If you look back at the technology that was trending at the start of this decade, you’ll recognize a lot of the predictions. Google began testing self-driving cars. Facebook was poised to take over the world, soaring to become the most popular online site. Apple debuted the iPad and opened its app store. Big Data and the Internet of Things were not yet a reality but predicted to be game-changing (as indeed they are.)
But looking back, some of the predictions for the decade did not come true, or came true in ways that can only be understood in hindsight. Facebook continues to be a leading social media site, but is embroiled in data privacy and other issues, while its latest endeavor, Libra currency, is mired in regulatory issues. There was lots of buzz around Big Data, Hadoop and NoSQL – and data volumes continue to grow – but those technologies have not made relational databases obsolete. Instead, Big Data is feeding machine learning models, most of which are still collecting enough data to deliver effective AI solutions like self-driving cars.
Right now AI sits at the top of most tech prediction lists, but perhaps like blockchain, it is a technology looking for a use case, generating buzz beyond its ability to deliver real change. Success takes time and investment – those chatbots aren’t going to write themselves. In contrast, we’ve seen the Cloud become a truly (to use another buzzword from the decade) disruptive technology.
Perhaps one very simplified way to look at why a technology becomes a success is to see it as the convergence of three factors:
- the time it takes to ramp up,
- the ability to make use of existing assets instead of requiring significant investments, and
- the ability to directly impact cost savings or profitability.
In the case of the Cloud, companies can move to it fairly quickly, even lift and shift their legacy applications there. Unlike on-premises hardware and software, the Cloud doesn’t require significant capital investment. In fact, it offers opportunities for cost savings and revenue growth. No surprise then that cloud usage has been strong.
Data analytics is another example that meets all these criteria. Organizations that adopt analytics can get started pretty quickly making better decisions using their data. Much of that data is an existing asset, and if it is relational data, much of it will be clean and ready to use. Organizations that have seen their data volumes escalate understand the urgent need to understand the insights in their metrics. And they’ve seen that other companies have had success saving money or uncovering other sources of profit using analytics.
That’s why, analytics, in some shape or form, continues to trend in Gartner’s forecasts, and why Gartner predicts growth in citizen data scientists. This year we’ve seen additional confirmation of this in the form of several large investments in analytics companies by Google and Salesforce.
Will other technologies, like AR and blockchain, become the game-changers of this next decade? It’s still possible. But in the meantime, organizations cannot continue to fly blind, guided by gut instincts with no basis in reality. To know where they need to go, they need analytic insight into their current operations. Without that, they won’t be able to set a course towards the next innovation.