Is Your Analytics Strengthening or Masking Your Product

Every Financial Year End, I ask ISV Founders the Same Question

Every financial year-end, I ask ISV founders the same question: Is your Analytics strengthening your core product or quietly compensating for what it cannot do on its own?

Most founders pause when I ask that, and the pause itself tells me a lot more than their answer.

In this blog, I am going to walk you through the patterns I keep seeing during FY planning cycles, what it actually looks like when analytics strengthens a product versus when it masks one, and what ISV teams can do differently this year to turn analytics into a genuine competitive advantage rather than an expensive workaround.

The Pattern I Keep Seeing During FY Planning

During FY planning cycles, the founding team is usually proud of their Reporting Dashboard, their users seem to like it well enough, and on paper, the feature looks like a value-add that justifies a premium tier or a longer contract.

But dig one level deeper and a different picture emerges: the analytics layer is patching gaps in decision support that the core product was always meant to provide in the first place.

Here is what that typically looks like in practice when I am walking through a product roadmap with a founder:

  • Analytics substituting for product logic: When users rely on a separate reporting module to understand what their core workflow should already be surfacing, that is a design problem, and it grows more expensive every quarter you leave it unaddressed.
  • Reporting demand outpacing product evolution: When teams have a backlog of custom report requests from a single enterprise client, there is a clear indication that the product is not answering the questions users have from day to day.
  • Analytics used as a sales bandage: Some teams bolt on a reporting layer just before a renewal or upsell conversation, hoping it adds enough perceived value to close the deal. However, such an approach rarely survives because the underlying product gaps remain unchanged and visible.

What It Actually Means to Strengthen Your Product with Analytics

Strengthening your product with analytics means that the insights layer is not just a line item on your must-have features but is woven into the actual decisions your users make while they are inside your platform doing their work.

What I have seen work well is when ISV teams integrate augmented analytics directly into their product’s decision flow, so users are never forced to leave their workflow just to understand what is happening in their data.

This is where low-code/no-code tools change the economics for ISVs. Embedding analytics capability into a product no longer requires hiring data scientists, maintaining a separate data engineering team, or investing in a multi-year platform overhaul.

That assumption made sense about eight years ago, but it is genuinely outdated today. ISV founders who are still operating from that mental model are consistently underinvesting in an area that their competitors are quietly turning into an advantage.

What platforms like Smarten have made possible is a fundamentally different implementation model where a business user can create a working predictive model in under five minutes, build dashboards without scripting, and share insights across teams without any of the traditional overhead. Here’s how the Citizen Data Scientist model changes everything:

  • Speed of implementation as a strategic edge: When your team can deploy a fully functional analytics layer rapidly using a low-code platform instead of spending eighteen months building one from scratch, that time advantage compounds directly into faster product iteration cycles and earlier feedback loops with enterprise clients who want to see real capability, not roadmap promises.
  • Reduced training burden across the organization: When onboarding a new analytics feature requires multiple instructor-led sessions per user cohort, that cost quietly eats into your implementation budget in ways most ISV teams never account for correctly in their planning models — and the auto-suggestion and recommendation features in modern augmented analytics platforms eliminate almost all of that overhead before it becomes a problem.
  • Democratized access without governance risk: Giving broader user populations access to analytics does not have to mean compromising on data security, because the right platform lets you implement appropriate governance structures from within so that data democratization and data security coexist rather than compete — and that balance is what makes enterprise clients comfortable enough to actually roll it out organization-wide.

Turn Analytics into a Competitive Advantage

When I ask founders at the start of every fiscal year whether analytics is strengthening or compensating for their product, I am not trying to judge or scare anyone. What I am really asking is whether the product they are building is one that users would choose because of its analytical capability or one that users are managing to use despite its analytical limitations.

The founders who answer that question honestly and then act on the answer during the FY planning cycle are consistently the ones I see growing their accounts, increasing their average contract value, and turning analytics into a genuine competitive advantage rather than a checkbox on an RFP response.

Platforms like Smarten exist precisely to make that transition achievable for Citizen Data Scientists without requiring ISV teams to overhaul their architecture, hire a data engineering team, or delay a product release to build something from scratch that is already available as a well-integrated, enterprise-ready solution.

Curious to learn more? Learn How we helped a municipal corporation support a smart city initiative with analytics. Or Speak To Our Experts to transform your product.

FAQs

1. Why should analytics be embedded into software products?

When analytics is embedded into products, users make better decisions faster as insight appears at the exact point of action inside the product they are already using, rather than in a separate tool they have to switch to, log into, and manually reconcile with their workflow.

2. How does the Citizen Data Scientist model benefit ISV products specifically?

It allows ISV products to deliver embedded, Self-Serve Analytics directly to end users across the enterprise, which increases product stickiness, reduces support overhead, and turns analytics from a feature into a retention driver.

3. Can a business user without coding skills actually build predictive models with platforms like Smarten?

Yes — Smarten’s no-code platform enables business users to create working Predictive Models in under five minutes without any data science background, coding ability, or prior BI training required.

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