Building analytics into your product roadmap: 4 pitfalls to avoid

Your product roadmap for analytics

Across industries, the demand for embedded analytics in applications has surged, spanning marketing, sales, and finance. Incorporating analytics into products enhances end-user satisfaction and drives adoption, making it a critical component of the modern user experience. As highlighted in the Harvard Business Review, “Products fueled by data and machine learning can be a powerful way to solve users’ needs. They can also create a ‘data moat’ that can help stave off the competition.”

For product managers, delivering a seamlessly integrated analytics experience is essential. It boosts end-user engagement, increases sales, offers a competitive advantage, and reduces customer attrition. Most importantly, it empowers end-users to make better, data-driven decisions within the context of their workflow.

Analytics has evolved beyond basic usage reporting and rudimentary dashboards. Today’s end-users expect sophisticated analytics in the apps they use daily. With the rise of artificial intelligence and machine learning, business users now anticipate intelligent experiences powered by predictive insights and GenAI, complementing the traditional visualizations, and metrics.

Analytics is no longer confined to standalone dashboards or the home screen. It is now embedded within end-user workflows, providing real-time guidance for daily decision-making.

This demand for intelligent, integrated analytics is pushing product managers to rethink their approach to analytics. New technologies, such as composable analytics software development kits (SDKs), enable product teams to deliver a new class of analytics. These modular and reusable components can be integrated into applications, enhancing functionality with machine learning and GenAI-powered insights.

In this whitepaper, we’ll explore why integrating embedded analytics into your product roadmap is crucial for every product manager. We’ll discuss how it enhances the end-user experience, accelerates time to market, and more.

1: An introduction to embedded analytics and composable development

2: Using embedded analytics to provide a personalized and contextual experience

3: Avoiding analytics pitfalls in your product roadmap

4: Key capabilities to look for in an embedded analytics platform

5: Analytics are a ‘must-have’ for buyers

Sisense: The fastest, best way to deliver embedded analytics

1. An introduction to embedded analytics and composable development

An embedded analytics platform empowers product teams and developers to seamlessly incorporate data analysis and visualization functionalities into their applications. This integration allows end-users to readily access and interact with valuable data insights directly within the user interface.

Modern embedded analytics platforms are designed around composable development. This approach utilizes interchangeable, modular components to construct applications, making systems more adaptable and agile. By using an embedded analytics platform, product teams can accelerate the delivery of in-product analytics and data products to market without compromising the end-user experience.

Spotlight on composable development

The Gartner Market Guide for Embedded Analytics reports that by 2026, 50% of organizations will have to evaluate analytics and business intelligence (ABI) and data science and machine learning (DSML) platforms as a single and composable platform.

In a nutshell, product teams are looking to access BI and AI capabilities through a unified set of composable services. But what exactly is composability? In the past, analytics platforms were “monolithic.” It means teams would struggle to embed entire dashboards, or unwieldy charting components, or would have to establish a workaround to simply access the data. They didn’t provide enough granularity to give development teams the control they needed. And with end-users expecting analytics to be blended into their daily tasks, developer control is more important than ever.

Embedded analytics geared around composable development solves this challenge. The analytics platform provides a strong foundation of a semantic layer, data security, query optimization, data connectivity, instrumentation, and more. It enables back-end teams to build a robust analytics core and data model. And for application development teams, it provides composable services, such as data visualization, data access, GenAI chat, forecasting, and other services. These services are available as standards-based UI components or through APIs for maximum control.

This way, composable development enables product teams to access a broad range of analytics and AI/ML services and utilize them quickly and harmoniously in their app.

The Rise of Conversational Analytics with GenAI

Today’s users expect a more intuitive way to interact with data. Traditional click-to-query analytics no longer suffice. Product managers offering analytics solutions need to embrace conversational experiences powered by GenAI (Generative Artificial Intelligence). This means choosing an embedded analytics platform that provides GenAI capabilities for seamless integration into your product.

But here’s the key: You’ve got to incorporate it in a way that feels natural. And that’s where composability comes in. With composability as the foundation of your product roadmap for analytics, you have the visual components, building blocks, and APIs to add GenAI-powered natural language query (NLQ). You can flexibly integrate a conversational experience into your product or service.

