What is embedded analytics? And how it works

Embedded analytics, at its core, is a technology that makes data analysis and business intelligence more accessible to any application or end-user. This guide dives into all things embedded so that you'll walk away empowered with the insights you need to make the most of your data.

What is Embedded Analytics?

What is embedded analytics? embedded analytics is the integration of analytic content and capabilities within applications, such as business process applications (e.g. CRM, ERP, EHR/EMR) or portals (e.g. intranets or extranets). Incorporating relevant data and analytics inside applications helps users work smarter and more efficiently by solving high-value business problems.

As we mentioned, and perhaps more importantly, embedded analytics is a tool that makes data accessible to non-technical users. 

But how, you may ask, is embedded analytics different from traditional BI?

Embedded Analytics vs. Traditional BI

Business intelligence (BI) is a set of independent systems (technologies, processes, people, etc.) that aggregate data from multiple sources, prepare the data for analysis, and then provide reporting and analysis on that data from a central viewpoint.

BI is best suited for situations that aim to support management-level decision-making (which tend to require an aggregated view of information from across a department, function, or entire organization). These systems are specifically designed for people whose primary, or even sole, responsibility is often to perform data analyses.

Embedded analytics, on the other hand, is a set of capabilities that are tightly integrated into existing applications (like your CRM, ERP, financial systems, and/or information portals) that bring additional awareness, context, or analytic capabilities to support decision-making related to a variety of tasks. These tasks may require data from multiple systems or aggregated views, but the output is more than just a centralized overview of information. Rather, it's targeted information that supports decisions or actions in the context in which they take place.

Comparatively, traditional business intelligence can be thought of as needing to extract insights from data within the silo of data analysis.

For decades, BI and analytics tools have failed to be adopted by more than 25% of an organization. And even within that 25%, most people only use the tools once or twice a week (and don't tend to get much value from them). By inserting charts, dashboards, and entire authoring and administrative environments inside other applications, embedded analytics dramatically increases BI adoption.

The catch? Most business users don’t know they’re “using BI”— it’s just part of the application they already use. 

Let's use an analogy: 

The business intelligence tool is like a map that we use to plan the itinerary before a long road trip. Embedded analytics is the GPS navigation inside your car that guides your way in real-time! So, turn up the volume, and enjoy that view.

More specifically, BI software isn't integrated into your software or platform's experience; these tools require navigation between separate interfaces to view reports, which means users have to go back and forth between multiple windows. 

BI solutions give you an overview of data coming from disparate systems, but fail to offer an easily digestible context. Insert embedded analytics, which makes it possible, and easy, to process data from multiple sources and integrate contextualized reports right where you need them. 

With the evolution of in-app analytics and business’ responses to it, there has been a fundamental shift in the way we consume data. Visualizations used to be comprised of static pictures and charts that presented a snapshot of a single point in time, making insights quickly irrelevant. Now,  analytics, interactive visualizations, and  real-time reports are increasingly necessary to turn the massive volume of data into compelling stories that are actually valuable, which is why Forrester accurately predicted that embedded analytics would become the new normal (due to these increases in operational complexity).

Types of embedded analytics

Embedded analytics strives to join insight and action by integrating analytics deeper and deeper within business applications and workflows. The embedded analytics come in several architectural formats: 

Standalone analytics. Generally speaking, standalone BI and/or data visualization tools are built to create custom views of multiple data sources and to help you look at other relevant data sources together (or at least within a wider context). Standalone analytics also allow IT and software developers to embed analytics into their applications, enabling them to mash and merge data in order to answer complex business questions. The resulting visualizations can then be embedded into applications to make it easier for users to actually get the insights they need.

Detached analytics. This is a “diet” version of embedding where the host and analytics run separately but are tightly linked via URLs. The detached analytics approach works well when multiple applications use the same analytics environment, portal, or service. There is no commonality between the host application and analytics tool, except for a shared URL and shared data. The two applications might also share a common authentication mechanism to facilitate single sign-on (SSO).

Inline analytics. With inline analytics, the analytics tool becomes part of the host application. It looks, feels, and acts as the host. but runs as a separate element (or module) within it. There might also be a separate tab where customers can view analytics about their activity within the application. In most cases, the embedded components sit within an iFrame, which is an HTML container that runs inside a webpage.

Fused analytics. Fused analytics deliver the tightest level of integration with a host application. Here, the analytics (e.g., a chart, table, or graphical component) sit side by side with the host application components and communicate bi-directionally with them. Fused analytics are facilitated by JavaScript libraries that control front-end displays and REST API calls that activate server functions. 

