Data Products 101: Everything You Need to Know

Twenty years ago, data was little more than fuel for forecasting. A few marketing insights here. A couple financial reports there. Today, data doesn’t simply support your products—more often than not, it is the product.
In the age of AI, data isn’t just another cost center—it’s a value creator. Data teams aren’t service providers—they’re essential technology partners.
Now, if you’re reading this article, I have no doubt that you’re aware of the value of data for the modern enterprise. What you want to know is, “what should I do about it?” As the bottomline impact of data continues to expand, the need to manage our critical data assets differently comes into focus. If your data is as valuable as other business-critical products and resources, then it needs to be handled with the same level of intentionality.
In other words, you need to treat your data as a product.
But, what exactly does it mean to treat your data as a product? And is every asset really a product? In this article, we’ll answer those questions and more as we dive into examples, best practices, and strategies to create and manage effective data products.
Let’s dive in.
Table of Contents
What is a Data Product?
The first thing you need to learn about data products is that…not every asset is a data product. So, before we get too far ahead of ourselves, let’s define a data product with a little more rigor.
A data product is a critical data asset, such as a key dashboard or AI model, that’s been developed and maintained with the same reliable processes and architecture that you would use to develop a product for an external customer.
To treat any data asset as a product means combining a useful dataset with product management, a domain semantic layer, business logic, and access to deliver a final product that’s appropriate and reliable for a given business use-case.
In other words, the difference between a data product and something with data in it is the process you use to manage it.
Benefits of data products
We’ve obviously talked a lot about what data products are—and even how to create them—but what are the benefits?
There are several benefits to treating your critical data assets as data products, from encouraging adoption to measuring performance. Let’s look at a few now.
Data products increase trust
Trust is hard to win and easy to lose—especially when it comes to your data. The more your stakeholders trust the data, the more value they’ll be able to realize from it—and the more valuable your data team will be.
Treating your data facilitates trust by aligning data practices to business goals, delivering data assets that are both more reliable and more valuable for the teams who use them.
Data products provide a framework to measure (and report on) the value of your data team
If you’re a data engineer, data scientist, analytics engineer, or a similar role, you’re likely very close to the data. That means you have an intimate understanding of your technical architecture, how your pipelines are running, and the data they’re delivering to the business. But, that intimate data knowledge typically isn’t shared by everyone in the organization – especially business stakeholders. Because of that, it can be difficult to explain where your time goes—and why that matters for the business.
Treating data like a product encourages hard metrics that can demonstrate real business impact if done correctly. These metrics might include the number of data incidents, time to detection (TTD), time to resolution (TTR), data downtime, the cost of that downtime, and more.
Quality Metric | Definition and Calculation | Industry Standard |
---|---|---|
Number of incidents (N) |
An instance of incorrect, missing, or incomplete data. Total incidents (N). Incidents per 1,000 tables per month. |
6 per 1,000 tables per month. (37% schema; 23% volume; 28% freshness; 12% quality) |
Time to detection (TTD) | The average time from when incidents occur to when they are detected. The time to response — the time between an alert and investigation — can also be a helpful metric. | 4 hours |
Time to resolution (TTR) | The average time from when the issue has been detected to when it has been given a resolved status. | 9 hours |
Data Downtime |
N x (TTD + ITR) It can be helpful to calculate data downtime in the aggregate as well as by specific data products. |
793 hours/month* |
Data Downtime Cost | Our calculator and eBook are the best resources for this. |
~26% of revenue OR ~$280K labor cost ~$1 million in efficiency cost* |
If you’re not sure exactly how to calculate the cost of data downtime in your organization, you can use this calculator to input your numbers and get an estimate.
Data products improve your team’s focus and capacity
Surveys have shown that data engineers typically spend anywhere between a third and a half of their time on data quality. That’s huge – and it can feel even more time-consuming when you don’t approach your data with a data product framework.
By treating your data as a product, data teams can automate and expedite a significant amount of the tedious work that goes into fixing data errors and instead, spend more time on innovative work that actually moves the business forward.
Plus, giving self-service access to data products to users across the business can dramatically reduce the amount of ad-hoc questions directed toward the data team.
Moving from Data to Data-as-a-Product
Data without context is just data. Data becomes a data product when it takes on a framework that ties data directly to the business. Data products like these might take the form of tables, reports, machine learning models, and other key assets used by the business.
Sounds simple enough, right? Well, this shift from data to data-as-a-product also needs to be accompanied by a mindshift change: the data must be treated with the same diligence as production-grade software products in order for your data and AI products to realize their full potential.
Unfortunately, you can’t just declare that an executive dashboard or internal machine learning model is now a data product. To turn your data into a data product, you need to make the necessary changes to your data’ governance and reliability frameworks.
In the next section, we’ll dive into the critical adjustments you’ll need to make to turn your data assets into bonafide data products.