For example, integrating AI-powered conversational features into your product may allow end-users to engage with an analytics virtual assistant (or you might extend your existing assistant so that it can also answer analytics questions) and inquire, “Could you provide an overview of customer satisfaction trends in our European market for the previous quarter?”

The embedded analytics platform’s GenAI assistant would then interpret the user’s request, map it to the data model (semantic layer), and retrieve relevant information. If the user seeks further explanation, the conversational interface could analyze the results and provide insights like “Customer satisfaction in Europe shows steady improvement, with a significant rise following the launch of the new loyalty program.” With composable development paired with GenAI, you get the code-first flexibility to deliver an experience that weaves traditional “click-to-query” and conversational paradigms together. For example, by presenting analytics using visualization, and providing inline GenAI-powered narrative explanations alongside the visualization, your end-users get the best of both worlds.

We would not have been able to develop the game-changing analytics innovation we have today without having an embedded analytics platform to build on.”

– Shaul Shalev, Safety Analytics and Innovation Manager, Air Canada

The benefits of embedded analytics

Embedded analytics can provide a key point of differentiation in the market, increase end-user stickiness (and reduce churn), drive sales and win rate, and enable new monetization opportunities. The top benefits include:

Enhanced product experience. You can increase end-user satisfaction by delivering consumable insights and context into the hands of your end-users.

Your end-users see more value from your product. Providing built-in analytics where your end-users can measure the impact of your product or service is a powerful way to reduce churn and enable your end-users to justify continued use of your service.

Increase your sales win rate. Adding a great analytics experience to your product is a guaranteed way to make it attractive to potential customers, ease selling friction, and turn what might be a tactical value proposition into a strategic, high-value win for your sales team.

Drive new revenue streams. Embedded analytics can be monetized through various pricing models, such as subscriptions, pay-per-use, or tiered pricing, depending on the target market and customer requirements. All can increase deal size or drive net retention rate (NRR).

Empower users with self-service. Allow users to answer their own questions and explore data at their own pace, reducing the burden on your team to generate manual reports.

Increase user retention. With all of the customer acquisition costs (CAC) that go into adding a new customer, it’s critical that product managers do everything possible in the product to prevent churn after the customer has been acquired. And the fact is that end-users who rely on the data that your product delivers are less likely to consider replacing your product with a competing solution.

2. Using embedded analytics to provide a personalized and contextual experience

Embedded analytics allows product managers to personalize the user experience by giving end-users access to the data they need, exactly when they need it. This empowers end-users to analyze data and make clearer decisions at the moment. By infusing analytics into your apps and providing them in context, you can also integrate them into the daily workflow of your end-users; meaning you’ll create the stickiest possible analytics end-user experience. You’re creating an analytics experience that becomes an essential part of their routine when using your app.

Personalized contextual analytics offer multiple benefits that can significantly enhance decision-making and drive impactful outcomes. Here are some key advantages:

Increased relevance. By delivering insights that are directly relevant to your user’s context and responsibilities, personalized analytics help avoid information overload. End-users can prioritize their attention and efforts on the most critical aspects.

Improved engagement and adoption. Personalized analytics create a more engaging end-user experience by delivering targeted insights that resonate with end-users.

Enhanced efficiency. Personalized analytics provides users with the information they need instantly, eliminating the need for time-consuming searches and manual filtering. This improves productivity by allowing users to focus on analysis and decision-making.

Better decision-making. Personalized analytics enable end-users to gain a deeper understanding of their data by providing insights specific to their needs at the moment. This empowers end-users to make more accurate and informed decisions, for improved business outcomes.

Integrating advanced analytics and insight capabilities into your product can dramatically increase the perceived value to organizations and increase the average selling price by 25%:

“Independent software vendors say embedded analytics increases the value of their apps by 43% and enables them to charge 25% more on average.”
-Eckerson Group

3. Avoiding analytics pitfalls in your product roadmap

Analytics roadmap pitfall #1: In-house resource constraints negatively impacting time-to-market

One of the biggest roadblocks preventing organizations from successfully incorporating analytics into their product roadmap is in-house resource and budget constraints, which ultimately hurts time to market. Few development teams have the resources to build satisfactory modern analytics from scratch, and end-users have higher expectations than ever.

