Metrics Store 101: The Data Leader’s Guide for Accurate Business Reporting

Need more accuracy and consistency in your business intelligence (BI) tools? A metrics store, a central repository between data layers and the applications that access them, can help organize definitions to ensure consistent results, regardless of which tools you use.
January 20, 2023
Metrics Store 101: The Data Leader’s Guide - Metrics Store - Hakkoda

These days, there are few questions that loom larger in the data leader’s mind than how to ensure accuracy and consistency in a world of ever proliferating data. With more business users accessing, leveraging, and applying data in their day-to-day roles across organizational functions, the cost of dealing in inaccurate information rises each year—enter the metrics store. Wielded correctly, it’s the solution we’ve all been looking for. 

How Did We Get Here? Origins & Use of The Metrics Store

The data world has been talking about metrics stores for a few years, but its status as a crucial framework for businesses is only now solidifying. In part, that’s because what the metrics store can do today simply hasn’t been possible in the past. Historically, the functionality of the metrics store as we know it has been cobbled together through metrics definitions cleverly hidden in front-end facing layers of the technology stack. While this was a useful measure, it required BI tool formula languages or at worst, manual tracking in an Excel spreadsheet to maintain. 

Today, the metrics store functions as a key piece of an analytics stack–a layer that sits between your data lake or data warehouse and all of your data users, offering a consistent source of data metrics definitions, no matter what BI tool an individual is using to query. For businesses that hope to compete in a modern marketplace, it’s essential, not only for the consistency and ease of use it offers, but for the ability to function in a world increasingly driven by headless commerce.

The Rise of Headless Commerce

Headless commerce is an analytical architecture that separates the data back-end from the user interface front-end. This configuration frees companies to deliver exceptional and highly customized customer experiences at high speed. However, this architecture also requires careful transportation of data between the back and front ends.

Headless commerce relies on an architecture that can pass API requests between the presentation layer (the user interface) and the application layer (the backend of your platform). In its simplest form, headless commerce looks like this:  A user makes a purchase through their smartphone. The presentation layer then sends an API request to the application layer to process. The application layer processes the order and communicates back to the presentation layer, where the user sees their order confirmation. The order processing is happening in an entirely different location from the user front-end, delivering benefits like speed and a more customized experience for the individual. 

Decoupling the user interface from the back-end is a powerful move for e-commerce companies —just take a look at the market gains companies like Nike, Amazon, and Target have made under the banner of headless commerce. In short, it’s a trend that’s not going to go away anytime soon. 

As customer expectations around user experience continue to grow, it’s likely the number of businesses and industries relying on headless commerce to deliver better UX will, too. Large organizations like Toytoa and McDonald’s are among recent adopters, and one study found that while only 64% of businesses are currently using headless tools, 92% of organizations plan to evaluate their headless commerce options in the next 12-months. 

While headless commerce makes the old way of storing metrics definitions all but impossible by separating the user interface and the business back end, the modern metrics store is more than up to the task, thanks in large part to the same innovation that makes headless commerce possible: Headless BI. 

How Headless BI & Metrics Stores Work Together 

Headless business intelligence (BI) platforms are the functional tool that decouples data metrics from the presentation layers that display them, pushing metrics definitions up the data stack.

Data-driven organizations use business intelligence tools every day to analyze data of all kinds and guide decision-making. But the proliferation of business intelligence tools throughout a company can yield varying results despite using the same data source. At the core of this issue is the way each tool defines metrics. Often, there is no standard definition of metrics across tools. For accurate analysis and reporting, metric standardization should be a priority.

In the basic architecture of BI tools, data modeling and metric definitions go hand-in-hand with the visualization component. Headless BI data architecture decouples these layers, providing an intermediary layer for metric definition. This intermediary layer offers unified metrics definitions that can be shared across multiple tools. In other words, it creates your metrics store. 

To achieve this, the intermediary layer uses application programming interfaces (APIs) to connect to all the systems. Rather than connecting each tool directly to the data source, the tools extract information from the metric store.

Storing and Leveraging Metrics

Headless BI functions based on facts, measures, and dimensions. Facts are events the business is interested in analyzing, such as visits to a landing page. Measures include the quantitative attributes of facts. For example, each landing page visit includes quantitative information like time on page. Measures also represent qualitative information such as people, place, or products. Dimensions are used to further categorize or segment data.

