Exposing the Dangers of a 'One-Size-Fits-All' Cloud Analytics Strategy

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As technologies evolve and become more sophisticated each year, the world is becoming increasingly data-driven. Large volumes of data inject macro- and micro-level insights into our behaviors, opinions, and preferences, which in turn power business opportunities for revenue growth, cost reduction, and customer satisfaction.

To capitalize on this substantial technological transformation, enterprises need to assess the best solutions available to store, manage, and utilize  data in order to boost return on investment. This becomes even more important in situations where enterprises need to respond quickly to  fast-changing business environments such as during the COVID-19 pandemic.

As business needs fluctuate and expand, one significant competitive advantage is provided by  readily-accessible data and analytics to help reveal new or emerging insights. When such programs are implemented in the cloud, companies can scale their capabilities quickly. This explains in part why enterprise spending on cloud services has grown so fast. In fact, Gartner predicts that the global public cloud services market will grow by 17%this year to total $266 billion.

Data analytics in the cloud removes the burdens of information technology implementation and onsite infrastructure management. This is particularly important as enterprises across all industries face a significant skills gap. And with technologies morphing from one innovation to the next each and every year, that gap continues to widen.

Amid this whirlwind of change, companies may be tempted to simplify their supply chains and turn exclusively toward cloud-native services, opting for a “one-size-fits-all” strategy which mandates that all data and analysis be confined to only one type of cloud architecture. Often, this entails companies putting the entirety of their analytics capabilities into the hands of a single public cloud service provider.

However, it is short-sighted to simply check the box on analytics services offered natively by a  cloud provider and expect to yield anything other than least-common-denominator outcomes. In other words, just because a cloud service is offered does not mean it is anywhere close to sufficient for delivering the enterprise-class capabilities that executives need to transform business.

The Disadvantages of the “One-Size-Fits-All” Approach

Choosing a single cloud strategy and procuring all IT services from a single provider can result in  several disadvantages and companies can run the risk of trading away long-term strategic advantage for short-term tactical convenience. Specifically, the one-size-fits-all approach handicaps enterprises on:

  • Data gravity. The concept of data gravity refers to the tendency of data to attract applications, services, and other data to it, essentially accumulating mass and becoming more rooted in place the larger it gets. As a repository accumulates more and more data gravity, it becomes much harder for an enterprise to move that data and its associated services elsewhere. If all of a company’s data and analytics are centralized within a single public cloud provider, then, it becomes much more likely that thecustomer will become a victim of self-imposed vendor lock-in, unable to relocate their analytics without significant cost and disruption down the road.
  • Lack of flexibility. As technologies evolve and their usefulness changes, enterprises need to retain as much flexibility as possible to shift budget allocations, resource utilization, and priorities to stay ahead and not lose ground to others within their industry. Flexibility becomes increasingly important as firms embrace the importance of data privacy and governance, for example, as evidenced by strict compliance regulations such as GDPR in the EU and the CCPA in the U.S. Maintaining agility means keeping control over one’s data assets and being ready to pivot when a new competitor enters the market.
  • Least common denominator capabilities. Although a one-size-fits-all strategy may result in less upfront work to provision systems and manage infrastructure, sacrificing analytics sophistication for generic functions that have been designed for a broad spectrum of “typical” users with myriad use cases is not a recipe for success. Think about the service station where one gets gasoline. Just because it also sells drinks, chips, and candy, would it be a good idea to forego shopping at an actual grocery store? No, it would not. There is a tradeoff between off-the-shelf convenience and nutritional value. Likewise, for enterprises that aspire to differentiate themselves with more than run-of-the-mill analytics, a one-size-fits-all strategy will not provide what is truly needed.
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