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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.

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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!

The Data Export Problem No One Talks About – And Why It Quietly Hurts Your Product

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January 15, 2026

Why Data Exports Quietly Undermine Product Value

Do your customers export data to Excel or Power BI every week? Do they build their own dashboards outside your product? Do they ask for CSVs even though you already offer reports inside the platform?

These signals look harmless. Many SaaS teams treat the export button like a helpful feature. It feels convenient. It feels simple. It feels like a nice-to-have option for power users.

Exports slowly weaken product stickiness. Every export creates a separate analytics layer that you do not control. Customers rely on these external dashboards for insights. Your product becomes a tool that sends data somewhere else for real decisions.

This problem matters in 2026 because users expect Insights inside the platform. They expect speed, context, and intelligence where they work. They no longer want to jump across tools for basic understanding.

Why Customers Export Data in the First Place

1. Your Native Analytics Aren’t Enough

Many products still offer basic or rigid reporting. Users hit limits fast.

  • Filters feel limited.
  • Dashboards feel slow.
  • Exploration feels locked down.
  • Custom questions need engineering support.

When insights take too long, users move their data into tools that feel flexible and fast.

2. Users Want Flexibility and Speed

Business teams prefer familiar workflows. They know Excel. They know Power BI. They know Tableau. These tools feel fast and independent. No engineering tickets. No waiting for custom reports. No bottlenecks.

3. Internal Stakeholders Demand “Their” Format

Each department expects reports in a format that fits their routine.

  • Leadership wants specific visualizations for weekly reviews.
  • Finance teams run their own ratio analysis.
  • Sales teams want funnel views in their language.
  • Operations teams expect granular tables.

Internal preferences push teams to export data and build Dashboards outside your product.

How Data Exports Create a Hidden “Shadow Analytics Layer”

1. Insights Leave Your Platform

Once your data lands in Excel or Tableau, all analysis happens outside. Teams make decisions without using your reporting features. Your platform loses mindshare in daily workflows.

2. You Lose Visibility Into What Customers Need

When users build external dashboards, you lose visibility. You cannot see their filters, metrics, or visual preferences. You cannot track their questions or pain points. You lose the insight needed to improve the roadmap.

3. Your Product Stops Being the Source of Truth

Users trust their Power BI dashboards more than yours. Your product becomes a data pipe. Their dashboards become the real reporting layer. When your product no longer informs decisions, it loses purpose.

4. Compounding Technical Risk

Exports create fragmented, outdated data that lives in multiple versions.

  • Old spreadsheets circulate.
  • Numbers do not match.
  • Reports lose accuracy.
  • Teams debate which file is correct.

The mess grows. You are blamed for inconsistencies even if the problem started outside your system.

The Churn Loop – How Export-Led Leakage Quietly Kills Stickiness

1. Reduced Daily Active Usage

Users spend more time in external BI Tools than in your product. Daily active usage drops. Engagement drops. You lose opportunities to show value.

2. Your Value Proposition Shrinks

Customers say they rely on their own dashboards instead of yours. Your analytics features feel basic. Your product feels less strategic.

3. Procurement Questions Your Pricing

Procurement teams ask why they should pay full price if most analysis happens elsewhere. Your value becomes harder to defend. Renewal cycles feel tense.

4. Churn Becomes Rational

Once external BI dashboards take over, switching becomes simple. A competitor only needs to replace workflows. They do not need to replace reporting because reporting lives outside your product.

External BI Tools Create Hidden Costs for Your Customers

1. Manual Refreshes and Maintenance

Teams spend hours updating spreadsheets. They merge exports. They clean data. They repeat this every week or every month. The process slows everyone down.

2. IT and Compliance Risks

Uncontrolled spreadsheets and rogue dashboards create risk.

  • No access control.
  • No governance.
  • No audit trail.
  • No data lineage.

Shadow reporting invites security issues.

3. Slow Decision-Making

Exports are snapshots. They are never real-time. Teams rely on outdated information. Decisions slow. Errors appear. Opportunities are missed.