With a range of sophisticated analytics and visualization tools available, integrating a well-established platform into your product is a much faster route to market. This approach delivers rich capabilities at a fraction of the cost of in-house development. Choosing the right provider offers greater ROI, reduces maintenance costs, and enhances innovation. Plus, you can start small and select tools that fit your architecture and development process.

Wiley’s Embedded Analytics for Dummies puts it this way: “If you’ve decided to build your own analytics, you may find it easy to build a starter dashboard with a few charts that run queries against a database and look great to demo to your users. But what happens when there’s a new question that comes up from customers? Will you have the resources to go back and build those new requirements into your product? What happens when the data size and complexity grow? How will the team maintain performance with complex queries? Customer requirements are constantly changing and growing. How will you support all these challenges?”

Using an embedded analytics platform allows you to deliver robust analytics without compromising your development team’s productivity. It also makes it easier to support ongoing enhancements and meet evolving end-user requirements effectively.

Analytics roadmap pitfall #2: Not attracting or retaining analytics end-users

For embedded analytics to deliver value, it must seamlessly integrate with user workflows. Avoid clunky, separate dashboards that disrupt the user experience. Misaligned analytics features lead to poor user engagement and hinder adoption.

Utilize a composable and API-first embedded analytics platform. This empowers you to integrate relevant metrics, descriptive, and prescriptive analytics directly within the user interface. By surfacing insights within the user flow, you eliminate the need for users to search for information, fostering continuous engagement.

Analytics roadmap pitfall #3: Not scaling successfully

In today’s fast-paced environment, sluggish performance (multi-second response times) can drive users away. User engagement hinges on performance, but managing increasing data volumes and complex queries can be challenging.

The ideal embedded analytics platform can alleviate the burden on your operations and engineering teams by automatically applying the latest optimization techniques to every end-user query. This efficiency not only saves you time and money but also supports your scalability as your deployment expands. Additionally, it offers robust multi-tenancy, along with comprehensive observability and instrumentation. These features streamline customer onboarding, facilitate scaling, and enable precise performance monitoring at both the customer and instance levels.

Analytics roadmap pitfall #4: Not developing a strong business case

As a product manager, building a compelling business case for analytics is crucial as it forms a significant part of your analytics roadmap. It’s essential to pinpoint the key drivers for analytics adoption, whether it’s enhancing customer retention and satisfaction, boosting product engagement, or innovating new avenues to improve win rates through increased monetization or deal flow.

By aligning your analytics solution with your organization’s strategic initiatives and emphasizing its ability to address high-value challenges, you can effectively secure internal support. This approach enhances user experiences and strengthens your bottom line.

Develop a business case that addresses the challenges of each stakeholder, and create a quantitative analysis that shows how embedded analytics can help. Ask the right questions to understand what they’re striving to achieve. Then show them how the solution can meet those objectives effectively. Have a plan and share real-life case studies to support your position.

How to hit the ground running with your embedded analytics product roadmap

The trend in data applications is shifting away from standalone BI tools towards integrating analytics directly into product workflows. Products that leverage data and analytics to provide enhanced insights to end-users tend to be more engaging, valuable, and competitive compared to those that do not. Plus, vendors typically command 25% higher prices for such integrated products.

But to really see game-changing outcomes, it’s critical to integrate your analytics as early as possible into your product roadmap for analytics. By integrating early, you’ll be able to increase revenue, achieve faster end-user acquisition, drive stickiness, improve satisfaction, gain a competitive edge, and meet end-user data demand.

It’s time to start capitalizing on your users’ demands for embedded analytics to catapult your product roadmap, but how exactly do you get started?

Here, we’ve got seven crucial steps you can take to hit the road running.

Understand where you’re starting from

Before embarking on the journey to integrate embedded analytics into your roadmap, start by engaging with your end-users to understand the specific challenges that enhanced analytics can help solve. Focus especially on issues that can be uniquely addressed using the data generated by your product.

Consider how insights derived from this data can fluidly integrate into your end-users’ existing workflows and translate into clear product requirements. This approach ensures that your analytics integration efforts are driven by real end-user needs and aligned with enhancing overall product functionality.

Carefully consider your launch phases

When strategizing your launch phases, it’s crucial to approach analytics integration thoughtfully. While rapid product launches can be enticing, adopting a more methodical, phased approach is best for analytics, particularly when implementing predictive capabilities. This approach allows ample time for thorough testing, establishing credibility, and closely monitoring usage trends. It also enables you to effectively guide end-users through the nuances of your analytics offering.

