The transformation of businesses to digital enterprises was supposed to create an enormous market opportunity for the storage industry.
As companies such as Netflix and Rosetta Stone transformed from DVDs and CDs to online data delivery models, their data footprint increased exponentially every year. In fact, digital transformation is increasing the data we store by over 2.5 exabytes every day. That’s equivalent to 530 million songs or 250,000 libraries of congress or 90 years of HD video! Each and every day. 100% growth every year.
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Conventional wisdom held that all this data needed to be stored somewhere, and the market outlook for the storage industry never looked better. Instead, there has been a rapid commoditization, consolidation and implosion of the storage market over the past three years, culminating in Dell’s acquisition of the market leader, EMC, in September of last year.
Why didn’t the prevailing wisdom prove true? Radical changes in what I call “Datanomics” drove the adoption of data and storage virtualization technologies, resulting in the complete disruption of the storage industry.
What do I mean by Datanomics? I’m referring to the economics of data over its lifecycle. Just as economics is the branch of knowledge concerned with production, consumption and transfer of wealth, Datanomics represents the knowledge concerned with production, consumption and transfer of data for large corporations and enterprises.
Digital enterprises operate with radically different Datanomics than conventional physical businesses. Suddenly digital information is the business. On one hand, that means there is exponentially more data to store and manage. But on the other, it also means a fundamental difference in how that data needs to be stored and managed.
Rosetta Stone tackles data explosion
How did radical changes in Datanomics drive the adoption of data storage virtualization technologies, resulting in the complete disruption of the storage industry?
Let’s take a closer look at Rosetta Stone’s digital journey. The company transformed into a digital business with an increasing amount of online offerings, which resulted in an explosion in the amount of data that had to be stored. The problem was there wasn’t enough space in its physical data centers to store it. It had to figure out how to be “datanomical” with its overflowing data and look into alternative ways to store and manage its data.
Rosetta Stone looked to copy data virtualization to better manage and store its data. Now the company can virtualize its data, hardware platforms, and storage and backup applications. Data is easily accessible and recoverable, and resilient, where it once wasn’t.
In the instance of a digital enterprise, data value is no longer a conveniently predictable model of exponential decay based on age, but one that has rapidly varying value throughout its lifecycle. The age of data no longer dictates the worth of data. Data availability and manageability does.
Over the next few months I’ll be exploring this concept of Datanomics in more detail—covering everything from its changing role in the transformation of businesses into digital enterprises, to harnessing Datanomics with business intelligence. I’ll share insights from my conversations with companies of all sizes around the world to bring to life how this concept works in practice.
Here is a sneak-peek of what I plan to expand upon in the coming months.
Datanomics, digital enterprises and the disrupted storage industry
There is exponentially more data and more users of data in digital enterprises. It means there also needs to be a fundamental difference in how that data needs to be stored and managed. I’ll look at virtualization as a solution to overflowing data centers. Infrastructure virtualization of servers is already understood, with the rise of public cloud services like Amazon Web Services. Data virtualization, which is less widely known, is now being rapidly adopted as the foundation for transformation to a digital enterprise.
Managing Datanomics with real-time business intelligence
The agility requirements of a digital enterprise resulted in continuous development of core business applications and continuous management, delivery and analysis of data. I’ll explore how a DevOps IT model with a Continuous Integration and Continuous Deployment (CI/CD) software development model and use of analytics or machine learning tools to analyze data are an integral part of the modern digital enterprise.
Harnessing modern Datanomics to win
In a digital enterprise, Data appreciates and infrastructure depreciates. IT departments have needed to radically change the technology they use to manage and use their data most effectively. This piece will dive into the steps IT departments are taking to manage their data and sustain a competitive advantage in their line of business.
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