Why Embedded Analytics Stops the Leakage

1. Keeps Users Inside Your Product

Embedded Analytics gives users what they need without leaving the platform.

  • Rich reporting.
  • Drilldowns.
  • Predictive insights.
  • Exploration tools.

Your product becomes the place where answers live.

2. Turns Your Product Into a Decision-Making Hub

When Insights live inside your platform, users build habits. Daily usage grows. Your product supports real work, not only transactions.

3. Eliminates Version-Splintered Dashboards

Embedded Analytics centralizes reporting. Everyone sees the same numbers. One dataset. One metric definition. One clear source of truth.

4. Gives You Visibility Into What Customers Value

You can see which dashboards matter. You can see what users click. You can see what they search for. You can build features with confidence, not guesswork.

5. Reduces Support Burden

You reduce complaints about mismatched numbers. You reduce export failures. You reduce confusion about metrics. Support teams spend less time debugging spreadsheet problems.

What Modern Embedded Analytics Needs (Beyond Just Charts)

1. Low Code or No Code

Business users need freedom to build their own Dashboards. Your product grows when users feel independent. They should not need technical help for every report.

2. Smart, Automated Insights with Augmented Analytics

Modern Analytics must go beyond charts.

  • Automated explanations.
  • Anomaly detection.
  • Predictions and trends.
  • Contextual insights.

These features reduce manual effort and give users faster answers.

3. Real World Business Scenarios

Users expect templates that match their industry. They expect ready workflows for retail, manufacturing, insurance, wellness, government, utilities, and more. Templates reduce configuration time and increase adoption.

4. Collaboration and Governance

Teams need governance, access control, sharing rules, and audit trails. Embedded analytics works best when data flows safely across the company.

Wrapping Up

Products lose value when insights leave the platform. Data exports feel simple, but they drain engagement. They increase maintenance work for your customers. They create multiple sources of truth. They shift decision-making to external tools. They make churn easier.

Embedded Analytics helps you protect stickiness. Users stay inside your platform. Teams make decisions faster. Your product becomes central to their workflow. You gain insight into what customers value and how they think.

Smarten supports this shift with a low-code and no-code analytics platform. The platform includes Augmented Analytics and BI Tools designed for business users. Teams use Smarten to answer real business questions. We solve problems like quality issues, maintenance delays, customer targeting, marketing optimization, and financial analysis. We combine internal and external datasets to study trends and forecast results.

Smarten helps companies in retail, pharmacy, wellness, insurance, financial services, manufacturing, government, public sector, utilities, and many other industries. The platform improves collaboration between business users and IT teams. Data stays consistent. Access stays controlled. Sharing becomes easy.

You do not have to manage this transition alone. The Smarten team supports each step with workshops, webinars, and structured programs that help you launch a Citizen Data Scientist initiative. You get data governance guidance. You reduce training time. You increase adoption with minimal effort.

Contact Smarten Today to bring embedded analytics into your product, stop data leakage, and build a platform your customers rely on every day.

FAQs

1. Why do customers export data even when a product has built-in reports?

Most teams want faster, flexible analysis, so they move data to Excel or Power BI where they feel more in control.

2. How do exports weaken product stickiness?

Once analysis shifts outside your platform, users stop relying on your dashboards and spend less time inside your product.

3. How does embedded analytics fix the export problem?

It keeps insights inside your product with flexible reporting, smart recommendations, drilldowns, and real-time data.

Original Post: The Data Export Problem No One Talks About – And Why It Quietly Hurts Your Product!

Embedding Analytics with Flexibility: How Smarten Helps ISVs Navigate Licensing & Deployment

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January 13, 2026

Flexible Embedded Analytics for Modern ISVs

Independent Software Vendors (ISVs) face pressure to add analytics inside their applications. Customers expect insights inside the product they already use. ISVs want a simple way to deliver this without breaking their pricing or licensing structure.

