Connor Quinn  — September 6, 2025

To my mind, the shift of focus to data products in recent years is a response to the fact that very few businesses feel they are being well served by their data. Data becomes a costly and time consuming effort that is rarely at the heart of how the business makes decisions.

Adopting a data product mindset aims to shift the focus to what will be most valuable to users. Making decisions about how best to manage, clean, curate, and serve data becomes simplified when the users are treated as the highest priority.

Despite this, it can be difficult for teams to break out of established ways-of-working without some extenal support. At Kelvin Analytics, we can partner with your data leaders and delivery teams to establish data product thinking and ways of working that your teams can carry forward.

The importance of Data Product Owners

A common pitfall when building data products is to skip the step of agreeing a data product owner. Usually that is because the existing org structure doesn’t have a role fitting this description and so instead, the job is fobbed off to someone already up to their eyeballs with work. Finding the right person to engage with the business is hard, and so there is a temptation for the data teams to name a data product and then send it out into the world without clear ownership.

This is a mistake.

The result of this omission will be that data teams make well-intentioned but poorly informed decisions about the data, while the business becomes increasingly removed from the data. Back to the common pattern of the data team working hard but the business being left unsatisfied.

The data product owner should sit between the business and the data teams, guiding priorities according to what will add most value for users. Their overriding concern should be what will make our users happy. They are accountable not only for the delivery of the product, but also for publicising it, making it discoverable, monitoring its use and performance, and working to align the semantics across the wider business.

One final key aspect of the role is knowing when to retire a data product. By shifting to a data product mentality means that we should have clear vision of how well the product is being used by the business. When the product stops justifying its existence, the product owner should decide to turn it off. Finally your data can have a lifecycle where the burden does not only ever increase.

Learn more about how we can support your data strategy

Check out this excellent blog post: Building high quality data products.

Connor Quinn  — September 2, 2025

Every company aims to be data-driven. But in reality, many machine learning projects get stuck in development and never help to support the business. Failure to deliver ML projects to product leads to wasted investment, frustrated teams, and missed opportunities to deliver real business value.

What is MLOps — and Why It Matters

MLOps is all about taking the principles of DevOps and applying them to machine learning. It covers the whole lifecycle:

• Tracking experiments and models
• Automating training and deployment
• Monitoring performance and drift
• Managing governance, security, and compliance

Without MLOps, ML projects often stall. Different teams build their own workflows, model versions get lost, and no one knows which model is actually running in production. Worse still, compliance and governance gaps can leave companies exposed to risk.

With MLOps, your ML pipelines become repeatable, reliable, and auditable. You can trust your models, your teams can collaborate, and your business can actually capture value from AI.

Why Databricks is the Best Platform for MLOps

Plenty of tools promise to help with MLOps. But Databricks has a unique advantage: it brings data engineering, analytics, and machine learning together on one platform.

• Unified Lakehouse Platform – One place for raw data, clean data, and ML-ready features. No more juggling multiple platforms.
• MLflow integration – Experiment tracking, model versioning, and deployment are built right in. No bolted-on extras.
• Unity Catalog governance – Centralised security, lineage, and compliance across all your data and models.

Real Benefits for Your Business

What does all of this mean in practice? When you get MLOps right on Databricks, you see benefits across the board:

• Faster time to production – Models move from notebook to endpoint quickly and consistently.
• Lower costs – Efficient pipelines and scalable compute reduce wasted spend.
• Governance and trust – Unity Catalog ensures compliance and full visibility into your models and data.
• True collaboration – Data engineers, scientists, and business stakeholders can finally work together in one environment.

These aren’t just technical wins. They’re business wins. Faster insights. Lower risk. Clearer value.

How to Get Started

Getting started with MLOps on Databricks doesn’t need to be overwhelming. Here’s a simple roadmap:

1. Review your current workflows
   Where do models currently live? How are they deployed? What breaks most often?

2. Identify the gaps
   Look at reproducibility, governance, monitoring, and cost. Where are the risks?

3. Leverage the platform
   Use Databricks features like AutoML, MLflow, Delta Live Tables, and Unity Catalog to close those gaps.

4. Bring in expertise
   Partner with specialists who’ve solved these challenges before. It’s the quickest way to avoid costly mistakes.

Why Work With Us

At Kelvin Analytics, we’ve helped teams move from “proof of concept” to production-ready ML on Databricks. We know the pitfalls, the shortcuts, and the best practices that really work in the real world.

We don’t just drop in tools and leave. We help you design robust architectures, build reliable pipelines, and upskill your team so they can confidently evolve with the platform.

Get in touch for a chat to explore how we can help your business succeed.

Connor Quinn  — September 1, 2025

Your business stakeholders don’t care about your data pipelines. They just want to succeed. Data value propositions ensure that the hard work of data teams actually drives success.

Managing data is complex and challenging. As data teams, the technical challenges can often distact our focus from the real goal of data - driving value for the business. Too often we encounter data teams who are working flat out to deliver data, but with unsatified stakeholders. This is usually the result of an ever increasing burden of deliverables with no obvious way to prioritise between them.

One of the key tools we use in Kelvin Analytics are data value propositions. A data value proposition is a clear and concise statement that communicates how this data will bring value to the business.

To generate a set of data value propositions, we run a sequence of structured workshops with business teams to understand what decisions they need to make and how data could inform these decisions. We help the users of the data to articulate the value of data in a way that data teams can understand. For example:

By segmenting customers based on their likelihood of making repeat purchases, we can target our marketing more effectively.

These propositions provide a guiding star for data teams who are swamped with complexity and competing demands.

Once we have a clear picture of how the data would be used, we can refine and prioritise these data value propositions according to effort, impact, and other considerations to inform a delivery roadmap that is ambitious for customers while also allowing incremental delivery.

If you want support to get your data roadmap tighly aligned with your business users then get in touch for a chat to see how we can help.