How to Design a Data Product
Facilitating the kind of cultural transition necessary to support a data product framework requires a mix of tooling and process, applied consistently across a web of domain and functional teams.
A data asset becomes a data product when it’s augmented with the right context, standards, and management practices to promote stakeholder value, predictable reliability, and long-term user adoption.
While that can—of course—look different for every organization (or even domain), the steps are the same.
1. Create SLAs
Before you actually start building a data product, you need to get everyone involved on the same page.
Start by creating SLAs to ensure different engineering teams – and their stakeholders – are confident that everyone’s speaking the same language, caring about the same metrics, and sharing a commitment to clearly documented expectations.
2. Assign ownership within the data team
Once SLAs have been created for cross-functional stakeholders, it’s important to assign ownership and accountability within the data team itself.
Data products involve multiple levels of production work, so be sure to determine owners at the organizational level at the top and then all the way down to the project level, pipeline level, and table level.
3. Establish documentation
Data products have many benefits (see above!), but those benefits go unseen if your data products are hidden away.
The challenge comes from the virtually limitless ways to pipe data combined with the limited capacity of humans to make it sustainably meaningful. In other words, you can never document all of your data, and rarely will you have as much documentation as you’d like. But, there are a few ways to combat this, including combatting data sprawl as much as possible, documenting key assets first and starting only by enabling self-service for members of the organization that really need it, iterating and automating your documentation process based on what works, and then measuring your documentation levels to keep them manageable.
4. Consider compliance and governance protocols
To build a great data product, you need to factor in data compliance and data governance.
Both activities involve documenting, classifying, and contextualizing the data in some manner, but we like to think of compliance as what you do for external stakeholders (regulators) to avoid fines and governance as what you do for your internal stakeholders (data consumers) to increase operational efficiencies.
5. Certify your datasets
Simply put, data certification is the process of taking tiered SLAs (many organizations use gold, silver, and bronze, and occasionally platinum) and assigning them to data assets. This helps data consumers understand the level of trust they can have in the data, and it also prevents data users from the mistake of leveraging the wrong asset.
It can also help the team to focus so they aren’t treating every table or data asset with the same amount of rigor (this is often a recipe for burnout).
Data contracts can help keep a log of changes to a dataset as well. Data contracts are similar to an API in that they serve as documentation and version control to allow the consumer to rely on the service without fear it will change without warning and negatively impact their effort.
6. Assess and demonstrate value with KPIs
Data teams need to hold themselves accountable to the same data-driven KPIs that their dashboards are driving for the rest of the company. The act of planning is valuable in itself, but demonstrating the value of a data product is important for obtaining and investing resources.
We recommend leveraging two core sets of KPIs. The first would be specific to the data and platform. These can measure:
- The breadth of customers and applications
- Building for the future
- Delivering business impact today
- Data quality
The second set of KPIs should be shared goals, or how the data team has helped achieve business level objectives. For example, if you agree with engineering, product, and marketing that onboarding is a pain point, you can decide to build goals and KPIs around making it easier for new customers to get started.
7. Consider DataOps and Agile methodologies
DataOps is a discipline that merges data engineering and data science teams to support an organization’s data needs, in a similar way to how DevOps helped scale software engineering.
Similar to how DevOps applies CI/CD to software development and operations, DataOps entails a CI/CD-like, automation-first approach to building and scaling data products. At the same time, DataOps makes it easier for data engineering teams to provide analysts and other downstream stakeholders with reliable data to drive decision making.
Here’s what a DataOps lifecycle looks like:
- Plan: Partnering with product, engineering, and business teams to set KPIs, SLAs, and SLIs for the quality and availability of data
- Develop: Building the data products and machine learning models that will power your data application.
- Integrate: Integrating the code and/or data product within your existing tech and or data stack.
- Test: Testing your data to make sure it matches business logic and meets basic operational thresholds (such as uniqueness of your data or no null values).
- Release: Releasing your data into a test environment.
- Deploy: Merging your data into production.
- Operate: Running your data into applications such as Looker or Tableau dashboards and data loaders that feed machine learning models.
- Monitor: Continuously monitoring and alerting for any anomalies in the data.
Data Product Best Practices
While the data product approach may be a novel idea for some data teams, it is by no means a new concept. With the shift toward AI and self-service taking hold across industries, data-as-a-product initiatives have become a natural outgrowth of those priorities—and that means there are more than a few best practices to be shared.
Every organization – and every data product – is different, but here are some things to keep in mind:
- Not everything needs to be a data product. Don’t minimize the value of focus. Sometimes you just need to spin up a quick dashboard and that’s okay. Don’t boil the ocean—especially if there’s no good fish in that ocean to eat.
- Don’t try to do everything at once. Building a data product takes time and effort. Start small with one or two key data assets (look for assets with outsized impact, like dashboards used by multiple teams), gather feedback from stakeholders, then iterate or expand as needed. Don’t get all the way to the end to realize you’re missing an ingredient. Get the formula right first, then make enough for the whole party.