The problem often starts with the BI platform they choose. Most BI Platforms follow rigid licensing models. These models force ISVs to change how they price their own products. ISVs work with customers across different industries, timelines, and contract histories. No two customers follow the same licensing pattern. A rigid BI model creates friction and slows adoption.

Flexible licensing and deployment matter. ISVs need a BI partner that adapts to them. Smarten gives ISVs the ability to Embed Analytics without changing how they sell or deploy their software. This article explains why flexibility is important and how Smarten helps ISVs align BI with their customer base.

1. The Licensing Realities ISVs Deal With

ISVs handle a mix of licensing models. These models shape how customers use the software and how they pay for it.

Common Licensing Models in ISV Software

  • Perpetual license: A one-time purchase. Customers get the right to use a specific version of the software indefinitely.
  • Subscription license: Customers pay for access on a monthly or yearly cycle. This model fits SaaS applications.
  • Concurrent license: A shared pool of licenses. A limited number of users log in at the same time.
  • Named user license: A specific number of individual users. Each user is identified and tied to a license.

Why ISVs Need to Support Multiple Licensing Combinations

ISVs do not follow a single model. They support many licensing patterns because their customers expect flexibility.

Points to consider:

  • Legacy customers were sold on one model years ago.
  • New customers prefer different models.
  • Some customers mix license types across departments.
  • Some customers buy additional users over time in small increments.
  • Licensing choices affect how much value the system delivers.

ISVs need BI to align with the same combinations. If BI has a rigid model, the ISV faces pricing conflicts. These conflicts reduce BI adoption and increase pressure on sales teams.

2. The Core Problem? BI Licensing Does Not Align with ISV Licensing

ISVs embed BI into their application. To create value, BI should reach all or most users. When BI licensing is restrictive, adoption drops, and user experience weakens. The ISV loses the ability to scale analytics.

BI Must Be Rolled Out to All Application Users

Analytics works when everyone in the application has access. When only a few users get BI, The Insights stay isolated. Product value goes down. ISVs need BI to follow the same user access patterns as the main application.

Problems arise when:

  • BI charges per named user while the ISV sells concurrent access.
  • BI requires a separate license for different types of analysis.
  • BI pricing escalates as the customer grows.
  • BI forces users into fixed BI roles that do not match the ISV’s user categories.

These conflicts break the embedded experience.

Example: The Concurrent vs Named User Conflict

An ISV has a customer with:

  • 500 named application users
  • A 20-user concurrent license to use the ISV application

This customer runs the ISV application with a concurrent model. Only 20 users access the system at one time. The other 480 users are registered users, but do not log in at the same time.

The ISV wants to sell embedded BI. The BI vendor only supports named user licenses.

Questions arise:

  • Should the ISV sell 500 BI named user licenses, even though only 20 users log in concurrently?
  • Will the customer accept BI priced by named user when their original application is priced by concurrent usage?
  • What happens when the customer adds 50 more named users next year?
  • What happens when usage patterns change but concurrency stays within the existing limit?

If the ISV tries to sell BI under the named user model, the cost becomes too high. The customer refuses. The ISV fails to upsell analytics. The BI licensing model becomes a barrier instead of an enabler. This pattern is common across markets.

3. Another Common Barrier? Mapping ISV User Types to BI User Types

ISVs have different user roles inside their application. A human resources module has different users from a finance module. A compliance workflow has different users from an operations workflow.

ISV Application User Types

Examples:

  • Reporting users
  • Data entry users
  • Module-specific users such as HR, Finance, Operations
  • Occasional users
  • Heavy users

BI Platform User Types

BI platforms define their own roles.

Examples:

  • Viewer users
  • Power users
  • Data prep users
  • Admin users

The Mapping Challenge

ISVs face questions during user role mapping.

  • How do you map a data entry user to a BI role?
  • How do you price BI for a module user who views only two dashboards?
  • How do you avoid overlicensing?
  • How do you prevent confusion during sales conversations?

