The modern enterprise is comprised of a complex set of application stacks that span a disparate variety of virtual machines, physical servers, and proprietary storage hardware. Tentacles reach from headquarters, branch and remote offices, and offshore facilities around the world to technology stacks, SaaS providers and a multitude of applications.
Over the years layer after layer of technology has accumulated, but rather than replace what came before, we simply built on top through a long series of incremental decisions and implementations. For many, mainframes were bolstered by a client-server layer that moved into data centers. Web technology added SaaS beyond our data centers before virtualization and server consolidation reorganized everything into more manageable chunks.
Mobile computing, the Internet of Things, and regulatory restrictions on data retention have all contributed to rapid data growth, making it increasingly difficult to understand, manage, and secure everything. As we move to the cloud, big data analytics, machine learning and artificial intelligence are creating more layers. We might term these layers the Archeological Dig of Technology. A typical company has many of these layers, as shown below.
Over time, the incremental nature of how these technology layers were deployed results in what customers affectionately call their “hairball architectures”; that is, we end up with lots of islands of data and applications all bolted together. The culmination of so much incrementalism inevitably results in vendor lock-in, spiraling costs to maintain and less business agility due to the compounded layers of complexity that must interconnect to handle evolving business processes and growing demands.
Embrace the complexity
The hype about each new technology layer that comes along suggests that it will be the unifying force we need, but the relentless pace of change is the only real constant. Companies need to be agile to innovate and compete. When 246 CEOs were surveyed by PwC, 75% of them agreed that innovation is as or more important than operational effectiveness for driving business success. Organizations must be in a position where they can readily adopt new tools or services to compel that innovation.
“It isn’t the biggest fish eating the smaller fish anymore, but the fastest fish that wins,” says Klaus Schwab, founder of the World Economic Forum.
It seems complexity is a necessary evil. It’s time we accepted that each new technology layer serves a purpose and adds value to our businesses. New apps will continue to be developed, new cloud services will need to be integrated, new partners will come onboard, and mergers and acquisitions are inevitable. The Archeological Dig of Technology will continue. The question is, where do we go from here? How do we evolve these critically important technology layers to increase business agility, reduce costs and gain the freedom to innovate again?
Enter the cloud era
The shift to multi-cloud environments is well underway and more new layers will follow. Gartner predicts that by 2020, 90% of organizations will adopt hybrid infrastructure management capabilities. While just 40% of the companies McKinsey studied have more than 10% of their workloads on public-cloud platforms today, 80% plan to within three years.
As more and more companies migrate their technologies to a mix of SaaS platforms and public clouds, complexity continues to be an issue. But the cloud is a combination of virtual computing and platform services that’s both backward compatible and forward leaning – it can accommodate existing workloads, but also makes it easier to create new services and applications.
For all the tantalizing business advantages it may afford, digital transformation is challenging to realize and there’s no simple way to tame all this complexity. But if we peel the onion, we find one common element at its core – data. It’s just not possible to rewrite all the applications, transform all the data and unwind the hairball architectures simultaneously.
Data is your foundation
With the shift towards cloud services and off-the-shelf, scalable infrastructure, data is increasingly valuable as a way for businesses to differentiate themselves from the competition. For many companies, it’s vital to collect, safely manage, and analyze huge amounts of data. If the promise of artificial intelligence is to be realized, companies must go beyond collecting data to develop the ability to process and apply it. Those who succeed in harnessing and leveraging their business data properly will excel and get a return on investment on the data. Those who do not will continue to see data as just another expense instead of the business fuel it could’ve been.
Without a proper data strategy, achieving multi-cloud and hybrid cloud diversity and integration is going to prove costly, time-intensive or even impossible. We can’t allow data to be tied down and isolated within platform-specific “storage” devices. To take full advantage of the cloud as a combination of virtual computing and platform services, we must set data free and make it accessible anywhere it’s needed without compromising on its integrity and security.
If the data layer is virtualized, it becomes flexible enough to quickly and easily adapt to the new multi-cloud environment. Establishing real-time control of data will enable rapid construction of hybrid clouds, IoT integration and the consolidation of layers across multiple clouds. Achieving this requires careful planning because there are many common cloud data management pitfalls to avoid.
Creating a virtualized, multi-cloud data foundation on which to build your new technology layers, and tap into the existing layers, affords you the stable base that’s required for true business agility.
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