Author: Smarten

Smarten Support Portal Updates – March – 2026!

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March 26, 2026

We invite you to explore our latest knowledgebase articles and to join the Smarten user community on Smarten Support Portal.

If you have not registered yet, Click Here to obtain your login credentials.

Knowledgebase Articles

  1. General : Custom Columns (UDDC) : UDDC Functions
  2. Data Sources : Improve data extraction performance from database data source
  3. Schedulers : Delivery and Publishing : Working with E-mail Delivery and Publishing Task

Predictive Use cases

  1. Assisted predictive modelling : Medical Cost Prediction Using Smarten Assisted Predictive Modelling
  2. Assisted predictive modelling : Hypothesis : Independent Sample T-test Using Smarten Augmented Analytics

Forum Topics

  1. Graphs : Global Variable : How can I plot the dynamic graph based on measure selected from the list of measures
  2. Embedded / API Integration : How to rebuild cubes/ datasets through API call?

Manuals and Technical Guides

  1. Smarten Administrator Manual
  2. Smarten Concept Manual SnapShot
  3. Smarten Securing Application Server

If you don’t find what you need in the Support Portal, we hope you will contact us with your questions, comments or suggestions. We are here to help and to support your business goals and success!

URL: Smarten Customer and Partner Community
Email: support@Smarten.com

Original Post : Smarten Support Portal Updates – March – 2026!

Assessing Ten Years of the Citizen Data Scientist Approach

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March 3, 2026

Ten Years Into the Citizen Data Scientist Revolution

It has been a decade since Gartner first defined the role of a Citizen Data Scientist. In 2018, Forbes published an article, referencing Gartner’s updated analysis of the role and stating that, ‘And the best part is, mid-market organizations already have potential citizen data scientists on their staff—it’s just a matter of tapping those with potential and interest in the work, and cultivating an analytical mindset across their workforce. Then, those who serve as citizen data scientists can grow their own skillsets, all the while being active players in their companies’ ability to tap the value of big data and drive transformation.’

‘It is likely that the Citizen Data Scientist role will continue to evolve, and it is important that the enterprise facilitate collaboration and knowledge sharing and build a sustainable technology environment with appropriate policies.’

A lot has happened in the past decade, and today the role of Citizen Data Scientist is no longer new. So, what has changed in the ensuing years? How as this role changed? Has the average enterprise embraced the role and made the technological and cultural changes required to truly support this approach?

A Decade of Citizen Data Scientist Evolution

Here are a few of the ways in which the Citizen Data Scientist approach has evolved within the organization.

  • In the early days of the Citizen Data Scientist approach, Data Scientists often worried that their positions would become obsolete. Nothing can be further from the truth. The use of Data Scientists to perform strategic analytics and to refine analysis performed and submitted by business users has kept Data Scientists busy.
  • The evolution of augmented analytics and self-serve tools has expanded analytical capacity, the speed at which business users can gather and analyze data and the dependability of the outcomes. Drag and drop capabilities, machine learning and, more recently, artificial intelligence (AI) have significantly improved tools and made it easier and more desirable for business users to dive into analytics and make it part of their day-to-day role.
  • As cloud-based access expanded, the enterprise leveraged improved access and data platforms to enable collaboration and create multi-disciplinary teams and power user roles that would further encourage the use of these tools across the enterprise. Methods and guidelines improved data literacy and encouraged business user expertise creating more confident Citizen Data Scientists and supporting the role as a mainstream concept.

Every enterprise must do more with less, increase productivity and reduce missteps in order to remain competitive. So, it is likely that the Citizen Data Scientist role will continue to evolve, and it is important that the enterprise facilitate collaboration and knowledge sharing and build a sustainable technology environment with appropriate policies for user access, upgraded technology and data analytics tools and standards and regulations to govern and manage risk and maintain alignment with enterprise strategies and goals.

‘Those who serve as citizen data scientists can grow their own skillsets, all the while being active players in their companies’ ability to tap the value of big data and drive transformation.’

If you wish to know more about the Citizen Data Scientist approach and how augmented analytics tools and your industry and market knowledge can position you for success in this role, Contact Us today to find out how our team can help you to improve business results and increase team collaboration, data literacy, productivity and competitive advantage. Get started today with our self-paced FREE Online Citizen Data Scientist course.

Original Post : Assessing Ten Years of the Citizen Data Scientist Approach!