When BI roles do not match ISV roles, pricing becomes unpredictable. Customers refuse BI upsell offers because the BI role model does not match their application usage. The ISV ends up with a “one size fits all” structure that no customer accepts.

4. Why Flexible BI Licensing Is Critical for ISV Success

Flexible BI licensing gives ISVs full control over their product strategy. ISVs grow faster when BI works like their own application.

Key benefits:

  • BI reaches all important workflows.
  • The ISV uses its own pricing model without force-fitting a new one.
  • BI adoption improves because customers understand the logic.
  • Sales teams feel confident Offering Analytics to any customer.
  • Customers avoid sudden cost jumps as they grow.
  • User management stays simple for both the ISV and customer.

Flexible licensing helps ISVs avoid “NO GO” situations. These situations happen when the customer rejects BI due to cost or structural mismatches. ISVs can preserve their relationship with customers and improve long-term value.

5. How Smarten Solves These Licensing Challenges for ISVs

Smarten is built with ISV flexibility at the center. ISVs embed Smarten into their application and manage licensing without friction. Smarten supports the ISV’s business model instead of forcing a new one.

Flexible Licensing That Mirrors ISV Realities

Smarten supports multiple licensing models, such as:

  • Perpetual
  • Subscription
  • Concurrent
  • Named user
  • Hybrid structures
  • Revenue share

ISVs align Smarten licensing with their existing structure. This reduces negotiation time. This reduces customer confusion. This gives the ISV a direct path to offer analytics to users at scale.

Custom User Role Alignment

ISVs have control over user role mapping.

Smarten lets ISVs:

  • Align BI roles with application roles.
  • Define access by module or function.
  • Hide BI complexity behind a simple user model.
  • Bundle analytics with application tiers such as basic, standard, and premium.

This gives ISVs clarity and control. Customers understand what they are paying for. BI becomes a natural extension of the existing product.

Scalable BI Rollout Across All Users

Smarten removes the need for fixed named user models. ISVs roll out BI to hundreds or thousands of users without cost spikes.

Benefits include:

  • Stable cost per customer.
  • Add pont of cost vs revenue generator.
  • Predictable pricing for multi-year contracts.
  • Flexibility to support multiple departments in the same customer account.
  • No lock-in to rigid BI user counting.
  • Growth without renegotiation.

Zero Friction Upselling

ISVs offer analytics to existing customers with confidence.

Smarten eliminates common blockers:

  • No pricing mismatch between BI and ISV application.
  • No forced per-user pricing restrictions.
  • No complex role mapping that pushes customers away.

ISVs choose how they want to sell BI. They control margins and deal size. Customers accept analytics because the structure aligns with their current contract.

6. Flexible Deployment Options That Remove Complexity

Licensing is not the only area where flexibility matters. Deployment is equally important.

Multiple Deployment Choices

Smarten supports:

  • On premises
  • Private cloud
  • Public cloud
  • Hybrid setups

Why This Matters for ISVs

Different customers have different infrastructure requirements. ISVs need a BI platform that adapts to each customer.

Key benefits:

  • Alignment with customer IT policies.
  • Support for strict security requirements.
  • Smooth integration with existing systems.
  • Faster proof of concept delivery.
  • Lower cost of ownership for the customer.

Smarten gives ISVs the freedom to match deployment preferences without building custom BI environments from scratch.

7. Real World Impact: What ISVs Gain with Smarten

Smarten helps ISVs deliver analytics that fit their customers without operational stress.

ISVs gain:

  • Faster integration of embedded analytics.
  • Predictable pricing aligned with customer expectations.
  • Support for customers with any licensing history.
  • Higher adoption because users access analytics without restrictions.
  • Stronger customer retention through added value.
  • Full control over how analytics is packaged and sold.
  • Freedom to innovate without BI vendor limitations.

Wrapping Up

ISVs need a BI partner that adapts to their business model. Smarten offers flexible licensing, user alignment, and deployment structures. This helps ISVs avoid customer resistance, improve analytics adoption, and scale their product strategy.

