Introducing SafeGraph’s New Data Maturity Model

The four key stages in becoming a data mature organization.

Data is Everywhere, But it’s Certainly Not All Created Equal

At SafeGraph, we pride ourselves on holding data up to an entirely new standard of excellence. Not only because we live and breathe ‘data’ every day and in everything we do, but more so because we know that good, clean, accurate, and comprehensive data can be hard to come by. 

We also know that good data can propel businesses, governments, non-profit organizations, and research institutions on an upward trajectory, enabling them to uncover unique insights and drive new innovations like never before. When used to its full potential, data can change the world and advance our understanding of society for the better. 

But our purpose here isn’t to talk up the value of data. You already know that data is valuable. You wouldn’t be reading this if you thought otherwise. The real trouble is, there’s a ton of data floating around and, unfortunately, many organizations still struggle with incorporating it effectively into their overarching business strategies. This comes down to one thing: data maturity. But how do you know where your organization sits on the data maturity spectrum? 

Fortunately, you’ve come to the right place. In this guide, we’ll provide a fresh take on the four stages of data maturity and clearly explain how progressing along the data maturity spectrum can have a fundamental impact on your organization’s future. So at a time when even the Economist has boldly claimed that the “world’s most valuable resource is no longer oil, but data,” the big question perpetually remains: How can we use it to drive long-term value?

What sets good data apart? Learn more in SafeGraph’s Data Evaluation Checklist

Key takeaways at a glance

Assessing your organization’s data maturity means understanding what happens at each stage of SafeGraph’s new data maturity model: 

  • Phase 1: Explorer  
    Data is primarily used for internal reporting purposes only.

  • Phase 2: User  
    Data-driven insights are used to inform strategic business decisions.

  • Phase 3: Leader  
    Data is leveraged strategically to drive competitive intelligence.

  • Phase 4: Innovator  
    Data informs a continuous evolution of business strategy.
Data has proven to be a competitive differentiator…. Company performance is highly correlated to data maturity.

William McKnight in The Importance of Data Maturity

What is Data Maturity?

Sisense tells us that “data maturity is a measurement of how advanced a company’s data analysis is.” Seems like a reasonable definition, but what does that really mean?  

Data maturity is not just about the role that data plays within an organization’s day-to-day operations as much as it is about how it can enable organizations, of all shapes and sizes, to do something in the future that it couldn’t have done in the past without using data.

Therefore, when looking at data maturity from this angle, it becomes a question of empowerment: How can data be leveraged in a powerful way to unlock new insights and innovations that can eventually turn ideas into reality? 

Here’s what we already know to be true. Organizations have more access to data—really good data at that—than ever before. Truth be told, they probably have more than they even know what to do with it. Even so, the 59% of companies (and growing) that use data analytics in some capacity every day have likely only scratched the surface in unlocking its real potential. 

As a starting point, many organizations primarily use data today to run standardized reports and build metrics dashboards. While this is better than nothing at all, limiting data to play a purely ‘administrative analytics’ role is a huge disservice—and an insult to the data itself.

Even so, and as the most data-savvy organizations know, data can tell stories. Data can uncover unseen truths. Data can inform, improve, and even challenge decision-making at all levels. And finally, data can overhaul a business’s strategy and fuel its long-term success. 

All of this is possible—and quite a bit more—as long as organizations know what to do with all this data they have at their fingertips. And while there may be different interpretations of what data maturity is, our approach gets at the heart of why it truly matters. Data can transform organizations in powerful ways. They just need a clear roadmap to get there. 

49% of companies say data helps them make better decisions, while 16% say it enables key strategic initiatives and 10% say it improves relationships with both customers and business partners.

The Analytics Advantage (Deloitte)

The Stages of Data Maturity, According to SafeGraph

For us, data maturity is a journey of exploration—where organizations not only get more acquainted with the data sources they have to work with but also learn how to leverage it in oftentimes surprising, eye-opening, and unexpected ways. In fact, once organizations take the big step from seeing data as merely a source of information and, over time, begin to understand its real potential as an influencer—or even disruptor—of decision-making, an organization’s desire to become more data mature will likely (and immediately) increase tenfold. 

It’s important to keep in mind, however, that the shift from a data novice organization (what we call the ‘Explorer’ stage ) to a sophisticated, data mature organization (what we call the ‘Innovator’ stage) isn’t something that can or will happen overnight. Working your way through the stages of data maturity takes time and patience. We actually see it as a journey. 

That’s why we decided to reimagine our data maturity model in 2021. Rather than simply create a structure that, for lack of a better way of putting it, puts organizations in a “box,” we wanted to give them a starting point for their future data maturity journey: an easy way to assess where they are today and a path forward for becoming a more data mature organization in the future. 

Getting there doesn’t always follow a linear path. Becoming a data mature organization takes time, effort, and a lot of work. Our framework can help you get there, as long as you are committed to unleashing the power of data to work harder for your organization. 

To get an idea of what our model looks like, here’s a birds-eye view of each of the stages:  

A snapshot of SafeGraph’s new data maturity model.
A snapshot of SafeGraph’s new data maturity model.

