4 Stages on the Path to Data Maturity

By TechFunnel Contributors - Last Updated on April 2, 2020
Data Maturity Stages Explained

Guest Contribution by Michael Milton, Senior Vice President of Data Science @ Infogroup

If you go in search of statistics around the rise of “big data,” it won’t take long for you to stumble across some pretty mind-boggling numbers. By some estimates, we’re generating more than 2.5 quintillion bytes of data every day, with 1.7 MB of data being created every second for every person on the planet.

Add that to IBM’s assertion back in 2016 that 90 percent of the world’s data has been generated in the past two years alone, and we start to get a sense of just how fast the data landscape has exploded all around us.

Given this astronomical growth, it’s no wonder that company executives—and marketing leaders in particular—are under great pressure to transform their organizations to accommodate the new, data-drenched reality.

Over the past decade, this pressure—and companies’ varying responses to it—have led to a rapid segmentation of today’s enterprises in which data maturity has become the prime indicator of an organization’s preparedness for the future.

These days, most companies exist in one of four stages along the path to data maturity, and their position on this spectrum says a lot about the opportunities that they’re able to harness.

( Also Read: What is Master Data Management? )

Let’s take a look at these four tiers of data readiness, and the key capabilities that companies in each tier must be looking to achieve.

  1. Drowning in Data

    These days, a significant number of companies are still sitting at the data starting line, and understandably so. They’re accumulating data faster than they’re able to make sense of it, and the companies’ lack of data infrastructure and organization—not to mention the lack of understanding when it comes to what’s quality data and what’s not—can be paralyzing. For these organizations, the first step in progressing to a higher level of maturity is to find a North Star.

    The goal for enterprises that are drowning in data is to gain a better understanding of their operational metrics so they can start to make sense of the flood. Start with the basics: What data do we have coming in, both internally and via third parties? And which metrics and insights can be tied directly to our success as a business?

  2. Harnessing High-Velocity Data

    Enterprises that already know how to make sense of their data face an important and equally daunting challenge in getting to the next plateau of data maturity: How can we take the high-velocity data that’s coming into our company and ensure we get the best and freshest insights to the people who need it to do their jobs better?

    Enterprises looking to extract value from high-velocity data need to go beyond understanding their data to breaking down internal and external barriers to its usage. This is where data infrastructure and integrated partnerships become key. A robust infrastructure is a necessity for extracting value from these streams through machine learning.

  3. Seeking Competitive Advantage via Data

    Once enterprises have established strong data competencies and systems internally, they’re ready to start leveraging data as a durable competitive advantage in their industries. This means investing in data engineering and data science to build capabilities and models around unique company data that others in their industry can’t duplicate.

    We’re seeing this stage of data maturity come into play right now in industries like insurtech, where even a tiny efficiency advantage in managing claims can be enough to catapult a company to a position of competitive advantage.

  4. Leveraging Advanced Models for Predictive Purposes

    Finally, at the pinnacle of data maturity today, we find advanced data organizations that have mastered the use of their data for functions like marketing, targeting and personalization.

    These are the organizations that are now looking to advanced modeling techniques for predictive purposes. In other words, how can AI and machine learning be applied to the company’s data stores in order to make better decisions regarding the future of the business?

    While only a select few companies are operating in this fourth stage, it can be disheartening to executives in the first stage to even know these enterprises exist. But there’s no reason to become discouraged. It’s useful to keep in mind just how quickly the data economy has sprung up around us and how quickly progress can be made.

    While many businesses and executives are daunted when it comes to knowing where to start, they should be encouraged that many of the tools and processes for achieving data maturity are well-established and that there can be huge efficiency advantages from being a second mover.

    Progressing along the path to data maturity begins with frank conversations and by putting data quality and accuracy at the heart of every conversation. If your company is languishing in one of the early stages of data maturity, why not jumpstart those conversations today?

TechFunnel Contributors

TechFunnel Contributors | TechFunnel.com is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicated to sharing unbiased information, research, and expert commentary that helps executives and professionals stay on top of the rapidly evolving marketplace, leverage technology for productivity, and add value to their knowledge base.

TechFunnel Contributors

TechFunnel Contributors | TechFunnel.com is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicate...

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