Start tracking these KPIs for a successful migration of your data infrastructure to the cloud
As enterprises are going digital, the amount of data they have to handle has exploded. New uses cases like machine learning and artificial intelligence are forcing them to re-think their data strategy.
It’s a new world where data is a strategic asset – the ability to manage torrents of data and extract value and insight from it is critical to a company’s success. A recent survey of 2,300 global IT leaders by MIT Technology Review Insights found that 83% of data-rich companies are prioritizing analytics as much as possible to gain a competitive advantage.
That’s where “data lakes” come in, as the underlying technology infrastructure to collect, store and process data.
The constraints of legacy data infrastructure
The original term “data lake” comes from the on-premise Hadoop world. In fact, many enterprises today still run their analytics infrastructure on-premise. But despite years of work and millions of investment into licenses, they’ve got little to show. And now this legacy analytics infrastructure is running into four major limitations.
Limited data. With on-premise infrastructure, data is often distributed across many locations. That creates data silos and a lack of a comprehensive view across all the available data.
Limited scale. Scaling your infrastructure to add new capacity takes weeks and months, and is expensive.
Limited analytics. The system runs canned queries that generate a descriptive view of the past. Changing these queries and introducing new tools is another time-consuming process.
Limited self-service. IT is the gatekeeper of all data. It’s a static world, where the consumers of the data have little to no control over changing the queries.
It’s this set of constraints that are driving enterprises to adopt the cloud for their data and analytics infrastructure.
Shifting your data to the cloud
The “new” data infrastructure combines cheap storage