Finding the right solution

Evaluation Criteria

Picking the right solution involves thoroughly evaluating the technology, understanding the expertise offered by the vendor, and implementing a process to ensure success.

Let’s examine the evaluation criteria that are critical to strong embedded analytics implementations. 

• Scalability: While the solution you choose will connect to your current data environment and meet your data security needs, it should also be flexible enough to meet future demands as your data evolves.
  
  
• Integration: One of the major ways embedded analytics initiatives differ from standalone analytics projects is the need to integrate with the application environment. This means providing a white-labeled solution to meet evolving business requirements.
  
  
• Deployment: Since time-to-value is so critical to the success of an analytics project, having a development environment where you can create, style, embed, deploy, and iterate on embedded analytics will enable your team to deliver the functionalities that users want.
  
  
• Customer Support: Choosing the right partner is not simply about the technology; it’s also about finding the level of expertise you require for training, support, and services, as well as aligned business terms that ensure shared success. Use a trusted third party as a guide. 
  
  

Evaluating an Embedded Analytics Solution

Now that we’ve established some of the criteria for evaluating embedded analytics vendors, let’s take a look at a process that can help you make the best decision for your business.

• Determine your goals
  Know exactly what you want the embedded analytics solution for. Who will use it? How will it help you? And why do you need a solution right now? Use a balance of quantifiable metrics like revenue, adoption rate, customer retention, etc., as well as soft metrics like user experience, competitive differentiation, customer satisfaction, etc. to reach clarity. 
  
  
• Establish your timeline
  Identify the steps you’ll take to reach your goals. Ask yourself, “When do I want to…”
       Begin the selection process?
       Have detailed vendor presentations and demos?
       Finish a proof of concept?
       Make my final decision?
       Start development?
       Release the product?
  
  

• Assemble your team
  Determine the stakeholders who need to be involved. Who cares most about embedded analytics internally (your IT team, product management, and the executive team) and externally (your key customers)? It's important to build your business case collectively so that everyone is on board before moving forward.  
  
  
• Identify your requirements
  Review your technical and non-technical requirements, and rank and weigh them. Researching your competitors and speaking with your customers will also help you develop a firm understanding of the analytics capabilities that are most important for you to add to your application. Understanding how end users will utilize your products can inform how you determine your requirements. Make sure you also consider who will use the third-party products internally. Understand their skill sets and identify any potential resource gaps as you move into the evaluation
  
  
• Research potential vendors.
  Utilize independent industry resources like  G2 Crowd for Business Intelligence and Analytics Platforms report. There, you can find potential vendors, check peer reviews, and gain a deeper understanding of the ecosystem. Pay special attention to vendors who specialize in the OEM market for software providers.
  
  
  
  You can schedule product demonstrations with an initial list of vendors to confirm a basic fit, during which time it makes sense to discuss your requirements and ask each vendor to demonstrate how they would deliver your specific processes and scenarios. Be ruthless! Ask tough questions and make sure the vendors show you the functionalities they promise.
  Don’t forget to enquire about pricing, or at the very least, get a ballpark estimate. Stick to your priorities and do not get dazzled by unnecessary features or shiny embellishments the vendors may try to distract you with. Evaluate each vendor’s ability to help you achieve success during the implementation process (through access to best practices, community, consulting, support, and training).
  
  

• Refocus on your goals
  Embedded analytics is far more than just pretty pictures. During your evaluation process, it can be easy to get lost among a dizzying array of charts and graphs. Don’t forget what we've discussed in this guide. Ultimately, you want to bring value to your application and your users through:
  
  
  • Embeddability, which is based on how tightly you plan to integrate analytics into the overall user experience, the existing application, and the workflow.
    
    
  • Customization, which speaks to your ability to white-label and control the look and feel of the application so that you can make it your own. You should be able to tailor functionality so every user has access to the capabilities that they alone may need.
    
    
  • Scalability, with the aim of having ultimate flexibility to create a unique application experience so your product stands out. Make sure you can also future-proof your solution so you can meet your users' evolving needs and desires.
    