Mobile BI Tools Are Not Just Nice to Have, They Are A Necessity

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February 18, 2026

Mobile BI Tools Support Users and a Competitive Strategy

No matter the size of your business or the industry or market in which you compete, the importance of business intelligence (BI) and self-serve augmented analytics is no longer in question. Nearly every business is using some form of business intelligence today and, if your business is not, you are already behind the competition!

When we consider the growth of this important solution market, we find that the mobile market for business intelligence solutions is growing exponentially and that is not surprising. Today’s workforce is mobile and, even where employees work onsite, their work will take them away from a desk and into a manufacturing or production environment, or perhaps within the walls of a hospital, a university, a retail store, etc.

‘The shift to mobile BI tools is not just a matter of convenience. It is crucial that your team remain informed and that they have the tools they need to collaborate, share and present data, whether they are on the road, with a client or in an airport. This flexibility reflects the evolving business environment and the need for agility.’

A recent Mordor Intelligence Report revealed that the 2025 market size for mobile business intelligence was 19.33 billion USD and that the 2030 projected market size is 55.56 billion USD.

While many businesses rely solely on BI solutions and look to their team to implement and use these solutions, the growth of business intelligence services is also of note. The complexity of technology landscapes, in-house vs. cloud implementations, security and regulatory and industry compliance, as well as artificial intelligence (AI) add-ons and integration have created an environment that is often best served by expert services.

These services can help businesses to personalize and customize their approach and to provide meaningful mobile BI tools, embedded BI for familiar best-of-breed and legacy software solutions and other tools that will support end users and make the team more productive.

User accessibility and ease-of-use are paramount considerations. The shift to mobile BI tools is not just a matter of convenience. It is crucial that your team remain informed and that they have the tools they need to collaborate, share and present data, whether they are on the road, with a client or in an airport. This flexibility reflects the evolving business environment and the need for agility.

Whether you are a sales manager, a media executive, a healthcare provider or a business analyst, it is important to have access to a suite of BI tools that will help you gather and analyze business data using mobile devices.

Why is Mobile Business Intelligence Important for My Business in Today’s Work Environment?

The team needs interactive data tools and tools that enable sharing and collaboration, reporting and sophisticated analytics – all with dependable security. Users should also be able to tailor their mobile BI tools to suit their role and preferences and integrate the use of these tools within workflow to foster a data-driven, fact-based culture.

‘Today’s workforce is mobile and, even where employees work onsite, their work will take them away from a desk and into a manufacturing or production environment, or perhaps within the walls of a hospital, a university, a retail store, etc.’

Look for a mobile BI tool that will:

  • Support a native mobile environment for Android and iOS, with an intuitive user experience (Ux).
  • Provide access rights defined at the server level with appropriate security and privacy at all levels.
  • Accommodate hosting within the IT infrastructure, on-premises or in public or private cloud environs.
  • Provide users with access to dashboards, reports, and clickless analytics with a natural language processing (NLP) search function.
  • Provide expertise and services that are appropriate for the needs of your organization.

If you want to support your business user team and provide a foundation for BI tools that will better serve your team and your customers, explore Smarten Mobile BI benefits and features, with powerful functionality and access for your business users including out-of-the-box Mobile BI and advanced analytics for every team member. For more information on Mobile BI and Augmented Analytics, read our article, ‘Mobile BI Business Use Provides Real Advantages,’ and take a moment to watch and listen to this informative webinar, ‘Smarten Mobile BI.’

Original Post : Mobile BI Tools Are Not Just Nice to Have, They Are A Necessity!

Case Study : Augmented Analytics for India Electrical Sector Organization Focused on Testing, R&D and Certification

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February 10, 2026

The Client is a premier not-for-profit institution in India, established as an independent organization with strong government and industry backing, and widely recognized for leadership in the electrical power testing, certification, inspection, and research domain.

Continue reading “Case Study : Augmented Analytics for India Electrical Sector Organization Focused on Testing, R&D and Certification”

Smarten Support Portal Updates – January – 2026!

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February 5, 2026

We invite you to explore our latest knowledgebase articles and to join the Smarten user community on Smarten Support Portal.

If you have not registered yet, Click Here to obtain your login credentials.

Knowledgebase Articles

  1. Datasets & Cubes : Migration from Cube to Dataset
  2. Data Sources : Improving performance for fetching data from the database
  3. Crosstab : Spot the Gross Sales highlighters

Predictive Use cases

  1. Assisted predictive modelling : Classification : Customer Churn model using Smarten Assisted Predictive Modelling
  2. Assisted predictive modelling : Hypothesis : Independent Sample T-test Using Smarten Augmented Analytics

Forum Topics

  1. Dashboards : How to improve the dashboard loading time in Smarten?
  2. Others : General : How to add SSL certificate to the Smarten BI Portal?

