy Debate is Pointless
As embedded analytics become increasingly prominent in the business intelligence (BI) landscape, the question of whether companies should build or buy embedded BI applications seems to be more relevant than ever. The numerous attempts to answer this question ignore the basic fact that the question itself is misleading since for most organizations there is not a simple yes-or-no answer. Instead, best practices for embedded analytics are neither “build” nor “buy” — but is, in fact, more akin to partnership.
Understanding the Question
“Embedded analytics” is a blanket term that describes the integration of various features of business intelligence tools into other applications (often, but not exclusively, in SaaS). For example, a company that develops CRM software might want to provide more in-depth insights from the data it collects to either enhance the company’s general value proposition or to sell a premium service. Hence it may look to incorporate features such as data transformation, rapid big data querying or interactive visualizations to its own CRM software package.
Most professionals in the BI industry would agree that embedded reporting has become a major area of focus for both business and technology. Customers are demanding self-service, meaningful access to data, and competition is forcing companies to accommodate these demands, which in turn leads to more focus on building these types of capabilities.
In-House or Out-of-the-Box
The question of “to build or not to build” has become the subject of heated discussions when considering an embedded analytics project. Run a quick Google search for “build vs buy embedded analytics,” and you’ll be bombarded with page after page of articles asking and attempting to answer this exact question. I will briefly present the most common arguments for each side of the debate:
Developing BI features in-house gives companies more flexibility and control over the end product. The original application developer is the most intimately familiar with its product and customers, and so will be able to tailor a solution more precisely. Building BI features in-house, however, requires a significant investment and often yields sub-par results due to the level of investment required and the need for specialized skills.
Buying an “out-of-the-box” solution enables a company to leverage the massive investments already made by the BI provider and gives access to state-of-the-art BI capabilities.
In a majority of cases, companies that seek to provide meaningful data analysis capabilities to their customers would be better off looking to embed an existing product rather than starting from scratch. However, what I would like to stress is that the way this question is posed is in itself misleading: by far, the more common — and preferable — scenario is actually neither build nor buy, but a third solution that could more accurately be described as partnership.
Business Intelligence is Not a Commodity Product (Yet)
When people talk about “build vs buy,” one might get the impression that the option exists to go online and buy a turnkey embedded BI solution, which one can easily plug into an existing product and presto! Instant customer-facing analytics. Sadly, when it comes to more sophisticated needs and products, this is almost never the case.
I do not mean to imply that BI implementations need to be lengthy or difficult affairs, but merely that each implementation is different. A company that typically wants to present a hundred thousand rows of data to its customers does not need the same technological “muscle” as one that works with a hundred million rows; likewise, data that comes from dozens of structured and unstructured sources is quite different than neatly-organized tables in a SQL database. High-level data visualization is one thing (for example, an e-commerce app that displays traffic and sales to sellers), whereas advanced analytics, drill-downs, and customizable reports require entirely different capabilities.
When it comes to these types of more advanced use cases, the notion of a one-size-fits-all solution is unrealistic: the analytical features will need to be integrated into the existing application and customized to meet the exact needs of the specific product and customer base in terms of data modeling, security, management and reporting. Again, this is not to say that these integration efforts need to be overly complicated or require extensive development resources — however, they will require an understanding of the underlying data, and the ability to easily customize and communicate with the BI platform via API access.
Partnership, Not a One-Time Transaction
The decision to use an external provider for embedding analytics is more similar to a partnership than to a “get it and forget it” type of purchase. The developer and the BI provider work together to build the required data product, and continue to collaborate as products mature, new features are added and new needs arise.
Does this mean that the developer will have to rely on the BI provider for every change or customization? Absolutely not — developers should have complete independence and control over their own product. They should be the sole owner of the product, from end to end, and be able to develop it on their own, without having to rely on a vendor’s professional services or external consultants. In order to achieve such an outcome, developers should partner with a BI vendor that is an enabler, always keeping developers in mind. Best practices include maintenance of a comprehensive SDK, with excellent documentation, and designing the BI product as an open platform.
Open platforms enable easy access via commonly used APIs, ensuring the BI software is flexible enough to integrate with the developers’ existing systems seamlessly, and accommodating specific needs and requirements around data sources, security and similar considerations. And for the truly complex, heavyweight implementations — top BI vendors provide the professional resources needed to get customers up and running as fast as possible and to address the various maintenance issues that inevitably arise.