    
• Complete technical evaluations
  After you have your initial  product demos, narrow down your list to the top two or three vendors that meet your needs so that you can begin a more structured evaluation process with each one. After understanding and evaluating all required features, it becomes about sticking to the timeline that you have in mind.
  Now, you can dive deeper into the product(s) with assisted trials where support is generally available if you run into issues. In structured evaluation, you and the vendors are building a proof of concept together which should be implemented in a technical environment that is as close to the production environment as possible. 
  In these technical evaluations, connect the embedded analytics solution to your data sources, integrate it with your security, and embed it into your application. If you host a SaaS application in the cloud, do not simply evaluate desktop tools or run analysis off a cleansed spreadsheet (unless that is what you expect your customers to do).
  At the end of the evaluation, present the output back to your stakeholders to get feedback and validate your direction.

• Select a vendor and get started
  Finally. Select the vendor you feel most confident in as a partner to reach your goals. Look beyond the software, for the vendor who gives you the best possible chance of success.
  Make sure the vendor you've selected has the necessary resources to help you, both now and in the future as your company grows. Later on, you’ll appreciate being able to test ideas and leverage best practices as your needs evolve.
  Get training for those who will be using the platform to create analytics. This will be easy if you have chosen an embedded anlytics oslution that offeres free lessonly courses like Toucan. Create your first set of reports. Work with your vendor’s enablement and consulting teams to understand and employ embedded analytics best practices. 
  
  
• Monitor, adapt and optimize
  There’s a lot that can be said here, given the endless possibilities that come from using embedded analytics. But here are a few general and useful tips:

1. Invest in the training you need to best utilize the embedded analytics solution.
2. After four to five months, touch base with your vendor and let them know how the solution is working for you. 
3. Suggest ideas for new features that you think will be helpful for you and others in your industry.

Why are companies embracing embedded analytics?

Today, Over 90% of software companies are embedding analytics tools in their applications, according to the State of Embedded Analytics Report. So why is embedded analytics getting so much attention in the software industry and business intelligence space? And, perhaps more importantly, how are businesses using embedded analytics? Let’s take a look: 

Benefits of Embedded Analytics

• Faster time to market

Dedicated analytics development requires lots of expertise, resources, and time. That's why it can be difficult to achieve speed, reliability, and scale with a homegrown solution.

By investing in a Software-as-a-Service (SaaS) embedded analytics solution, you no longer have the burden of maintaining infrastructure, storage, and computing power, so you can be confident your analytics solution will work at 5×, 10×, or even 100× users.

The right embedded analytics solution also positions you to go to market faster by drastically cutting down on your analytics development cycle. That is why Storyslab chose Toucan. 

StorySlab is a sales automation software that was troubled by clunky dashboards, unactionable data insights and poorly optimizes AWS inventions. They were looking for an analytics solution that could be up and running as soon as possible while meeting all the giving requirements. After extensive evaluations, Storyslab chose Toucan. Story slab had updated, easy-to-use visualizations and actionable data insights for business users within a month. 

"Empowered non-technical users to build robust, customizable dashboards that were fully integrated and live in <1 month "  

• Stand out in the marketplace

Businesses today want to differentiate their offerings and increase their product adoption rates, but the sad truth is that only a small fraction of users return to most applications after their first experiences with them.

One of the keys to more product sticky-ness is delivering insights to customers anywhere, anytime, in less than 3 steps. While traditional analytics solutions often require significant data wrangling, embedded analytics delivers on-demand insights within applications in one easy context. The ability to quickly and intuitively drill down into a report and cross-highlight it with other relevant data delivers significant value.

In fact, the difference in usage between embedded analytics and non-embedded solutions is significant: 43% of users leverage embedded analytics on a regular basis – double the adoption rate of traditional analytics. The trend is clear: the more accessible analytics are in your app, the more customers will adopt them. This was the case with Verified Market Research, VMR.

VMR is a market research and consulting company that was looking to differentiate their offering and increase customer retention. After embedding Toucan into their product, VMR saw a 10% increase in their sales closures as they were able to prove a stand-out analytics functionality that customers couldn’t. 

• Expanded revenue streams

An embedded analytics solution can impact your own business as much as it impacts your application. Embedded analytics often opens the door to new revenue streams and helps grow opportunity size. You can develop entirely new lines of business around embedded analytics services – such as white-labeled dashboards – as ways to expand customer loyalty and scale your business. More opportunities for upselling and cross-selling? Yes please. 

Deloite financial services is a great example of a company who was able to leverage the embedded analytics capabilities of Toucan to open up a completely new revenue stream. Check out how Deloitte leveraged exciting data and combines it with embedded analytics to create a new product.  