Selecting a multi-tenant embedded analytics platform facilitates swift customer onboarding and continuous rollout of upgrades, enhancing your ability to scale and adapt to evolving user needs. This deliberate approach not only fosters a smoother implementation but also sets a solid foundation for long-term success and customer satisfaction.

Build in AI at the core

End-users are expecting more from analytics. They’re expecting AI to be part of the experience. Incorporating AI at the core of your embedded analytics can significantly enhance the user experience by providing predictive insights and personalized recommendations. By leveraging AI, your product can stay ahead in delivering sophisticated analytics capabilities that adapt and evolve with end-user needs.

Plan to continually adapt

Successfully integrating embedded analytics into your roadmap should be a part of the journey, not the final destination. End-user needs will inevitably change and so will the market requirements, so use this time to plan for adaption. Get a plan in place to track and respond to end-user feedback even after your product launch. Don’t engineer yourself into a corner with costly-to-modify visualizations and integrations.

Develop a monetization strategy for your solution

Use this stage to strategize on monetizing your solution, exploring multi-tiered revenue streams centered around analytical and product value tiers. For example, perhaps you can charge for the integration of third-party data.

Pick the right partner

Choosing the right embedded analytics provider is crucial for enhancing your product roadmap, bolstering your UX, and ensuring a successful launch. However, before you can reap the benefits of an integrated embedded analytics solution, you must address the biggest challenge: Do you build or do you buy?

Buy, don’t build

Relying on ground-up development or managing high-maintenance open-source components can derail your product team. Carefully evaluate the time and cost benefits of building vs. buying an embedded analytics platform. For example, buying an embedded analytics platform can lead to faster implementation, offer immediate access to advanced features such as AI, deliver ongoing support for your development team, and achieve a significantly lower TCO. This approach empowers your team to concentrate on providing value instead of allocating resources to develop and sustain an in-house analytics solution.

Take a composable-first approach

Many embedded analytics platforms aren’t designed for modern composable development. Composable analytics development offers the advantage of rapidly building and customizing analytics applications by leveraging pre-built components and capabilities, enabling product teams to accelerate their delivery process.

By utilizing granular and reusable components, composable analytics development streamlines front-end engineering and eliminates the need to balance end-user experience with velocity. In addition, it empowers teams to efficiently deliver advanced in-product analytics capability, quickly.

Key questions to accelerate your analytics roadmap

• Are data experiences and analytics development the core competency of your team?
• Do you have the resources to deliver an MVP solution? Could those resources be better spent on another project if you used an established analytics solution?
• Can your team also develop a ground-up solution for improving and nurturing it over time?
• Do your development resources have the deep understanding of business intelligence capabilities required to build a credible system from scratch? Will you need to hire?
• What’s your timeline for rolling out an embedded analytics solution?
• How much advanced analytics will your roadmap require, such as forecasting, prescriptive analytics (like recommendations), or emerging capabilities like GenAI-powered natural language analytics assistants?
• Will a basic in-house analytics solution allow you to quickly adapt to changing end-user demands for data? While building seems initially attractive—and development teams are often eager to try—take a moment to consider the decades of engineering that have gone into the modern embedded analytics experience. Also, consider the deep functionality that end-users expect.
• How important is providing an analytics experience that’s tightly woven into your end-user experience and workflows, which necessitates a composable-first approach?

4. Key capabilities to look for in an embedded analytics platform

To ensure success and grow in a scalable way, the embedded analytics platform you choose should provide a range of capabilities, including:

Composable development. Enable teams to assemble granular pre-built components or source analytics data into their applications to ensure speed of development and an optimal end-user experience. Comprehensive TypeScript-based SDKs offer fine-grain control of analytics integration directly into the UX.

Support for the latest front-end frameworks. Support modern frameworks such as React, Angular, and Vue.js, allowing front-end engineering teams to easily incorporate analytics.

Connectivity to analytics data. Provide pre-built data connectors for seamless integration with various data sources, such as Snowflake, Redshift, or your specific database schema.

Scalability and elasticity. Offer caching, query optimization, and instrumentation to help product teams handle growing data and end-user volumes while ensuring fast-loading visualizations.