Furthermore, both parties should see their relationship as long term — new features introduced in the BI platform should always be built in an “API-first” approach, enabling application developers to quickly and easily incorporate these features into their own offering; communication between the BI vendor and the application developer needs to be open and frequent so that both can gain a better understanding of the other’s strengths and limitations and adjust development, support and account management efforts accordingly.
Understanding embedded analytics as an ongoing partnership, rather than a one-off purchase, will lead developers to ask more relevant questions before embarking on an embedded BI project; and lead BI providers to make a serious commitment to building truly open platforms, maintaining superb customer service and documentation. In such cases, everyone stands to benefit.
Embedded Intelligence with Email Engagement in Dynamics 365
Microsoft launched many exciting features as a part of Embedded Intelligence in Dynamics 365 Customer Engagement. Embedded Intelligence has key capabilities– Relationship Insight, Auto Capture, and Email Engagement, which enables you to track email statistics and engage customer more timely and effectively.
In this blog, we are going to uncover one of the key capabilities of Embedded Intelligence – Email Engagement. Email Engagement enables Dynamics 365 users to keep track of the various statistics about the email messages goes out of Dynamics 365 such as:
1. How many time email messages opened.
2. Follow if attachments are opened or not.
3. Number of time links in email clicked.
4. Replies to the email.
Along with these statistics and a chronological view of different action performed by recipients, you can also set reminders to follow up with an email and set ‘Send Later’ to match the recipient’s time zone or working hours.
How to enable Email Engagement in Dynamics 365
1. Navigate to Dynamics 365 > Settings > Administration > Intelligence Configuration.
2. Under Sales Insight > Accept (Step #3).
3. The System will navigate away from the D365 page for authentication and permission to access your profile. (Step #4)
4. Come back to Email Engagement and Enable it. (Step #5)
Note: One should have Dynamics 365 administrator and Office 365 Administrator permissions to install it. Email Engagement navigation shown in this blow is as per Dynamics 365 v9.0.2.
There are few pre-requisites before enabling Email Engagement:
1. OneDrive for Business must be available to your Dynamics 365 tenant if you want to enable the ability for users to follow email attachments.
2. To use followed email attachments, you must also enable document management for email in Dynamics 365 instance to follow email attachments.
How to use Email Engagement in Dynamics 365
Navigate to the new email form, and you will notice a new section for Email Engagement is added into the lower right corner with 3 options – Don’t Follow, Send Later, Set Reminder.
Follow Emails
This option will let you know when a recipient opened an email, clicked embedded links, opened attachments, or send a reply. By default, Dynamics 365 will follow all the new emails created within Dynamics 365. You can explicitly opt out to not follow any specific emails by clicking Don’t Follow.
Users can also disable follow option permanently for a specific contact by updating its contact preferences from Follow Email to Do Not Allow.
Follow an Attachment
To track an attachment, add the attachment to the email and then click on the Follow button from the attachment window.
Once the attachment is enabled for tracking, it will show up as an attachment in email body as well.
Track Recent Activities
Once you send out an email with tracking enabled, Dynamics 365 will track who opened, along with the time and the device on which it was opened. It also tracks and shows attachment views, link clicks, and replies sent back to this email. Users can directly navigate to replies from this view:
Send Later
You can create an email at your convenience and schedule it for delivery based on recipient’s time zone or working hours, so it’s more likely to be read or responded to. Once you click on the Send Later option and specify the date and time, the system will send out an email automatically on that date and time.
Set Reminder
Now you can set reminder if a customer hasn’t opened your email or responded back to you. Alternatively, you can also set a reminder to just follow up with a specific recipient.
Reminder Options:
1. If I do not receive a reply by
2. If the email is not opened by
3. Remind me anyways at
Note that once an email is sent out, Delay Send and Reminder can be removed/disabled, however Follow can’t be removed once an email goes out of the system.
Email Engagement is available now in Dynamics 365 Online, version 8.2 onwards. Please refer to Configure and enable embedded intelligence to get more details on how to configure embedded intelligence.
Note: By enabling this feature, you consent to share data about your customers’ email activity with an external system. Data imported from external systems into Dynamics 365 are subject to our privacy statement.
Learn more about Microsoft Dynamics 365 by reading our blog!
Happy Dynamics 365’ing!