• Generate value, not just reports

Your application developers would, in an ideal world, be focused on creating new features and improving core functionality, not custom building an extensive analytics engine or coding reporting features that constantly need to be updated. With the right embedded analytics, you don’t have to start from scratch to meet customer demands. This puts time back into the hands of your developers to improve core features and functionalities, all while delivering a state-of-the-art analytics experience. 

The more an application can guide users through decisions by weaving massive sets of data into something meaningful, the more likely adoption and usage will increase. As a software vendor, you need a combination of powerful analytics and visualization capabilities in order to deliver on the promise of turning data into actionable insight. This was the case with Dataneo. 

Dataneo is a data valorizer for the automotive industry and ran into the issue where their reports were too complex for the business users to understand and use in an impactful manner. This is when they turned to embedded analytics and Toucan. With Toucan, Dataneo was able to not only increase adoption and recurring visits to their product as they now had reports that were understandable by non=tecnical users, but they also new revenue by empowering persons they couldn’t reach without embedded analytics. 

"Generated new revenues by addressing new personas and empowered their 150+ users with data"

Learn more about Dataneo’s journey

• Reap the rewards of others’ dedicated BI investments

As soon as a feature becomes “industry standard” and is noticeably lacking within an application, it creates a challenge for businesses trying to grow their user base. And what if you want to integrate world-class analytics into your application? By leveraging the dedicated R&D of an analytics vendor, you lift a significant development burden from the shoulders of your developers and product managers. Critical updates and new features are automatically integrated into your application, and you get to reap the rewards.

The additional value provided by a community of experts, support staff, and customer care shouldn’t be underestimated. Dedicated onboarding, with step-by-step explanations and refined e-learning, gets your teams working faster and helps build expertise in your business. Global user communities also often lead to fruitful partnerships, inspire new offerings, or just help you troubleshoot a tricky issue. 

• Empower customers with confidence 

From business executives and data scientists to casual users, providing actionable insights to anyone who uses your app keeps them coming back for more. This can be easily accomplished with the use of embedded analytics. At the end of the day, it’s all about driving success for your customers and that means giving them not only the tools they need but also the confidence that they are making the right decisions for their business. Today, every role is a data role and, with embedded analytics, your customers have actionable insights where and when they need them most. 

• The key to insight is embedded analytics

With ever-growing volumes of data and the increasing complexity of business management, having timely access to data insights is more important than ever. But customers can’t be expected to make informed decisions based on raw data alone. They must be equipped with the means to understand it. Business applications with easy-to-use analytics are the critical link between customers and the data they need. Simple charts don’t do justice to your application, or to the challenges your customers face. That's why application data must be accessible anywhere, and in a way that tells a story. By investing in a complete embedded analytics solution, you can make this sort of impactful data exploration easy. Free your customers from exporting, give them fewer steps to insight and create more powerful self-service capabilities.

• From cost centric to profit-centric with embedded analytics

Today, data volumes, sources, and reporting complexity have grown, and the potential overhead has skyrocketed. This leaves many businesses wondering how to best expand their analytics and visualization capabilities. Does a custom-built platform make the most sense for your business's unique needs, or will an off-the-shelf solution free you up to focus on what’s most important to you? Real-time, self-service, and freely explorable visualizations provide massive competitive differentiation, but developing a homegrown set of visuals to represent your data strains the time and resources of software vendors and developers. Technical and business decision-makers alike know that building analytics from the ground up comes with serious challenges, and you’ve probably said something like this yourself:

Mature, embedded analytics and BI solutions are available off the shelf, enabling you to leverage the dedicated R&D, infrastructure, and development effort of a vendor that has BI and data expertise. If you, like many, don’t have this core competency in-house, choosing to buy becomes the obvious option. By eliminating the burden of development, your business will be free to invest in capturing additional value for you, your developers, and your customers. 

So, how will you provide in-app analytics to your customers? Will you invest in building a custom solution in-house, or purchase an embedded analytics solution off the shelf?

build or buy?

When faced with the need to embed analytics into an application, most software providers arrive fall headfirst into the infamous “build versus buy” conundrum.

Why Build (or Really, Code)?

The first instinct many application developers have when it comes to analytics is to build the necessary reporting functionality with the help of code libraries or charting components. Amongst the many things that happen over time is that users ask for more functionality, more flexibility in their analysis, and more methods to gain insight without needing your help. Very few customers want to simply extract data into an excel file. Most want to be able to build custom dashboards and visualizations as they learn the product. With constantly increasing demand, it becomes difficult to build analytics in a scalable way.  

