In 2013, research stated that “90% of the world’s data was generated in the last two years”. The volume, variety, and complexity of data continue to grow as new streaming and web-based services are created. As the Internet of Things becomes more common in industrial and commercial applications, devices, vehicles, and sensors will soon produce terabytes of data a piece, per day.
We have the plummeting cost of storage, computation, and internet bandwidth to thank for the profusion of data and products derived from data. Technological change has made these powerful capabilities accessible to small organizations with limited resources.
By radically improving accessibility to powerful business tools, this rapid technological change has also enabled disruption at an unprecedented speed. In 1964, a company stayed on the Fortune 500 for an average of 33 years. By 2016, the average length of tenure shrank to 24 years. By 2027, it is projected to be 12 years. Competitiveness and survival increasingly hinge on quick, informed decisions.
Ride the wave or sink
These trends present both opportunities and challenges to how you construct your organization’s data stack.
On the one hand, your organization can avail itself of an ever-expanding array of cloud-native products and services, including customer relationship management, enterprise resource planning, event tracking, payment processing, and more. Using data from these sources, your organization can quickly discover new opportunities and stay on the leading edge of your industry.
On the other hand, that same ever-expanding array of data sources poses an extraordinary burden to your engineers who must build and maintain a staggering range of data connectors and orchestrate the flow of data. If your data infrastructure is maintained on-premise, the difficulties are further compounded by the need to extensively design and build the exact hardware configurations required, especially as your organization’s needs grow. Even worse, your data integration and analytics activities might compete with each other for the same computational resources.
To remain competitive and scale smoothly, you should leverage the proliferation of managed services that has accompanied the growth of data in the cloud. A managed data pipeline allows you to take advantage of outside expertise and web-based resources. A cloud data warehouse will likewise allow you to take advantage of elasticity, performance, and low cost. Overall, moving your data stack to the cloud will preserve the labor and expertise of your engineers, reduce your operational costs, and eliminate much of the downtime that might cause you to miss important opportunities.