How a modern data stack supports data maturity

Modern, cloud-based data infrastructure can serve as a bridge from the beginning of your data journey to the final stages of data maturity.
August 1, 2022

The concept of data maturity is admittedly a little buzzy these days, but it can be a useful tool for assessing how effectively a business is utilizing its data. Data teams are primarily responsible for extracting value from organizational data, and smaller data teams often have a dual responsibility — deliver fundamental metrics for specific stakeholders while laying the groundwork for wider data access and more advanced analytics projects down the road.

In a recent ebook and video, we explore how a modern data stack can serve as a bridge from the earliest stages of your data journey to the latter stages, which tend to involve widespread data exploration within an organization and initiatives like machine learning and prescriptive analytics.

Here’s a quick preview of one part of the ebook — our discussion of data maturity and how a modern data stack can help you get there.

The stages of data maturity

“Data-mature” businesses typically wield forward-looking analytics — they don’t just understand what happened in the past, and why. They can respond to market signals in real time and anticipate the future effects of a range of strategic decisions.

Progress towards data maturity is often measured in stages. Here’s one of the models we use at Fivetran:

Data-mature businesses tend to have an organization-wide single source of truth, with standardized reporting, and analysts and decision-makers work with real-time or near real-time data. These businesses have moved from descriptive analytics to diagnostic and predictive analytics and finally to prescriptive analytics, often in tandem with AI/ML initiatives.

The benefits of being data-mature

The concept of data maturity is broader than the concept of data-drivenness, but a data-mature business is by definition data-driven. And we have a lot of evidence that data-driven organizations outperform their competition. Take these findings from Forrester’s “State of Insights-Driven Business Maturity”:

  • Data-driven companies are 178% more likely to grow revenue
  • They are 240% more likely to have a competitive edge
  • Between 2019 and 2021, they took $1.8 trillion from their competitors 

Data-mature organizations also look a lot like what Thomas Davenport, in the Harvard Business Review and his recent book, has called “analytics competitors”: businesses that leverage analytics to consistently outcompete their peers.

Analytics competitors go well beyond basic analytics. As Davenport observes, they use predictive modeling to understand which customers are most profitable, how much they will buy over a lifetime, what prices they will pay, and what will trigger them to buy more. They set prices in real time; anticipate supply chain bottlenecks and simulate solutions; and model the impact of operational costs on profit margins.

To achieve this, they need to rely on modern data infrastructure — a modern data stack.

Data maturity and the modern data stack

A modern data stack generally consists of automated data integration, a cloud data warehouse, a data transformation tool and a business intelligence tool.

This cloud-based infrastructure supports data maturity and advanced analytics in a number of ways. Because data integration, storage and analysis are largely automated and infinitely scalable, organizations can easily access and leverage all of their data — there are no incomplete data sets or missing data sources, and plenty of fuel for AI/ML.

A modern data stack also standardizes all data distribution processes and channels; everyone is operating from the same source of truth and reports all emerge from the same tool set. Data access is universal and data sharing is seamless, so nearly all decisions are influenced by relevant metrics visible to everyone.

This is how analytics competitors operate. They eliminate siloed data and siloed analytics. Analytics initiatives are centrally managed and rely on the same technology, tools and data. 

Set up and start test-driving your MDS in under an hour

In the video accompanying the ebook, we show you how to quickly set up a data stack of Fivetran, Snowflake and Sigma, and use it to load and transform Salesforce data. This stack is appropriate for smaller teams with limited budgets, but it will also scale with you as you grow into a larger, more data-mature organization.

Free ebook: Scalable data infrastructure in under an hour


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