Application providers who stay on the “build” track are committing to allocating significant resources toward developing, supporting, and keeping up with advances in data visualizations and business intelligence over the long term.

Why Buy?

Many software organizations are under pressure from customers or competitors to improve analytics capabilities, but they simply don't have the time or resources to build a reasonably strong solution on their own. In fact, in virtually every survey conducted with software providers, the top reasons for embedding with a third-party product are:

• Cost to build and maintain capabilities on their own – it can be expensive to initially develop, provide ongoing support, and continually enhance analytics capabilities.
  
  
• Need to get to market faster – there is usually only a small window of time available to satisfy customers without having them churn, differentiate a product offering, and stand out in the marketplace.
  
  
• Desire to have internal resources focused on core application functionality – delivering functionality with a third party makes development teams more efficient and frees up resources for your core product.
   

We certainly recommend coming up with a cost-benefit analysis over time so you can come up with an expected ROI for both the buy and build options.

We've done some of the work for you. To start with, let's take an ROI formula:

ROI [%] = Benefit / Costs  -1 

• Benefits, in this case, are a combination of strategic benefits (e.g., revenue increase) and operational benefits (e.g., cost reduction).
  
  
• Costs, in this formula, would include any investments required to develop and maintain an embedded solution.
  
  
• “-1” assures that a positive ROI is achieved only when benefits exceed costs.
  
  

If it's tough to understand the exact benefits and costs that an embed solution would offer, here is an ROI calculator that can help you with the decision. 

So let's say, for arguments sake, You’ve decided to invest in embedded analytics. Now what? 

Set Goals and expectations

Determine “Need-to-Haves” vs “Nice-to-Haves” 

It is essential to distinguish between functionalities that are absolutely necessary for product launch and those that are beneficial but not required. As soon as possible, you can organize functionalities into these categories during the planning process so that the team can rally around completing the list of "need-to-haves" without getting bogged down in details of "nice-to-haves." Once this list is in place, you can refer to it whenever stakeholders suggest changes or corrections. 

Minimum Viable Product (MVP) 

A MVP is a simple concept: design the simplest, most basic product that will accomplish the objectives, and then iterate from there. This means that you will not need to devote a considerable amount of time to creating a "perfect" product before it is released for production. The MVP approach aims to get a functioning analytics product out and running as soon as possible in order to test its performance in the real world. Consider the MVP as your initial test platform for challenging your assumptions and ideas about your new product, and apply what you learn to the next version.

Define Workflows 

The next step is to map out specific workflows that each persona needs to do his or her job. To get started, follow these steps: 

1. Brainstorm the top 5 functions the persona performs on a regular basis, such as detecting fraud, providing personalized pricing, and identifying cross-selling opportunities. 
2. Give each of these functions a “story” title, such as “Sales Performance” or “ Employee Health.” 
3. For each story, build a simple 5-7 step flowchart that describes the steps involved using your analytics. For example, to review a team’s performance, the user logs in, sees a trend chart, clicks the chart to drill down on a problem area, decides which actions to take, etc.

Determine the Analytics Required 

The next step is to identify which analytics can fill the gaps that your personas are experiencing after defining the workflows they perform on a regular basis. Consider the workflow for each persona and identify insights that might be helpful. As an example, if the persona’s workflow is to identify cross-selling opportunities, an advanced analytics product that recommends next-action promotions may be of use. If the user has a workflow called “review product performance,” an advanced analytics product that shows revenue associated with each product, broken down by region, product line, and new users versus existing users and suggests pricing changes might be helpful. This step ensures that every analytics capability you put in place is directly tied back to the workflow of a specific persona.

Using Embedded Analytics for Success

Now that you've embedded analytics within your application, what comes next? 

Here are two of the many ways you can use embedded analytics to improve your product and revenue: 

• Promotion: Generate excitement from your customer and user communities about embedded analytics and the value it brings.
  
  
• Pricing and Packaging: For commercial application providers, create a shared value proposition for monetizing your embedded analytics offering.
  
  

This is all about bringing value to owners of internal IT applications who are looking to promote and build a user base for the embedded analytics rollout.

Embedded analytics is a journey. More often than not, once your customers see data in new and exciting ways, their thirst for more data and insights will only grow.

So be sure to monitor usage, actively acquire feedback from all stakeholders, and adapt your activities to optimize the success of your project over time. Listen to sales teams and prospects, as their feedback will inform future product development plans.