- A data product doesn’t have to be a single, universal source of truth without any siloes. Remember, data products are meant to enable specific teams to accomplish specific goals. Don’t try to fit a square peg into a silo-less hole. Know your business, understand your domain users, and then build data products that fit their needs—whatever those look like.
- You don’t have to be decentralized to have a data product. While data products are often associated with decentralized architectures like the data mesh, they work just as well in a centralized environment. Delivering more value for your stakeholders is always a good idea—regardless of how you organize those responsibilities.
Data Product Examples
As we mentioned earlier, data products can take many different forms depending on the needs of your business, industry, customers, and more. What do these different data products look like in action? Here are a few examples:
- An airline’s flight tracking system that combines real-time GPS data, flight manifest tables, and historical arrival and departure information
- A customer relationship management platform syncing data across marketing tools
- An AI algorithm that trains disparate financial and investing data from thousands of sources to forecast future stock returns
It’s essential for data products like these to manage their data quality effectively, and data observability is an ideal tool for the job. Let’s look at how Choozle, a leading digital advertising software company, leverages data observability to maintain the reliability of its data products.
Choozle’s main data sources are demand-side platforms (DSPs), with which they facilitate the buying of media on behalf of its users. These data sources are static with defined ingest patterns, and are powered by ELT pipelines that bring data into a unified schema where Choozle abstracts the difference in how data is presented between the platforms. That equation changed and a data quality issue arose when Choozle released its massively powerful unified reporting capability, which allows users to connect external media sources.
“When our advertisers connect to Google, Bing, Facebook, or another outside platform, Fivetran goes into the data warehouse and drops it into the reporting stack fully automated. I don’t know when an advertiser has created a connector,” said Choozle Chief Customer Officer Adam Woods. “This created table sprawl, proliferation, and fragmentation. We needed data monitoring and alerting to make sure all of these tables were synced and up-to-date, otherwise we would start hearing from customers.”
Monte Carlo gave the Choozle team deeper visibility into data product issues that otherwise may not have been proactively caught. “Without a tool like this, we might have monitoring coverage on final resulting tables, but that can hide a lot of issues,” said Adam. “You might not see something pertaining to a small fraction of the tens of thousands campaigns in that table, but the advertiser running that campaign is going to see it. With Monte Carlo we’re at a level where we don’t have to compromise. We can have alerting on all of our 3,500 tables.”
Every organization experiences some level of data downtime, or periods of time when data is partial, erroneous, missing or otherwise inaccurate. By leveraging Monte Carlo, Choozle has reduced their data downtime approximately 88%.
Data Observability for Data Products
Data products provide a fantastic framework to enable data teams to reduce tedium, automate the small stuff, and focus on innovative projects that move the business forward.
But your data products are only as valuable as they are reliable. A data asset ceases to be a product when business users lose the ability to trust its output. That’s why working with a data observability tool is so important.
By taking an end-to-end approach to your data quality management, you can understand at a glance how a product is performing, where it needs coverage, and how to fix it if something goes wrong.
In fact, tools like Monte Carlo’s include features like the Data Product Dashboard that help organizations manage and improve the quality of data and tables powering their most critical product—and foster cross-functional collaboration in the process. With our tailored dashboards and automated data profiling, you’ll be able to identify coverage gaps instantly, scale coverage automatically, and understand the health of all your critical products at a glance. Plus our AI enabled monitor creation gives your stakeholders the power to create their own monitors too—if you’re into that sort of thing.
And when your data environment (and products) grow, your data quality coverage will grow right along with it.
That goes a long way towards building trust with your stakeholders—and protecting your sanity.
To learn more about how data observability can facilitate the reliability of your data products, talk to our team.
Our promise: we will show you the product.
Frequently Asked Questions
What is an example of a data product?
Examples of data products include an airline’s flight tracking system that integrates GPS and historical flight data, a customer relationship management platform syncing marketing tools, or an AI algorithm forecasting stock returns based on diverse financial datasets.
What are types of data products?
Types of data products include dashboards, reports, machine learning models, and unified schemas for specific business use cases. These vary based on industry needs and user demands.
What does a data product do?
A data product turns raw data into actionable insights by combining datasets with product management practices, business logic, and access controls. It delivers value by aligning data to specific business goals and improving trust, reliability, and user adoption.
What is a data product in data mesh?
In a data mesh, a data product is a domain-specific, decentralized data asset designed for self-service use. It adheres to governance and quality standards, facilitating its use across teams without central control.
Why do you need data products?
You need data products to increase trust in data, provide measurable value to stakeholders, reduce time spent on ad-hoc queries, and enhance focus on innovative tasks. They also improve team efficiency and ensure data quality by structuring assets with governance and reliability practices.