Public cloud is about so much more than cost-effective data storage. The speed, flexibility and processing power it offers are now must-have components of modern analytics and machine learning capabilities.
A democratic revolution in analytics and AI is underway–and public cloud is at the center. By leveraging the speed and flexibility of a cloud architecture like AWS (Amazon Web Services), a business can gain access to capabilities that were once prohibitively expensive to all but those with the deepest pockets. So much so that public cloud is now a vital part of any modern insight-driven business strategy.
How so? It’s because today’s analytics and machine learning tools all rely on processing large volumes of high-quality data. And with so much data now flowing continuously through digital ecosystems, organizations have near-limitless seams of rich datasets to mine.
But collecting data is one thing–using it effectively is quite another. To extract meaningful insights through analytics and machine learning, an organization needs sophisticated infrastructure and vast amounts of processing power. They need to store, process and serve huge volumes of data quickly and efficiently. And they need skilled talent to provision and manage the necessary infrastructure.
That can be seriously costly. For smaller businesses, prohibitively so. But, equally, many larger organizations can find it just as challenging–particularly those with business models that predate the digital era.
What’s more, with so much of today’s data being unstructured (webchats, emails, documents, videos, images, voice interactions) or, at best, semi-structured (web/mobile/IoT logs, clickstreams, social media), the volume and complexity of the data a business must now handle is fast outpacing their abilities to do so.
Public #cloud has the capability to monetize today’s massive data loads. What’s holding business back?
Traditional analytics environments just can’t provide enough visibility into these rich datasets. Normalizing, tagging, annotating, and preparing these sources calls for networking, storage, and computing on a much bigger scale. On top of that, analytics systems must be able to handle huge variations in data flows, with peak data bursts in some industries reaching 100 times normal volumes.
Because public cloud environments offer the basic constructs and processing power needed to ingest and process this kind of data flexibly, at scale, businesses can monetize their datasets that much more effectively. In fact, those that aren’t leveraging public cloud for their analytics and AI risk finding themselves at a serious competitive disadvantage.
So what’s stopping every organization doing so? For many it’s a question of skills. A recent IDG survey suggests 41 percent of businesses consider data analytics one of the top-two in-demand skills. But data scientists are in short supply–and salaries are skyrocketing as a consequence.
The answer? Strategically reassess the entire organization’s approach to data, developing a sophisticated understanding of the various roles and responsibilities in the data insights and AI pipeline.
Most likely that starts with the appointment of a chief data officer and the definition of a data strategy, including a monetization baseline. It will certainly involve identifying the data providers and data consumers within the organization and mandating them to ensure data quality. A cross-functional center of data science excellence can be another key move in establishing the right kind of data-driven culture.
All that is easier said than done, of course. Which is where external partners can make a significant difference. IDG’s data suggests more than a quarter of companies are choosing this route. And for good reason–by outsourcing analytics capabilities, developing data strategies or fortifying internal teams, the proven expertise of these third parties can transform an organization’s data-centricity so much faster.
Whichever route a business takes, one thing stays the same. Public cloud is now a cornerstone of modern analytics and AI.