Many people think because we’re an online business, with technology at the heart of everything we do, we’d be better placed than many to make the transition from in-person meetings to virtual meetings during the pandemic. Whilst we have made that leap and made it work well, it’s not because we’re au fait with tech and the latest tools, it’s quite the opposite.
These days, there are two kinds of businesses: data-driven organizations; and companies that are about to go bust. And often, the only difference is the data stack. Data quality is an existential issue—to survive, you need a fast, reliable flow of information. The data stack is the entire collection of technologies that make this possible. Let's take a look at how any company can assemble a data stack that's ready for the future.
There is nothing permanent except change. In a world of turbulent, unpredictable change, we humans are always learning to cope with the unexpected. Hopefully, your machine learning business applications do this every moment, by adapting to fresh data. In a previous post, we discussed the impact of COVID-19 on the data science industry.
Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. The implications of ML on the future of business are clear. However, only 4% of enterprise executives today report seeing success from their ML investment.
Start-ups founded on analytics have been shaking up every industry. Finance has been disrupted by Monzo's data focus, Netflix’s analytics has upended film entertainment, and Swyfft has used data to change the game for US home insurance. Today's users have come to expect analytics in their applications.
The pandemic has accelerated enterprises’ digital transformation investments, notably their efforts to use AI and the cloud to meet rising customer expectations.
A watershed year brought continued rapid growth, major engineering advances, and partner awards from Google, AWS, Snowflake and Databricks.
It is a truth universally acknowledged that the culture change side of any technology transformation program is the hardest and slowest part to get right. If you cannot efficiently operationalize a technology investment, that investment is wasted. This is no different in the world of APIs and microservices, where every service is designed to support a change to a digital-first culture. APIOps makes this change possible.
Usage-based pricing, consumption-based pricing, and PAYG (Pay As You Go) are relatively new SaaS pricing models that enable you to drive both top of line growth while also increasing net revenue retention over more traditional subscription pricing models such as license or seat-based pricing. With Pay As You Go, a customer only needs to pay for what they consume such as hours of a VM or number of messages sent.
