Eavesdropping on conversations around Silicon Valley might sound surprisingly medieval these days, as CEOs of the top tech companies are constantly discussing the sizes of their ‘moats.’
In fact, the buzzword has popped up 89 times in the past year in company presentations, earnings calls and tech events.
And no, Google and Facebook haven’t built grand medieval fortresses surrounded by water outside Palo Alto, California; instead, they’re using the word to invoke the protective network perimeter around their digital platforms.
Why we need digital moats
As the global economy has galvanized around a vast network of digital platforms, mounting defenses around these platforms has become key to ensuring market dominance.
In short, a handful of firms control powerful digital platforms and tremendous amounts of data (Amazon, Google, Facebook, etc.). Their business models, which successfully leverage network effects and advanced technologies, have created virtual monopolies in cloud computing, digital advertising, e-commerce and social media.
However, new disruptions in cloud computing and mobile technologies are making waves in the incumbent players’ moats.
Data Analytics: Diving into the Data Lake
Bigger, better moats
Successful startups are leading with a dual-pronged strategy of attacking legacy moats, while building their own based on their disruptive tech.
These new moats aren’t focused on a singular data platform, but instead cross multiple data sets and platforms – for example, one might look like an application that combines web analytics with customer data and social data to predict end-user behavior or lifetime value of a customer, or simply to serve more timely and targeted content.
Companies focused singularly on technology without putting it in the context of end users will soon find their moats drying up. Ultimately, the battle is moving from the old moats (the sources of the data) to the new moats (what companies do with the data).
This shift provides entry to artificial intelligence (AI) and machine learning. These technologies are driving companies’ shifts to becoming customer-centric rather than data-centric organizations.
These new technologies are supporting widespread business transformation. Combining human expertise with machine intelligence can be powerful – human interpretation alone can miss contextual clues in massive data sets.
In looking to broaden a company’s defenses, the answer seems to be digging its moat wider, not deeper. Focusing more on one specific data set will leave a company in the dust. Instead, broaden your focus to translate consumer data into actionable intelligence with machine learning and AI to spur commercial success.
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