Headless BI promotes agility by enabling a one-time setup for each metric. With a one-time setup, companies gain significant benefits.

One such benefit is a single store for metrics. The key to robust reporting is aggregating data from multiple sources. A metrics store helps analyze the information using a standard metric definition, which keeps data consistent across reports. All of this happens regardless of which system the data originated from or where you use the data.

Real-time information is another benefit of headless BI. Real-time information is an approach to data modernization that gives companies the speed and agility they need to remain competitive. With the use of application programming interfaces (APIs), businesses can get information from data sources as soon as it’s available. A metrics store enables organizations to create real-time metrics dashboards.

An additional benefit of this approach is that metrics developed via headless BI are composable and reusable across systems. Moreover, they can be reused without needing knowledge of the underlying data structure. This opens up new opportunities for companies that want to incorporate their metrics into other operational systems. That way, users can access the information in systems they already use without needing to visit the BI tool for the information.

A Metrics Store in Action

A metrics store organizes categories of data you want to measure—such as sales or customer behavior data—in a single place. Then you can tap into the store via an API to extract measurements accurately and consistently.

As a real-world example, Airbnb relies on data for its extensive apartment rental service. To support many users of their large data sources, Airbnb built a metrics platform. This works as the sole data access point for analytics at the organization.

Before building a common metrics store, Airbnb suffered various operational problems, including site failures from direct access to their production databases. As the company underwent rapid growth, so did the complexity of their data. The proliferation of data and users resulted in inconsistent tables.

Even straightforward queries could result in different answers depending on who asked. It also took time (and money) to find and fix problems in the data, as each team had their own metric definitions.

The new metrics store streamlines how data is processed from its sources to its users. A simple API ensures the accuracy of data in any application. Now changes to the data tables remain consistent.

Airbnb has over 12,000 metrics in its system, working for people in any department and using any tool. The metrics definitions are consolidated under source control. One store allows access to the entire data set.

As a result of their switch, Airbnb has reliable information for reports, analysis, testing, and all other uses. They relied on the metrics platform to survive the tumultuous effects of COVID restrictions on travel—and they continue to use it to drive growth.

Accuracy and Efficiency Using a Metrics Store

Metrics stores provide a centralized place to get data from the warehouse and ensure the data and metrics are consistent. As a result, users see the same results when they analyze data regardless of the tool they use.

Holding metrics information in a central location improves productivity. With the metric defined in one location, users don’t need to recreate the same information multiple times or worry about inconsistent data.

A metrics store also allows the metrics themselves to become managed assets. This means teams can track and control changes to the information. There’s no need to spend time tracking down issues should a problem arise. Users can see what changed and who made the change. 

As managed assets, companies can also assign security rules to control who can access the information. This level of control minimizes the chances that bad data will enter systems and lead to inaccurate reporting. Data management systems foster better collaboration between teams. By unifying metrics, companies make it easier for teams to get what they need.

Business Reporting With Hakkoda

When leaders struggle to optimize their data management and stay current with the modern data stack, the business costs are clear and tangible. Headless BI and metrics stores support data accuracy by letting companies define and store metrics in a common repository that can be used across tools. As a result, they’re an increasingly essential piece of modern data architecture.

Implementing headless BI and metrics stores can be a complex undertaking. With so much riding on accurate data, it’s essential to migrate quickly and effectively. That’s why partnering with a data management expert like Hakkoda is so important.

Our highly trained team of data experts can help you move to a more modern data management and reporting solution using headless BI and a metrics store. To begin your business intelligence modernization, reach out today.

With Coalesce, Snowflake Cortex Offers a Built-to-Scale Data Management Solution

With Coalesce, Snowflake Cortex Offers...

Here’s how Coalesce and Snowflake pair to make data management easy, scalable, and more powerful than ever.
Why Your Enterprise Gen AI Deployment Isn't Delivering & How to Identify Truly Impactful Gen AI Use Cases

Why Your Enterprise Gen AI...

Learn how to identify the hardest hitting Gen AI use cases for your organization and see more robust returns on…
What is a TRE & How Can They Help Your Organization Manage Sensitive Data?

What is a TRE &...

Using the built in capabilities of Snowflake and Streamlit, manage your Trusted Research Environments efficiently and securely.

Never miss an update​

Join our mailing list to stay updated with everything Hakkoda.

Ready to learn more?

Speak with one of our experts.