Smarten supports ISVs with a modern embedded BI platform designed for flexibility. Reach Out To The Smarten Team to explore how embedded analytics can align with your licensing model and deliver value to every user in your application.

FAQs

1. Why do ISVs need flexible BI licensing?

It keeps BI aligned with how they already sell their software, which makes adoption easier for customers.

2. What if my customers use different license types?

Smarten fits into any mix, so you do not need to change your pricing or user structure.

3. How do I check if Smarten is right for my product?

Reach out to the Smarten team and walk through your licensing model and embedded BI needs.

Original Post: Embedding Analytics with Flexibility: How Smarten Helps ISVs Navigate Licensing & Deployment!

AI-Infused Apps and Products Provide Improvement Across the Enterprise

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January 12, 2026

Why Infuse New or Existing Software with Artificial Intelligence?

Artificial Intelligence (AI) has been in the mainstream for years with new advancements occurring every day. As the promise of AI evolves, businesses are undertaking new AI software development projects to support consumer products and internal business processes, and many companies are infusing AI into existing software products and apps to modernize and upgrade features and functionality, scalability and the quality of system interaction with human users.

Gartner predicts that by 2026, around a third (30%) of new applications will leverage AI.

‘By leveraging AI, businesses can tap into its numerous benefits and advantages, positioning themselves for continual success and growth.’

On the back-end, the benefits of AI development include improved speed of development and time to market. According to McKinsey research, Generative AI Accelerates Certain Coding Tasks By 35% To 45%.

Infusing AI allows you to incorporate the advantages and advanced technologies of the AI landscape into the enterprise to automate, predict and optimize and to improve business decisions and outcomes, as well as improve user adoption of intuitive tools and features.

Infusing AI into the environment also allows you to more quickly adapt and upgrade to accommodate changes to customer needs, business user needs and the competitive market. Development can be completed quickly and new, more sophisticated features can be added on a shorter cycle. You can develop faster and design and deliver software and applications to create dynamic end-user experiences.

There are real business benefits to adding AI into the mix! The data gathered and delivered by AI can provide real-time results. For example, a decision regarding loan approval should be based on currently available information and the transaction should be approved or denied using the most up-to-date information in order to protect the interests of the prospective client and the business. Interest rates and other variables can be calculated to provide the best outcomes. The system can detect and protect against fraud to ensure that insurance claims are valid and appropriate.

By infusing existing systems with artificial intelligence or building new systems to incorporate these capabilities, the business can mitigate risk, speed business processes and approvals and automate many redundant tasks, thereby improving team productivity, customer satisfaction and competitive advantage in the market.

Does an AI-Infused App or Software Product Benefit a Business and Its Customers?

By leveraging AI, businesses can tap into its numerous benefits and advantages, positioning themselves for continual success and growth. Benefits of AI-infused technologies include:

Competitive Advantage – Businesses can secure an edge over their competitors through the use of AI technologies that amplify productivity, decision-making processes, and consumer experiences.

Personalized User Experience – AI technology allows businesses to effortlessly examine consumer behavior and preferences, enabling the provision of products, recommendations, and services tailored to their precise needs.

Data-Driven Insights – AI-based solutions help businesses forecast business trends, identify patterns, and make informed decisions by analyzing vast amounts of data and extracting invaluable insights, paving the way for better planning.

Operational Efficiency – By automating repetitive tasks, streamlining workflows, and optimizing resource allocation, AI-driven software helps businesses simplify their operations, improve productivity, and reduce costs.

‘Infusing AI allows you to incorporate the advantages and advanced technologies of the AI landscape into the enterprise to automate, predict and optimize and to improve business decisions and outcomes, as well as improve user adoption of intuitive tools and features.’

Contact Us to find out how our Enterprise Software Services and Artificial Intelligence Development Services can support your business needs and ensure consistent, professional, skilled technology and knowledge. Explore our free White Paper: ‘What Is Artificial Intelligence Technology And How Can It Help My Business?’