Promotion

Generate hype around the new features that were implemented to help your customers better visualize data and gain actionable insights from them. 

Equip internal stakeholders with the tools they need to successfully communicate compelling value to external customers. Think beyond sales enablement. Ensure that teams involved in training, professional services, and marketing understand the embedded analytics value proposition and know how to best utilize the assets available to them.

Pricing Embedded Analytics

Determining the value embedded analytics has for your customers is key to monetizing your offering.

Typically, you want to relate the value that your customer receives to the price you offer for the product. Get down to the numbers. Software providers revealed these key insights when it comes to embedded analytics:

The value of embedded analytics relative to the value of the application has increased by 20% in two years. 93% of commercial SaaS providers say embedded analytics has helped them increase revenue. They charge an additional 25% on top of their core product offering, up from 15% last year.

The value is increasing over time, so the minimum value and functionality customers expect from your application is increasing. For anyone still on the fence about committing to or investing in analytics, there is a real danger of being left behind.

When it comes to pricing structure, charging a percentage of the core product is not the only approach you should be considering. Analytics pricing can also be based on the number of users, overall system usage, or simply a fixed dollar amount. There are multiple factors that go into determining the pricing metrics, including how the pricing fits into your existing pricing structure.

Start your pricing exploration with the question, “what is the value of embedded analytics to our customers relative to the overall value of our application?”

There are three common pricing models for embedded analytics: all-inclusive, module, and tiered. Let's look at each one:

Pros: 

• You are clearly communicating the value of analytics in your product.
• Customers will feel like you are giving them more functionalities than they are paying for, increasing their lifetime value. 

Cons:

• Customers who do not use analytics may not want to be “paying” for it and may rather have your analytics be an opt-in service.
• Analyzing revenue that can be attributed to your analytics offering becomes more difficult. 
• There is no upsell opportunity.
• It doesn’t take into account the level of usage or number of users.

Pros: 

• Simple packaging model that customers can easily understand.
• You have a clear path to upsell customers. 
• Makes it easy to account for embedded analytics revenue.

Cons:

• Customers may feel nickel-and-dimed, especially your existing customers. 
• It becomes harder to manage customer expectations.

Pros: 

• You map capabilities to value and justify the pricing.
• There is a clear path to upsell.

Cons:

• Understanding the revenue impact is harder than module pricing but easier than all-inclusive.
• If the customer doesn't understand the value, they will feel like they are overpaying. One of the main things it comes down to is communication.

Keep in mind that how you package your analytics offering can be used as a competitive tool, as well. So be sure to take into account your own positioning in the marketplace. For example, if your competitors have some level of embedded analytics, customers will likely expect you to deliver that minimum capability at a similar (or lower) price. Another scenario could be that everyone in your industry charges for analytics separately and, to differentiate, you can decide to bundle your capabilities in an all-inclusive offering.

The future of embedded analytics: Data storytelling

Embedded analytics is constantly evolving, but one of the key trends driving this evolution is  Data Storytelling, which is the ability to tell a story with data and to personalize what's being presented to fit the audience.  

Data visualization, which is understood as the art of illustrating numbers in a clear and pedagogical way, is not sufficient in today's market. Though it allows you to communicate complex figures and information by transforming them into visual objects, it is more suited for Data and Business Intelligence departments in large companies. Today all employees want (and need) to have greater insight into business activity and operational data. This is where the value of data storytelling really shines. 

To illustrate, a Marketing Director doesn’t have the same reporting needs as an operational manager responsible for digital campaigns. With data visualization alone, the Marketing Director will not be able to get a separate view of the data. But with Data storytelling, the Director can customize their data reports depending on their data needs to get relevant, actionable insights. 

Data Storytelling has many advantages, including:

• Turning your data into action. With clear and usable data at your disposal, you are able to quickly identify trends and possible strategies for your business.
• Improving team productivity. Data is automatically presented in a simple and interactive way. Your teams' time is thus spent more on high-priority, high-value tasks.
• Finding more agility in your decision-making. Thanks to a simple and easy-to-use tool in a context where decisions must be made more and more quickly.
  
  

The future for XaaS is embedded analytics. Application providers continue to mature the way they embed analytics into their products as well as the self-service functionality and analytics capabilities they offer. All these innovations will make your application more valuable to your users. And in the end, isn’t embedded analytics a means to make your product the best it can be?