A flexible semantic layer. Enable product teams to build and maintain a shared data model that represents dimensions, metrics, and fields mapped onto their underlying data for query consistency.

Strong SDLC integration. Integrate tightly with CI/CD processes, version control, and your source control systems like Git.

Native multitenancy. Enable product teams to support any number of customers in one instance securely. This streamlines maintenance, upgrades, instrumentation, management, and observability across customers who are utilizing analytics.

Deep AI/ML support. Provide components for AI/ML-based services that make it easy for product teams to incorporate anomaly detection, forecasting, recommendations, and GenAI-powered natural language processing.

Built-in security. Prioritize strong data security and compliance with industry standards and regulations to protect sensitive customer data.

Ensure integration with your DevOps and CI/CD processes

For successful embedded analytics adoption, seamless integration with your development team’s CI/CD methodology and DevOps processes is crucial. Most engineering teams rely on Git source control to manage code changes. This allows them to track revisions of analytics features alongside other product functionalities.

However, not all analytics tools fully integrate with Git. Some limitations to be aware of include:

• No-code Models and Dashboards: Certain tools don’t represent models or dashboards as code, necessitating blind commits, making it difficult to identify and resolve conflicts.
• Limited Git support: some tools might only support Git for data models, not visualizations. Additionally, they may enforce their own branch strategy, potentially clashing with your existing development workflow.

This is why it is essential to leverage a platform that can effectively support:

• Separate Development Environments: This allows developers to work on new features without impacting the production environment.
• Staging Environments: Staging environments provide a testing ground for new features before deployment to production.
• Production Environments: This is the live environment where users interact with the final product.

Ask yourself:

• Is it simple to get a live system running against a feature branch?
• How easy is it to revert a staging environment to the version of the analytics and the product code that was in use yesterday?

Seek out platforms built for your developers

Choose platforms with granular APIs that allow developers to manipulate all parts of the platform, and allow uninterrupted integration with front-end frameworks. The benefits of this approach include:

• Dynamic changes to data models
• Customizable visualizations in code
• Modern React, Angular, or Vue.js components
• Support for modern languages like TypeScript
• Automated administrative tasks such as end-user management and permissions via API

Check for flexible deployment options

Make sure your embedded analytics platform supports the deployment model you prefer. Some solutions are fully SaaS, which is great if you’re also SaaS. If you have some financial services end-users or healthcare providers, you may need to run the embedded platform in Azure, or on your own (or your customers’!) VPC, or even physically in-data center or on-premise.

And remember, ‘can be run in the cloud’ isn’t the same as ‘takes advantage of the scalability of the modern cloud.’ Look for microservices and native Kubernetes support (EKS, AKS, GKE), especially if you plan to self-host on one of the major public clouds.

Take a look at multi-tenancy support

Multi-tenancy allows developers and delivery teams to optimize resource allocation, simplify operational management, and achieve scalability more efficiently. It also ensures it’s easier to roll out upgrades and maintenance fixes across all customers, rather than having to upgrade customers individually. That’s why it’s particularly important to ensure your embedded analytics platform provides native multi-tenancy.

Embedded analytics platforms vary significantly in their capabilities, particularly concerning multi-tenancy. It’s essential to delve deep into the specifics of how each platform manages multiple customers, including the procedures for rolling out upgrades and its instrumentation and monitoring capabilities at both the tenant and overall instance levels.

This scrutiny ensures you select a platform that not only meets current operational needs but also scales effectively with your business growth, enhancing customer satisfaction and operational efficiency.

Five top considerations

There are five critical considerations you need to keep in mind when choosing the right vendor for embedded analytics.

Analytics scope

Likely, you’ll need both traditional and modern data capabilities. End-users expect more, and their needs are evolving. For some, baseline capabilities may be enough, but for many, advanced analytical features are the way to go. Therefore, it’s important to consider a vendor that can cater to a broad spectrum of self-service analytics requirements, both traditional and more complex, such as using predictive analytics. A combination of expected traditional embedded BI offerings with advanced capabilities offers a differentiated analytics experience, as well as opportunities to explore critical metrics and discover insights that encourage end-user adoption.