Manuals and Technical Guides

  1. Smarten Concept Manual SmartenInsight
  2. Smarten Working with Real time data
  3. Smarten Working with On-Premise Gateway

If you don’t find what you need in the Support Portal, we hope you will contact us with your questions, comments or suggestions. We are here to help and to support your business goals and success!

URL: Smarten Customer and Partner Community
Email: support@Smarten.com

Original Post : Smarten Support Portal Updates – January – 2026!

Embedded BI for End-Users Improves User Adoption and Results

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February 4, 2026

Should My Business Employ Embedded BI for End Users?

The Embedded Business Intelligence (BI) and augmented analytics market is growing by leaps and bounds. According to a recent Mordor Intelligence the banking and finance industries represent 21% of the Embedded Business Intelligence end-user market, while Healthcare represents 15% and there is significant growth in the end-user component of business intelligence solution use for manufacturing, retail and energy. In short, embedded BI for end-users is growing in many industries and markets.

‘No matter the industry or market, end-users can easily access embedded BI and analytics tools from within familiar, popular solutions and apps and use analytics to gather and analyze data, produce reports and collaborate with other users to make fact-based decisions.’

Embedded BI allows the enterprise to affordably and quickly put the power of integrated business intelligence where they need it, with simple, easy-to-use analytics integration within enterprise and business applications in a single sign-on environment. Organizations can easily integrate and embed BI objects within their ERP, CRM, Intranet portal or other applications, to improve user adoption and maximize Business Intelligence ROI.

When enterprises integrate Embedded BI within popular solutions, they report significant improvement in user adoption and in the value of BI tools.

  • 67% of companies say time spent in their applications increased after they embedded analytics
  • 93% of application teams say embedded analytics improves their user experience

Business users can access augmented analytics and BI tools from within their enterprise applications or mobile apps. The enterprise can engender business intelligence data democratization and alleviate user frustration with real business user solutions and deep dive capabilities. Your team can provide user access within their favorite applications to improve user adoption of business intelligence tools, and engender collaboration, data sharing and fact-based decisions.

How Does Embedded BI Improve BI User Adoption and End-User Solution Satisfaction?

When your business selects the right embedded BI solution, there are many advantages for end-users, including:

  • Seamless Access to Data Sources including data warehouses, and business application architecture with single-tenant mode or multi-tenant modes.
  • Single Sign-On Access (SSO) for access to analytics, using the same login credentials as those employed to access the business application.
  • Access to Embedded BI Objects from Within Your Application with easy to use, scalable Integration API. Integrate BI objects (Dashboards, Crosstab, Tabular, KPIs, Graphs, Reports, Clickless Analytics and more) into your business application.

Select a solution that is easily implemented without a lot of customization and that can be deployed quickly and easily, so your end-users can get started using self-serve analytics right away.

‘Business users can access augmented analytics and BI tools from within their enterprise applications or mobile apps. The enterprise can engender business intelligence data democratization and alleviate user frustration with real business user solutions and deep dive capabilities.’

No matter the industry or market, end-users can easily access embedded BI and analytics tools from within familiar, popular solutions and apps and use analytics to gather and analyze data, produce reports and collaborate with other users to make fact-based decisions. Embedded Bi enables user adoption to improve Return on Investment (ROI) and Total Cost of Ownership (TCO) and it increases user satisfaction and understanding of analytics by providing easy-to-use tools that make the team member’s job easier and increase productivity.

Contact Us to discuss the unique needs of your organization and your users and find out more about Smarten Technology. Explore Embedded BI And Analytics and how it can help you to achieve your goals, improve user adoption and satisfaction and improve ROI and TCO.

Original Post : Embedded BI for End-Users Improves User Adoption and Results!

Case Study : Augmented Analytics for India-Based Municipal Corporation to Support Smart City Initiative

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January 30, 2026

The Client is one of the largest municipal corporations in India, and is responsible for delivering essential civic services, i.e., water supply, sewage treatment, solid waste management, road infrastructure, public health, education, and environmental programs for an urban population of over 3.1 million citizens.

Continue reading “Case Study : Augmented Analytics for India-Based Municipal Corporation to Support Smart City Initiative”

Should I Choose a BI Tool with On-Premises or Cloud-Based Storage?