Original Post: AI-Infused Apps and Products Provide Improvement Across the Enterprise!

Embedded BI: What Makes a User Adopt BI Tools?

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January 7, 2026

Embedded BI: Productivity vs. User Needs

Executives and managers may want to believe that staff and team members will always embrace and adopt new tools and processes just because they are told that these tools or processes will help the company. But this belief does not take into the consideration the pressures, stress and day-to-day realities a team member faces.

While the best of our team members may truly wish to support the company and have the best interests of the business in mind, even THEY are subject to the pressures of getting the job done, dealing with conflicting opinions and direction from other team members and from managers and just getting through the day!

When an IT team, executives and managers come together to choose the right business intelligence (BI), and augmented analytics tools, they often fail to consider ease-of-use, existing business processes and technology and user and team member skills and preferences.

‘When considering BI tools and self-serve analytics, it is wise to consider embedded BI solutions.’

As an executive or manager, your primary goal is to support the objectives of the business and to get the most out of the skills, knowledge and time of your team members. But, if you don’t provide the right tools and acknowledge the issues that impact productivity and team satisfaction, you will not achieve your goals.

A recent Forbes Article includes statistics from studies that include surveys of employees and the results are eye-opening:

  • 40% of those surveyed feel their daily responsibilities have changed to a large extent within the past year.
  • 78% rank flexibility as key to choosing a job.

So, if you want to recruit and keep the best team members, you need to provide the most efficient, productive, intuitive technology and tools.

When Considering BI Tools and Productivity, Don’t Forget the Needs of Your Team

When considering BI tools and self-serve analytics, it is wise to consider embedded BI solutions. These solutions allow your team to leverage familiar, existing software and solutions with integrated analytics. There is no need to migrate or move information from one system to another in order to perform analysis, and your team can use a single sign-on to access popular software and work in a way that is familiar and meaningful to them – all while having access to intuitive, self-serve BI tools that enable quick, clear analysis of data.

Benefits of Embedded BI

  • Improved user adoption
  • Reduced TCO
  • Improved ROI
  • Integration APIs provide intuitive, self-serve BI tools from within enterprise applications and public websites
  • Users have access to facts, data and business insight

When surveyed, businesses using embedded BI reported the following results:

  • 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
‘When an IT team, executives and managers come together to choose the right business intelligence (BI), and augmented analytics tools, they often fail to consider ease-of-use, existing business processes and technology and user and team member skills and preferences.’

If an enterprise includes team member satisfaction and user adoption concerns when it considers Business Intelligence (BI) and Augmented Analytics requirements, it can address productivity, employee optimization and user satisfaction. Adding Embedded BI to the technology landscape will ensure adoption of crucial software and business processes to improve business results and allow team members to leverage data, and capitalize on technology investment, while improving the quality of decisions and making the most of the knowledge, skills and time of its employees.

You can find out more about the Smarten Embedded BI And Integration APIs solution and add powerful functionality and access to existing ERP, SCM, HRMS, CRM or any other products. Provide analytics capabilities within existing products without major Investment. Your business users and your customers will appreciate the ease-of-use and access and you will gain a competitive advantage. Read our White Paper: ‘Making The Case For Embedded BI And Analytics.’

Original Post : Embedded BI: What Makes a User Adopt BI Tools?

Which BI or Analytics Tool is Best for My Business?

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January 5, 2026

Traditional BI, Augmented Analytics, or Modern BI/Analytics?

Whether your business is planning to acquire a business intelligence (BI) or augmented analytics tool for a first-time installation or looking to upgrade an existing system, the decision starts with the ‘what’ of the solution. What kind of solution will best fit your needs for infrastructure, integration, user requirements, pricing, upgrades etc. and what kind of approach, features and functionality will be most suitable for your business.

‘When considering a new or upgraded business intelligence or advanced analytics solution, it is important to look at your enterprise requirements to accommodate existing technologies and infrastructure, business processes and models and user needs.’

