What are Data Products + How to Build One

SaaS Growth and Trends
Feb 28, 2024
What are Data Products + How to Build One

About 329 million terabytes of data is generated every single day. Given how tech is advancing and how there are more data consumers each day, that number will continue growing. It’s no wonder that data products are becoming popular for a huge variety of use cases.

But if you’re wondering what those products are and how they work, you may need some initial guidance. Then, you can learn how to create a product that helps data teams in various industries.

Let’s get started.

What is a data product?

A data product is any type of product that helps end users make data-driven decisions or solve their problems based on data analysis. Data products are typically built by using a combination of data science and analytics.

Data products take raw data and transform it into meaningful insights, presenting it in a way that is easy to use. With data products, you can do predictive analytics, augmented analytics, data mining and more.

Data product vs. data as a product

In the world of data mesh, the terms are often used interchangeably but there is a significant difference.

A data product is any type of product that uses data analysis and insights to provide value to organizations and guide them with better decision-making. They help solve specific problems or optimize business processes based on insights from said data.

Some examples of data products include reports, dashboards, predictive models, fraud detection systems, and more.

For example, Apogea built interactive dashboards to help visualize the buildings they are developing - they showed 3D models to engage prospective customers.

apogea dashboard

Data as a product refers to the concept of treating data as an asset that can be packaged and sold to stakeholders and end users. While the product itself can deliver value, the data is a standalone product that can be monetized for the right end user.

Examples of data as a product include datasets, databases, data streams, APIs or data feeds.

luzmo data connectors

One good example is Luzmo’s Data Connectors, allowing you to use APIs to connect Luzmo with various apps - BigQuery, Databricks, MongoDB, Panoply, Oracle and many others.

The key characteristics of data products

Want to build a data product using your existing data assets? This is what a great data product typically looks like.

  1. Support in decision-making: the end goal of a data product is to support someone in making more data-driven decisions
  2. Interactivity: data products should be interactive and allow the end user (e.g. a data product manager) to take a look at the data from different angles and make their own conclusions through tools like dashboards or reports
  3. Real-time or near-real-time data processing: the data pipelines should allow for real-time access to data so that the end user can make decisions based on data that is relevant and up-to-date
  4. Predictive or prescriptive analytics: a data product should allow the end user to benefit from the complex algorithms and successfully predict future outcomes, as well as suggest methods for improving them
  5. Personalization: good data products offer personalization based on user preferences or historical interactions
  6. Scalability: no matter how much data you have in your data catalog, a data product should easily accommodate it
  7. Integrations with business workflows: be it an app or a business process, the data product should be easy to integrate

These are the key traits that a good data product should have to be functional and meet the needs of your end users.

How to create a data product

Despite the many tools and methodologies we have today, data teams can still get lost when creating their data products. These are the typical steps you should go through.

1. Define your objectives

What do you want to do with a data product, and for what kind of audience? Do you want to build an interactive dashboard for C-level executives to do data reporting on sales? Or an embedded analytics dashboard for a project management tool, showing the key team metrics to the end user?

Before writing a single line of code or collecting data from your data platform, data engineers should determine the pain point they are solving and the audience they are solving it for.

2. Determine your data sources and collect your data

When you know what your end goal is, you can start building a data product. Data is the starting point and it has to come from somewhere. Typically, this data comes from other apps, such as your own app (through an API or direct integration) or other business apps like CRMs, marketing tools (e.g., SEO software), data warehouses, and others.

The data sources you use will determine the kind of tools you use for building your data product, as you need tools that integrate with your data sources without hiccups.

3. Clean and preprocess your data

Raw data is not very useful for building data products. This is why it’s necessary to do data cleaning, also known as data scrubbing or data cleansing. Some of the things that happen during this stage include:

  • Filling out missing values in data fields
  • Deduplication
  • Format standardization
  • Correcting errors in data
  • Handling outliers
  • Normalization and scaling
  • Data validation
  • Addressing inconsistencies 

Once the process is done, the data is formatted in such a way that it can further be used for exploration, visualization, and making important business decisions.

4. Do exploratory data analysis (EDA)

The datasets are ready and you can go through them to make sense of your data. Before the data is visualized to solve a business problem and showcase your main KPIs, you need to explore it and make sense of it.

exploratory data analysis

At this stage, you do descriptive statistics, data analysis, data transformation, recognize outliers and patterns, do statistical testing, and summarize your data.

For example, you could take a look at your overall sales numbers to come up with the data on your worst-selling products for each quarter, to determine what needs to go. The data quality is good, and once it’s explored, it’s ready to be visualized.

As the final step of the process, you do data visualization to turn your data into visual insights. From numbers, the data is turned into graphs and charts that allow for better decision-making and ease of insight.

5. Do feature engineering 

With the data ready, clean, analyzed, and explored, you can build the features of your data product. These features will be learned by machine learning models or any other analytical tools.

At this point, you want to extract relevant information and use it to improve the performance of the model.

6. Do model development

At this stage, you’re building a predictive or analytical model that is going to be the core of your product. Some of the models you can choose include decision trees, linear regression, support vector machines, or ensemble methods. Depending on the problem you’re trying to solve and how complex it is, you’re going to choose one of these models.

7. Evaluate

Once you’ve developed your data model, you want to make sure it works. The best way to do this is to introduce it to new, unseen data. Depending on the problem and its complexity, you could use several metrics to evaluate the success of that model:

  • Precision
  • Recall
  • Accuracy
  • Mean squared error
  • F1 score

And others.

8. Integrate

One of the most crucial parts of your product development is integrating your data product into the rest of your app or system. This is where certain things come in handy, like great knowledge of SQL, having a team of data scientists or having a product that is built API-first, with connectivity in mind.

If you’re building your product from scratch and you want the integrations to click, developers in your team are going to have a rough time. But if you’re grabbing a product off the shelf, such as Luzmo for the resulting data visualizations, it should fit into your product nicely with just a few API connections.

10. Do UI design and test

To get real-time insights from the new data they have in front of them, your users need a great user experience. For a high-quality data product, you could create a self-service product where customers can explore the data on their own terms.

Or if you’re really up to date, you could help them explore their data and use your data product using artificial intelligence. For example, Luzmo has an AI chart builder, where you can put in various examples of data and have Luzmo create a chart for you, with minimal input from the user side.

11. Deploy and collect feedback

Your data product lifecycle is almost complete. Now you need to ship the finished product to the users and wait for the feedback to roll in. 

Make it easy for the users to leave feedback through the product, or with other tools like live chat, email, social media, or your preferred platforms for communicating with users. If you have a community where your users hang out and get product updates, this is another great opportunity for feedback collection.

Create better data products with Luzmo

You don’t need a degree in data science to create amazing data products. With Luzmo, you can create embedded analytics dashboards that seamlessly fit into your SaaS product. With real-time data analytics, access control, and amazing customization options, your product manager, data scientist, and developer will love Luzmo.

Grab your free demo today to find out how we can help you create and integrate an embedded analytics dashboard - in hours, not days or weeks.

Mile Zivkovic

Mile Zivkovic

Senior Content Writer

Mile Zivkovic is a content marketer specializing in SaaS. Since 2016, he’s worked on content strategy, creation and promotion for software vendors in verticals such as BI, project management, time tracking, HR and many others.

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