I kicked off my career back in the ’90s as a software engineer working on the space shuttle program and as a result I’ve seen some pretty amazing developments in aerospace technologies. However, I’d have to say that they pale in comparison with the staggering advances we’ve seen in cloud computing in just a few short years. And get this, where we’re going next is even more exciting.
Back when I changed my career path to focus on cloud computing startups, it was extremely challenging to get a high-tech startup off the ground. You needed significant investment – millions of dollars’ worth of hardware and software purchases – to bring your product to market. Even getting a simple email server up and running in a reliable fashion was a headache. Then there was the challenge of IT talent acquisition. Just to get a simple database up and running, you needed to have high-priced DBAs on your staff.
“Cloud is a real game changer.”
Cloud computing puts a layer of abstraction over physical hardware and lowers the cost and expertise barrier to entry. For example, you can get database as a simple, managed service from vendors such as AWS and Google Cloud that employ the world’s foremost database experts. This allows today’s startups to focus on their core competencies and bring products to market in a matter of months rather than years. Going back to the email server challenge and how SaaS has basically eliminated the problem, today Google’s G Suite has become the gold standard office package for most startups, even large enterprises.
The best isn’t yet to come – it’s here now
Now we find ourselves in a world where cloud technologies continue to expand, while at the same time the data generated by consumers and organizations is exploding. The rise of Big Data and the Internet of Things is the stuff of everyday headlines, but what makes the current landscape so exciting is the combination of those trends with one that has received much less attention – the reemergence of artificial intelligence (AI), specifically of machine learning.
Historically, interest in AI tends to wax and wane, and in the early ‘90s the field was definitely in a “winter” period (see for example Tanya Lewis’s article A brief history of artificial intelligence). But by the early 2000s AI was hot again, and it remains white hot today. That’s especially true of machine learning, a branch of AI that enables computers to generate analytical models that detect patterns in data and independently adapt as they’re exposed to new data – to learn, in fact. This technology holds great promise as a way for companies to make sense of the massive amounts of data currently lying around untapped in their technology infrastructure.
“Companies are already using machine learning to turn big data into big rewards.”
In fact, for some data sets, companies are already using machine learning to turn Big Data into big rewards. Customer relationship data is one area that’s ripe for a new approach. Businesses tend to accumulate this information in somewhat haphazard fashion across their ERP, CPQ, CRM, contract management and billing systems. As a result, they typically overlook important clues to customer behavior and miss out on opportunities to build revenue. Any organization that can fully exploit its customer data assets can build a significant competitive differentiation by maximizing customer relationships in ways that weren't possible before.
Pramata helps companies do exactly that by leveraging machine learning to detect patterns in their data that provide deep customer insights and situational awareness. Pramata's cloud platform enables companies to digitize their customer relationships by extracting key information from contracts, and then supplementing that with data from CRM, CPQ, billing, and other enterprise systems to provide a single, comprehensive view of the customer.
It could be the start of a transformation in the ways enterprises relate to their customers – one that’s just as far-reaching in that sphere as the impact of the cloud on start-ups.