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January 21, 2026

Is Cloud-Based or On-Premises Data Access Best for BI Tools?

There are many considerations your business much include in a business intelligence (BI) and augmented analytics decision. When reviewing analytics software and solutions, remember that data storage and data management is a crucial decision. Ease of access, the speed of that access, dependable storage and security and other factors must be considered.

‘Scalable technology infrastructure and architecture and support for on-premises, private cloud or public cloud implementation is crucial to support future growth and enable your users with easy, secured access to information and seamless response.’

When you consider the technical foundation of the product and services, you must decide between a solution that resides on premises and one that is cloud-based.

Cloud-Based: This relies on third-party servers to support software and data and accesses information via a secured internet connections using a cloud environment, providing a flexible, scalable approach and allowing the enterprise to access software and features without using its own resources, IT support, and hardware.

On-Premises: This option uses in-house, enterprise hardware and infrastructure and businesses often choose to go this route retain control and provide assured security. It is important to note that some solutions store the BI tool in the cloud, while data is stored on premises, and this can create issues with delays and latency.

Gartner Predicts that, ‘Demand for integration capabilities, agile work processes and composable architecture will drive continued shift to the cloud.’

If you are choosing an analytics product, a business intelligence (BI) solution or augmented analytics, consider a vendor that allows you to choose between on-premises or a cloud-based approach, and provides the flexibility to change that approach without having to change your software or vendor.

https://www.elegantmicroweb.com/wp-content/uploads/2026/02/make-the-right-choice-does-your-enterprise-need-cloud-based-or-on-premises-bi-tools-1024x1024.png

Work with your IT partner, to develop requirements and include the following factors in your requirements:

  • Business Strategy for Growth and Number of Locations and Users
  • Security and Standards Compliance
  • Ongoing Access to Technical Experts
  • Clear, Flexible Management of Data Sources, and Data Access
  • Location of the Data Sources – On Premises or Cloud
  • Affordability of Solution and Support and Maintenance Services
  • Performance, Scalability, Flexibility
  • Product and Service Support for Future Upgrade, User Needs, and Digital Transformation (Dx)

A flexible, low-code, no-code analytics platform with scalable technology infrastructure and architecture and support for on-premises, private cloud or public cloud implementation (including Amazon, Microsoft Azure, Google Cloud and other options) is crucial to support future growth and enable your users with easy, secured access to information and seamless response. Consult your IT partner to decide on your data storage and access approach based on data velocity, data sources and locations, data security and governance policies and your required return on investment (ROI).

The location of data sources is a paramount consideration when choosing between on-premises and cloud business intelligence. Pushing large volumes of ERP or CRM data from an on-premises location to the cloud BI tool will affect scalability and can cause delays. When considering a business intelligence solution, include a review of the architecture and data access, as well as vendor IT policies and security standards.

‘When reviewing analytics software and solutions, remember that data storage and data management is a crucial decision. Access, speed of access, dependable storage and security and other factors must be considered.’

Contact Us to discuss the needs of your organization and your users and find out more about Smarten Technology. The process of choosing the right Analytics Solution for your business is crucial and must include a careful assessment of your needs. Explore our White Paper: ‘Enabling Business Optimization And Expense Reduction Through The Use Of Augmented Analytics,’ and our article, ‘How Does Low Code And No Code Development Support BI Tools?’

Original Post : Should I Choose a BI Tool with On-Premises or Cloud-Based Storage?

The Biggest Analytics Mistake Product Teams Make: Assuming Usage Equals Adoption

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January 20, 2026

Why Usage Metrics Don’t Guarantee Analytics Adoption

Many product teams reach for the numbers first because numbers feel safe. When a dashboard opens a few hundred times and a Visualization loads, analytics teams celebrate. It appears to be proof that the product is functioning as intended.

However, usage metrics don’t necessarily indicate adoption. A spike in activity might come from curiosity, or confusion, or someone simply trying to find what they need. Adoption means the user came back because the analytics helped them think more clearly or take an action they wouldn’t have taken otherwise.

Read on to understand why relying too heavily on page views or click counts results in a distorted picture of success and uncover the fundamental drivers of adoption.

The Psychology Behind True Analytics Engagement

People approach analytics expecting quick answers. While a good dashboard reduces the mental effort required to analyze numbers, a bad one forces users to scan every corner and ask themselves what they’re supposed to understand. That hesitation is where adoption breaks.

