Data Flywheel
Other / April, 21 2021

The number one reason you need a Data Flywheel

Data has come to be the most valuable business asset as most companies have transformed themselves wholly or partially into a digital business. They are using data :

  • to personalize search and recommended results.
  • to gain customer insights that power their products and services
  • to serve relevant ads and drive further actions
  • to power live performance dashboard and deliver customizable insights to users.

How did we get here?

  1. Over the last 5-7 years, three key trends have significantly upgraded the value of data: Connected device, apps, and systems now generate more data than ever before
  2. The cloud has reduced the cost of storage, with businesses now able to hold on to all of their relevant data
  3. The cloud provides pay-as-you-go, on-demand compute, allowing organizations to more easily analyze and gain insights from data

In many ways, connected devices and the cloud have democratized data, enabling companies of every size to use data in innovative ways that spark business growth. Organizations today are using data to determine when and how to develop and release new products, opportunities for new revenue streams, automate manual processes, earn customer trust, and more—all of these decisions fuel innovation and drive the business forward.

But what’s the best model—the best strategy—for analyzing this data and applying the resulting insights in a structured way is the critical question that enterprises are asking. The answer lies in the Data Flywheel popularized by AWS. Jim Collins popularized the flywheel concept itself—it is a self-reinforcing loop made up of key initiatives that feed into and are driven by each other.

The components of the data-driven flywheel are:

  • Moving data and workloads to the cloud

Data is growing exponentially, and on-premises solutions are becoming too cost-prohibitive and complex to keep up with demands. Cloud offers businesses to move data, workloads, and databases from on-prem for better scalability.

  • Run fully managed databases

Old-guard database providers are expensive, proprietary, have a high lock-in, and impose punitive licensing terms. Because of these issues and others, we’ve seen many companies trying to move as fast as possible to open source databases like MySQL, PostgreSQL, and MariaDB.

  • Build data-driven applications

Relational databases have been the cornerstone of every enterprise application for the past two decades. However, a “relational only” approach no longer works in today’s world. With the rapid growth of data—not just in volume and velocity but also in variety, complexity, and interconnectedness—the needs of databases have changed. Many new applications that have social, mobile, Internet of Things (IoT), and global access requirements cannot function properly on a relational database alone. These applications need databases that can store TBs to PBs of new types of data, provide access to data with millisecond latency, process millions of requests per second, and scale to support millions of users anywhere in the world. Businesses of all sizes are using purpose-built databases today to build scalable and globally distributed applications.

  • Analyze data in your data lake

Data is a difficult beast to tame. It’s growing exponentially, coming in from new sources, becoming more diverse, and is increasingly harder to process. A data lake architecture allows you to store all your data once in a single place, in open formats that can then be analyzed by many types of analytics and machine learning services—faster and more efficiently than with traditional, siloed approaches. With a data lake architecture, multiple groups within your organization can benefit from analyzing a consistent pool of data that spans the entire business.

  • Innovate with machine learning and blockchain

At this stage, your Data Flywheel has some momentum; it’s time to add to perpetual power innovation in automation and intelligence. Machine learning (ML) can provide better customer experiences at a scale that was not possible before. With ML, customers of every size and industry can gain valuable insights into their businesses with ease and speed.

With blockchain, you can store your data with integrity and ensure that you have a complete, immutable, and verifiable history of changes of all your data. Additionally, blockchain enables you to perform transactions more efficiently and removes unnecessary intermediaries.

New data-driven apps, data lake architectures, products, and services create more data that can be stored and managed in the cloud, allowing organizations to develop new capabilities and apps, gain new insights, and deliver new products. This is a step-wise, repeatable process, which must be run project by project, like turning a flywheel, building momentum with each turn.

One of the unique characteristics of the Data Flywheel is that no “one thing” powers it, and organizations that search for such a fundamentally essential solution will likely lose their way. The Data Flywheel moves by many components acting in concert, equating to a whole that’s greater than the sum of its parts.

Start spinning your Data Flywheel now!

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