A Recent Study reveals a number of considerations to be included in your choice of BI tools and analytical platforms, including:

  • Zero footprint: 100% web-based, no client-side installs or downloads required
  • Support for role-based reporting and dashboards
  • Support for mobile Business Intelligence
  • Communication features like notes, comments, and likes
  • Updates in real-time: your dashboard is always up to date
  • Basket, advanced and predictive analysis
  • Support for machine learning and generative AI
  • Usability: ease-of-use and ease-of-learning
  • Insights (graphs, definitions, et cetera) that are reusable across BI applications

While this study carves out what it considers to be the optimal analytics framework and platform, some of all of these considerations may not apply to your business needs.

What Do I Need to Consider When Choosing BI Tools and Analytics Solutions?

When reviewing the various iterations of analytics frameworks and platforms, you will need to understand the following capabilities and structures. Understanding how each of these approaches works and what advantages and limitations they include will make it easier for you to choose a solution and approach to suit your needs.

Augmented Analytics

Today’s augmented data analytics incorporates artificial intelligence (AI) and machine learning (ML) to streamline, automate and improve the process of data analysis, so business users can more easily gather, interact with, select and report on data, simplifying data prep, insight and collaboration and allowing your team to explore data in a way that is meaningful to them.

Augmented analytics uses machine learning, natural language processing (NLP), and algorithms to easily analyze and manipulate large datasets and reduces the time needed for processing, thereby allowing business users to leverage analytics in their day-to-day workflow and freeing Data Scientists to perform more strategic tasks.

Benefits of Augmented Analytics

  • Streamlined data prep and analytics
  • Optimizes business user and data scientist time and effort
  • Faster, more insightful decision-making
  • Increased operational efficiency
  • Democratized data analytics

Traditional Business intelligence

Traditional BI Tools use conventional analytical techniques and technologies to collect, analyze and present data. These systems typically rely on a centralized data source and a selected set of formatted reports. Users leverage data extraction, transformation and loading tools (ETL) to extract data from a data warehouse and online analytical processing (OLAP) tools to analyze data. Reports and visualization are typically delivered through a dashboard with standard key performance indicator (KPI) software to monitor metrics.

Benefits of Traditional BI Tools

  • These tools provide support for a range of data types and data sources
  • IT and/or data scientists can create standardized reports and models to address business and user needs
  • The enterprise can maintain and control data sources on-site

Modern BI Tools and Augmented Analytics

When an organization integrates the use of modern BI tools with more advanced augmented analytics it can leverage artificial intelligence and traditional tools to satisfy the needs of a diverse set of users, business units, etc. Modern Business Intelligence technologies integrates more sophisticated features to bring analytics to all users and allow them to choose the tools that best suit their needs in a self-serve environment with automated data preparation, fast and easy data access and insight, Natural Language Processing (NLP) that allows users to ask questions using common language and receive answers using visualization techniques.

Benefits of Modern BI Tools and Augmented Analytics Combined

  • Sophisticated self-serve analytics suitable for business users with limited technical skills
  • Real-Time insights and reduced time-to-decision
  • Monitoring and managing of trends, anomalies and other factors that impact results
  • Collaboration and data democratization with unique tools to suit every user
‘When reviewing the various iterations of analytics frameworks and platforms, you will need to understand the following capabilities and structures. Understanding how each of these approaches works and what advantages and limitations they include will make it easier for you to choose a solution and approach to suit your needs.’

When considering a new or upgraded business intelligence or advanced analytics solution, it is important to look at your enterprise requirements to accommodate existing technologies and infrastructure, business processes and models and user needs.

Contact Us to discuss the unique needs of your organization and your users and find out more about Smarten Technology. The process of choosing the right Analytics Solution for your busine 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 : Which BI or Analytics Tool is Best for My Business?

Smarten Support Portal Updates – December – 2025!