When the screen feels cluttered or the logic is unclear, many users quit. If you want adoption to grow, you need to offer a sense of clarity. Users should be able to glance at a chart and know why it matters. They should feel a small sense of relief, not tension, when the numbers appear. Product teams often overlook these psychological cues because they’re hard to quantify, but they have a significant impact on the user’s long-term engagement.

Process Choices That Shape Adoption

Analytics is rarely plug-and-play. A launch without a plan sets users up for half-understood insights and half-hearted engagement. Teams sometimes push dashboards live without explaining what will change in the user’s routines or how the data supports the decisions they already make every day.

A rollout should be gradual, with space for questions, feedback, and reflection. People adopt analytics when it fits comfortably into their workflow—not when it creates a second job. Teams that handle adoption well introduce analytics as part of a sequence, not a surprise. They show users real examples of decisions the dashboards can support. They help people understand what to do when numbers unexpectedly shift.

Why UX Determines Whether Users Return

A user doesn’t keep returning to analytics because of a long list of features. They return when the experience respects their time and attention. At Smarten, we have watched Independent Software Vendors roll out analytics to thousands of users across varied industries. Over time, we realized that the teams that succeed focus on understanding real behavior rather than designing for hypothetical scenarios.

They start with the specific decisions users make, match analytics to those decision points, and build from that foundation. Their playbooks are built around gradual exposure, iterative refinement, honest user observation, and a willingness to discard dashboards that don’t serve a clear purpose.

Good UX is when users:

  • Get guidance through a clear hierarchy of information, consistent placement of controls, and thoughtful spacing.
  • Data is not hidden beneath layers of unnecessary complexity but is clearly visible for instant decision making.
  • The dashboard they’re looking at already knows their needs and what they’re trying to achieve.

How to Shift Focus from Usage to Adoption

A quick dashboard doesn’t tell the complete story. Time on page or high traffic doesn’t necessarily mean clarity or comprehension. Repeated views often come from people trying to confirm an insight they don’t fully trust.

Adoption occurs when a user changes their behavior due to what they have seen. That is the moment analytics becomes part of the work, offering accurate indicators, such as guidance, action, and improvement.

  • Instead of “How many people viewed this?” ask “Who relied on this to make a decision?”
  • Instead of “How can we drive more usage?” ask “What is stopping users from trusting what they see?”
  • Instead of asking for more filters or more charts, ask which ones genuinely influence meaningful actions.

When teams stop treating analytics like a destination and start treating it like a partner in decision-making, adoption becomes more predictable. Quality replaces quantity, relevance replaces novelty, and analytics becomes something the user reaches for because they help them think more clearly.

The Path Forward for Product Teams

Trust (and thus adoption) never arrives all at once. It’s built through consistent clarity and predictable behavior. If numbers change without explanation or if the logic behind a metric is hidden or ambiguous, trust fades. A user needs to know that the data is accurate and that the definitions are stable. They need small confirmations each time that the system works the way they expect. Trust grows when users feel informed and respected.

Usage will always be part of the analytics story, but it should never be the main character. Adoption should be the ultimate goal, achieved through clarity, trust, thoughtful workflow integration, and an understanding of how people think when presented with data.

Teams that recognize this offer Tools and dashboards that are an extension of the user’s own judgment. That is the difference between analytics that provides numbers and analytics that drives a decision.

FAQs

1. What separates adoption from usage?

Usage is an activity; adoption reflects decisions influenced by the analytics.

2. Why do high-traffic dashboards still fail?

High-traffic dashboards fail since traffic can mask confusion or lack of meaningful engagement.

3. How can teams raise adoption?

Teams must design for clarity, trust, and genuine workflow needs, rather than adding more features.

Original Post: The Biggest Analytics Mistake Product Teams Make: Assuming Usage Equals Adoption!

Why ISVs Must Stop Treating Analytics as a Support Function

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January 19, 2026

Why Analytics Must Become a Core Product Layer

Most software products are rich in data, yet surprisingly quiet when it comes to meaning. Analytics has lived on the sidelines for so long that many ISVs barely notice the cost, until scale exposes it. Report queues grow. Engineering time gets consumed by questions that the product should already answer. Customers wait for clarity that should arrive instantly.

The issue runs deeper than reporting. It’s the assumption that insight can live outside the product experience. Today’s users don’t want dashboards as destinations; they expect intelligence to show up in the flow of work, exactly where decisions are made. When analytics remains detached, a gap forms between what the software enables and what users actually understand about their operations.

