Why data will be the key to sustainable growth for organizations in 2025 

Becoming “data-driven” became a catch-all phrase for organizations this year. Leaders understand that data offers a way to unlock business insights and become more efficient, yet initiatives to become a data-driven organization don’t always bear fruits. Instead, leaders should focus on implementing a data flywheel above all else to reach their goal of becoming data driven more quickly and efficiently.

By leveraging a data flywheel first and foremost, organizations can begin to reap the benefits of being data-driven upfront. That’s because data flywheels create value by continuously improving the quality and efficiency of products, services, and processes. The insights gained drive satisfaction and support innovation, all of which contribute to a stronger competitive position.

However, to gain this competitive advantage by using a data flywheel organizations will still have to tackle common challenges. Let’s take a closer look at how to overcome common hurdles and begin using a data flywheel to drive sustainable growth.

The concept of a flywheel

Part of the issue with other data-driven initiatives is that unlocking organizational growth can be slow and require significant investments. Further, these are standalone initiatives, so the organizations may have one project that looks at success metrics and another standalone project examining internal efficiencies. In contrast, the data flywheel aims to set up data collection and analysis in such a way that the entire process of becoming data-driven becomes a self-reinforcing cycle. Essentially, once momentum takes hold the data insights drive increased learning and better decision making that pushes the process forward more quickly at each turn of the cycle.

The concept of a data flywheel was first proposed in Jim Collin’s book Good to Great, where the term was explained as follows: “Each turn of the flywheel builds upon work done earlier, compounding your investment of effort. A thousand times faster, then ten thousand, then a hundred thousand. The huge heavy disk flies forward, with almost unstoppable momentum.”

The data flywheel is essentially a business model that uses continuous data insights to enhance and streamline operations, products, and services.

Imagine a physical flywheel – once you start spinning it, its momentum builds, needing less input over time to maintain speed. Similarly, a data flywheel starts with the collection of high-quality data, which is analyzed for insights, driving actions that lead to more data generation. This new data, in turn, feeds back into the system, creating an ever-strengthening cycle that drives growth and innovation.

Examples of flywheels in action in organizations

Although data flywheels may be a new concept to some of us, the benefit of the approach can be seen by the leaders who have plowed ahead with this approach with impressive results:

Amazon is a key example. Here, the e-commerce giant uses customer preferences, purchase history, and browsing behavior to provide highly tailored product recommendations that drive additional sales. Each transaction generates new data, allowing Amazon to refine its recommendation engine continuously.

Moving on to streaming platforms, Netflix pioneered the idea of highly refined viewing recommendations. The streaming giant analyzes user behavior, from what shows users watch to when they pause or skip, using these insights to personalize recommendations. This flywheel effect also informs content creation, as Netflix uses the insights to produce shows that resonate with its audience.

Uber offers another great example of how data is used in real time to drive business decisions. The ride-hailing app leverages data from riders and drivers to optimize routes, reduce wait times, and match drivers with the highest demand. Each completed ride creates data that improves the overall experience and operational efficiency.

What one needs to get started

In order to get your flywheel in motion, it’s important to recognize the individual spokes that contribute to the system of perpetual motion. This starts with data collection. This should be gathered from a diverse range of sources such as customer interactions, supply chain data, or digital engagement metrics.

Next, this data needs to be processed and analyzed in order to produce actionable insights. Depending on the data sources, these insights could include identifying customer preferences or locating operational bottlenecks in the supply chain. With these insights to hand, your team can make relevant adjustments such as adjusting the price point of a service offering to meet demand in a key customer segment or improving supply chain processes to deliver products more quickly.

Next, the feedback loop is a key part of the data flywheel concept. Once the initial round of insights and actions have been delivered, the outcomes of these changes should be monitored and fed back into the system to further enhance business performance. Finally, the system unlocks new business growth which then circles back to data collection.

With all of these individual tasks in motion at once, each spin of the flywheel creates more momentum. As the system creates more valuable data insights at each, the organization can innovate faster, launch refined products and enhance customer satisfaction. All of this combines to achieve sustainable business growth for organizations.

Article by Oren Askarov of SQream