Analytics control

Your embedded analytics vendor should facilitate the seamless integration of analytics into your product’s UX. It’s crucial to choose a solution that effortlessly incorporates advanced features and automated insights into your core product, maximizing value for end-users. The UX experience is just as important as product capabilities, so selecting a vendor that offers flexibility in customizing the look and feel of analytics components is essential. This will also help to evaluate any potential third-party concerns from users.

Analytics time-to-market

Your embedded analytics vendor needs to accelerate your product’s time-to-market. As mentioned, the time and cost it takes to build your own analytics solution and get it up and running is a major roadblock. Therefore, it’s crucial to choose a platform that accelerates your time to market through streamlined integration processes. This efficiency not only reduces the deployment time for in-built analytics, making them accessible to end-users sooner, but also frees up resources to enhance other critical aspects of your product. Selecting a tool with robust integration capabilities ensures smoother implementation and faster realization of the analytics-driven value within your product ecosystem.

Analytics support and success

Deciding on an embedded analytics platform isn’t just about features. It’s about choosing a strategic partnership with an experienced provider who is focused on embedded analytics and has a track record of success to complement your organization and team.

Partnering with an expert can provide insight into embedding best practices, and technical architecture design, to ensure your analytics solution scales and fits within your architecture and infrastructure needs. Selecting a provider dedicated to embedded analytics offers significant advantages, including robust developer support and a continuous roadmap of new features and capabilities. These resources empower your team to enhance your solution over time, integrating cutting-edge analytics smoothly. A trusted analytics vendor becomes a long-term partner, supporting your evolving product needs and ensuring sustained innovation and competitiveness in the market.

Total Cost of Ownership (TCO)

While the initial cost of an embedded analytics solution is important, a more strategic approach considers TCO. Building your own solution can be expensive with development time, maintenance, infrastructure, and personnel costs. Embedded analytics platforms minimize these burdens.

Evaluate vendors beyond price. Look for transparent pricing models that scale with your needs, robust ongoing support with predictable costs, and clear communication regarding any potential hidden fees. Choosing a vendor with a competitive TCO unlocks cost savings and frees up resources from your team to focus on core product development.

5. Analytics are a ‘must-have’ for buyers.

Analytics are now essential, offering significant revenue and retention benefits when integrated into your product. When selecting an embedded analytics platform, prioritize its ease of integration into your development methodology and robust API support. This is something you buy, not build, especially with new end-user expectations around AI. Why?

The breadth of end-user needs makes building the necessary capabilities in-house impractical. Users now demand integrated insights within their workflows, moving beyond traditional dashboard reporting. Embedding analytics is crucial for enhancing product experiences and differentiating offerings in a competitive market.

This strategy opens avenues for new revenue, upselling opportunities, and the ability to command premium pricing. Partnering with a dependable embedded analytics vendor keeps you ahead of evolving user expectations, enabling the creation of products that truly delight users and deliver substantial value.

Sisense: The fastest, best way to deliver embedded analytics

A Gartner Visionary, Sisense provides an AI-driven analytics platform, enabling companies like Air Canada, Skullcandy, and Wix to deliver the best insights to their end-users.

Product and development teams at thousands of companies globally rely on Sisense to embed business analytics and accelerate their product innovation using Sisense’s suite of no-, low-, and pro-code tools. Sisense integrates context-aware insights and analytics into data products in a modular, flexible, and scalable way.

By partnering with Sisense you’ll have access to the following key benefits:

• Cloud-native deployment. Rapidly deploy in Sisense’s cloud or your own infrastructure.
• Extensive developer APIs. For enhanced integration into app workflows, interactivity, and custom behavior. Go beyond the simple ‘look and feel ’ changes.
• Modular development with Sisense Compose SDK. Leverage the latest in composable development using Sisense Compose SDK, which enables analytics to be woven into the end-user experience.
• Query and NLQ APIs. Drive application behaviors using data and Natural Language Query capabilities, going beyond simple chart embedding.
• Tailored user experience with design flexibility: Empower your designers. Sisense offers exceptional expressivity, allowing designers to create a user experience that seamlessly aligns with your existing product’s look and feel.
• AI capabilities. Integrate cutting-edge GenAI-powered chatbots and advanced machine-learning-driven predictive analytics into your product offerings.

Optimized query performance, responsiveness, and scalability. Ensure consumer-grade responsiveness for data queries and visualizations, even under high user and customer load.

To learn more about analytics solutions that will keep you competitive, schedule a demo.