This is why the conversation has moved beyond improving analytics. The real shift is treating intelligence as a product layer in its own right, embedded, governed, and self-service by design. When insight becomes native to the application, products stop delivering data and start delivering understanding. That change fundamentally redefines product value.

Why Reporting Becomes a Silent Bottleneck for ISVs

A familiar cycle unfolds in many software products: users encounter a data challenge, raise a ticket, wait for engineering, receive a report, request a revision, and begin the loop again. At first, this appears manageable. But as customer bases grow, small reporting requests accumulate into structural friction. The engineering focus gets divided. Product teams lose momentum because constant reporting tasks interrupt roadmap priorities. Over time, analytics becomes synonymous with operational dependency.

The burden goes beyond ticket volume; it stems from designing a system where insights are delivered manually instead of discovered naturally. When customers cannot explore their own data, they rely on engineering for clarity, even when the core product performs well. This dependence creates an invisible tax on innovation, slowing down releases and diluting the pace at which the ISV can improve its application.

Why Analytics Must Emerge as a Core Product Layer

Treating analytics as a core layer changes the behavior of both the product and its users. Instead of being an endpoint where someone receives a report, analytics becomes a living, navigable environment integrated directly into the application. This requires rethinking how insights are modelled, governed, and delivered. When customers interact with data as part of their workflow, they stop perceiving insights as external requests and start seeing them as an inherent capability. Analytics becomes a system that guides decisions rather than just answering requests.

This shift elevates the user’s experience because the product speaks through data, guiding decisions without requiring back-and-forth support. For ISVs, this architectural integration also creates consistency across modules, helping the product evolve with shared logic instead of patched reporting components. The approach emphasises this fusion, where intelligence becomes part of the application’s identity rather than an extension layered on top.

Self-Service Intelligence as a Catalyst for Reducing Tickets

Many reporting challenges happen because users can’t work with or understand their own data on their own. Without interactive intelligence, even small changes need engineering help. Self-service analytics flips this around. When users can create their own KPIs, filter by context, drill into behavior patterns, or explore anomalies without waiting for support, the reporting queue naturally shrinks. The value goes beyond fewer tickets, shaping how people experience the product.

A well-designed insight layer shows users that analytics is a tool they control. The principles of governed, augmented, and user-friendly intelligence support this. They ensure that autonomy and trust coexist. The ISV provides a structured space where customers can explore data safely and intuitively. This creates a balance: engineering teams focus on innovation, and users focus on understanding their world independently.

Unified Intelligence Accelerates Roadmap Delivery

When analytics lives on the edges of a product, every new feature sparks a wave of reporting requests, pulling teams away from building what really matters. Moving analytics to the center changes that dynamic. Shared models and consistent logic form a foundation that grows with the product, so enhancements flow through the insight layer automatically, cutting down custom reporting work.

Engineering teams gain clarity and focus, while delivery accelerates. Roadmaps become more predictable as teams focus on building scalable capabilities instead of handling ad-hoc data requests. A strong insight layer also illuminates how users actually interact with data, giving signals that guide the next steps in product development. This connection between insight, architecture, and user experience is where embedded, governed intelligence becomes a core advantage, unlocking both velocity and sustainable growth for ISVs.

Conclusion

ISVs that treat analytics as a support function limit their product’s potential. The impact goes beyond operations, influencing perception, adoption, and innovation. When analytics becomes a core layer, users gain control, engineering teams focus on building, and the product grows with architectural consistency. This shift accelerates roadmaps, deepens customer engagement, and creates a modern experience shaped by intelligence rather than static reports.

Smarten’s philosophy supports embedding analytics so applications show insights naturally. For ISVs aiming to boost product value and reduce reporting burdens, making intelligence a core part of the product is essential. It’s the path to resilient, insight-driven software that evolves with its users.

FAQs

1. How can ISVs shift from a reporting-driven model to a product-integrated analytics strategy?

By building a governed insight layer with embedded analytics, letting users explore data without waiting for reports.

2. Why does embedding analytics increase product adoption in enterprise environments?

Real-time insights within workflows keep users engaged and remove friction in decision-making.

3. What operational issues arise when analytics is kept outside the main product?

Engineering teams face recurring reporting requests, product consistency becomes harder to maintain, and roadmap delivery slows due to scattered dependencies.

4. How does Smarten support ISVs that want to modernize their analytics layer?

Smarten provides embedded, governed, self-service intelligence capabilities that integrate seamlessly into existing products, helping ISVs deliver insight-rich experiences without expanding support load.

Original Post: Why ISVs Must Stop Treating Analytics as a Support Function!