Phase 1: Explorer

As the name suggests, this is the stage where organizations are barely scratching the surface with the data available to them. In fact, the most defining characteristic of this stage is the lack of consistency in both how data is managed and used across the organization. Explorers tend to lack a central data infrastructure. Any data collection or analysis happening across the organization, therefore, takes place on an individual by individual basis. There’s simply no coherent strategy for organization-wide data sharing or data quality.  

Because they don’t have a centralized data strategy in place, Explorer organizations often rely solely on their own first-party data for simple reporting purposes. They have not yet begun connecting to third-party datasets to help answer critical and in-depth business questions. 

While these organizations have some work to do to grow along the data maturity spectrum, at least they recognize the value of data and are using it to drive rudimentary insights.

Phase 2: User

Becoming a User organization is a fairly big step up within our data maturity model. These organizations understand the importance of data quality and have put measures in place to make that an organization-wide standard. This includes building an internal data architecture that makes it easier to share data across departments, teams, and individuals—including ad hoc datasets from third-party providers that are used to enrich internal data sources.

What truly separates Users from Explorers is how they use data, analytics, and insights to inform decision-making. Even so, the collection and analysis of data is typically reactive, meaning that it is used primarily for measuring results and reporting on performance. User organizations don’t yet leverage data as a foundational element for business strategy planning. 

Phase 3: Leader

Leader organizations put data at the heart of decision-making and competitive intelligence. To that end, data analysis fuels business strategy and clears a path for achieving organizational goals and maximizing business outcomes. To do this, Leaders often see the joining of third-party datasets to their own data as the differentiator giving them a competitive edge in the market. 

These organizations have also streamlined and centralized their data infrastructure. Not only do they have systems and standards in place to ensure the highest data quality—helping to build greater confidence around the insights provided—but they also have built an architecture that makes it possible for the entire organization to be data-driven. 

Phase 4: Innovator

Reaching the proverbial data maturity ‘mountain top’ are the Innovators. These organizations see data as more than just a tool to inform decision-making and maximizing outcomes; rather, they embrace it as a catalyst for constant and continuous innovation organization-wide.

Innovators realize that even the best business strategies must ebb and flow over time. They use data proactively—and build predictive algorithms around it—to improve business outcomes and stay one step ahead of the competition at all times. For these organizations, being able to adapt to changes in the market and in society at the drop of a hat is not optional; it is table stakes.  

Additionally, they regularly seek new ways of joining their own data with third-party datasets—even from what you might consider ‘non-obvious’ data sources—to remain perpetually competitive and seize new opportunities to make a marked impact. This, therefore, implies that data governance is built into all business processes, with a robust architecture in place to be able to share large amounts of high-quality data with speed and efficiency. Innovators are the nirvana of data maturity, something that all organizations should aspire to. 

A high level of data maturity is the stage reached when data has woven its way deeply into the fabric of an organization and when data has become incorporated in every decision that an organization makes.

Data Maturity (Sisense)

4 Fool-Proof Steps for Evaluating Data Sources

If your organization relies on data, in any way, to fuel business success, competitive intelligence, or future innovations, you must know how to weed out “good” versus “bad” data. Failing to do so could be a massive waste of time and resources. 

As a starting point, ask yourself the following questions: 

  1. Does the data come from credible sources?
    When people don’t like what the data is telling them, the first instinct may be to “cry wolf” and blame it on data quality. So to avoid falling into that rut, be sure to verify the accuracy, quality, and trustworthiness of the data’s original source upfront. 

  2. What can (and can’t) the data tell you?
    True, data can do a lot of things, but not every dataset can answer every question on your mind. Be clear about its limitations and work within those parameters.

  3. How much scrubbing will be required to clean the data?
    There’s always a bit of cleaning, sorting, and processing in order before being able to use data, especially if you plan to connect it to other datasets. But knowing that some datasets are inherently “cleaner” than others, set some ground rules around how much time and effort is worth scrubbing a truly messy dataset to make it usable. 

  4. How will you ultimately use the data?
    Always have a plan in place about how you’ll put the data to work. But also give yourself some room to be creative—after all, you never know what unique and unexpected insights might arise by joining different datasets together. 

There’s a lot more where this came from. To learn more about what to keep in mind when assessing data source quality, be sure to check out SafeGraph’s Data Evaluation Checklist.

A single dataset on its own has limited value. The real value from data comes from connecting it across multiple disparate datasets.

SafeGraph CEO, Auren Hoffman and FICO CEO, Will Lansing in Why Data Standards Matter

Data Maturity is a Journey, Not a Sprint

Data is one of the most valuable assets available to any organization today. Unfortunately, many simply don’t know how to use data to its fullest. So if your organization falls into this category, don’t worry—it just means that you are on the start of your own data maturity journey. 

The good news for you: There are a lot of ways to become a data mature business. It’s not always a linear path nor is it going to happen over night. But when you make the important decision to put data at the heart of your organization—to fuel business strategy, inform decision-making, and uncover competitive intelligence like never before—you are taking the first step in bringing your organization into the data age.


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