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December 30, 2025

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 : Data Operations
  2. GeoMap : Workflow to create Default geomap
  3. Crosstab : Making use of Cell references

Predictive Use cases

  1. Assisted predictive modelling : Medical Cost Prediction Using Smarten Assisted Predictive Modelling
  2. Assisted predictive modelling : Hypothesis : One-Way Anova test using Smarten Augmented Analytics

Business Use Cases

  1. Business Scenarios : Calculating Pending Completion Months for an Ongoing Project
  2. Business Scenarios : Find out the frequency of repetition of dimension value combinations – e.g. frequency of combination of bread and butter from sales transactions

Manuals and Technical Guides

  1. Smarten User Manual SSDP
  2. Smarten Working with Sentiment Analysis
  3. Smarten Dashboard Design Best Practices

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 – December – 2025!

Build Your Software Team (A Dream Team) At a Price You Can Afford

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December 24, 2025

Building a Software Team That Suits Your Needs

Imagine having the resources you need to satisfy your software development, support and maintenance needs? Imagine a universe where you don’t have to recruit, interview and house a software development team to fulfill your vision for a consumer or business application? What if you could have the right resources available at the right time without having to wait for your team (or an IT partner) to find the time to address your needs or ideas?

‘A Build Your Own Team approach assures that the business has the skills, knowledge, resources and access it needs to complete its project on a timely basis. This personalized approach is not simply a staffing, recruitment or shared workspace approach.’

CIO.com recently published research that outlines the primary reasons for project failure. Among the top reasons for failure, they list two primary factors relating to resources:

Not Enough Resources

Underestimating the resources and talent required to do the work and the amount of work required to complete the project.

Inexperienced Teams

Failure to provide experienced project managers and specific skills and talents required to complete the project.

Even if your business has a skilled in-house IT team, it is likely that they are already overwhelmed by existing projects, support, and maintenance of the existing infrastructure. Add to that the fact that new technologies like Artificial Intelligence (AI), and new platforms, frameworks, coding techniques, and other factors can make it nearly impossible to keep your staff trained and sustain the skills you need to address every requirements.

But when your business needs software and application development help, it is often difficult to find a service provider that will satisfy all business needs and do so in a timely, skilled fashion. If a business chooses the wrong service provider, it is likely to face one or more of the following issues:

  • Cookie-cutter solutions and teams that provide some, but not all, of the skills required to support business needs.
  • Delays in achieving milestones because the right resources are not available at the right time.
  • Inflexible team and resource availability to address changing or evolving requirements leads to uncertain results.
  • Out-of-date or incomplete skillsets.
  • The need to find, interview and select a vendor, sign contracts and establish relationships for each new project.
  • Resource instability. Changes and disruption in the team demand re-training, and result in lost resource skills and project knowledge.
  • Failure to protect Intellectual Property (IP), Data and User Privacy.

And even if you CAN find a vendor or partner with the right skills, many businesses report delays and gaps in the actual delivery of completed projects. A skilled developer may be pulled from your project to satisfy another client and, when it is time for that developer to work on your project, they may not be available. You may find that the vendor is unavailable on certain days or during certain hours because of time zone differences or project overload.

Build Your Software Team Without Investment and Get the Right Skills and Resources

A Build Your Own Team approach assures that the business has the skills, knowledge, resources and access it needs to complete its project on a timely basis. This personalized approach is not simply a staffing, recruitment or shared workspace approach. By combining experience in software development and project management, and a unique understanding of the software lifecycle, training and skills requirements, with a deep and broad network from which the partner can draw you are assured of the right skills, experience and talent – without delays or default.

‘When your business needs software and application development help, it is often difficult to find a service provider that will satisfy all business needs and do so in a timely, skilled fashion.’

Contact Us to find out how our software application development services and find out how you can Build Your Software Team to support YOUR needs and ensure consistent, professional, skilled technology and knowledge. Explore our free articles here: ‘Build Your Software Team For Cross-Platform Apps Project,’ and ‘Ensure Success And Engage A Software Partner To Build Your Own Team.’

Original Post: Build Your Software Team (A Dream Team) At a Price You Can Afford!