Understanding the new landscape of data in the cloud

As the nature of enterprise data becomes more dynamic, gone are the days of large CAPEX investments in physical hardware for storing and organizing our data. The uptake of cloud-everything has enabled nearly every service imaginable to be consumed on a consumption-based basis – causing a complete rethink of the way we manage our data across a range of environments.

With enterprises now operating the complete gamut of on-premise, public/private and hybrid multi-cloud infrastructure environments, we’ve seen the appetite for cloud services showing no sign of slowing. Accelerated by the pandemic, Gartner has found the worldwide public cloud services market has grown by 6.3% in 2020 to total $257.9 billion, with Software as a service (SaaS) remaining the largest market segment at $104.7 billion.

However, we know the shift to cloud isn’t an all or nothing proposition, as enterprises have learned that not every database or workload belongs in public or private clouds. Hybrid multi-cloud has become a key deployment model, with Flexera research earlier this year finding that 93% of enterprises having a multi-cloud strategy, while 87% have a hybrid cloud strategy across an average of two public clouds and two private clouds.

Accordingly, our analytics and data integration strategies need to move with these models of infrastructure consumption. The rest of the enterprise is deploying workloads in the best environment for balancing performance, scalability, speed, costs, and compliance – and our data platforms need to follow suit.

Democratizing analytics and data integration

The good news is that the move to SaaS for analytics and data integration tools has opened the door for a greater number of organizations of every size and scale to begin deploying the types of capabilities that were previously reserved for the largest of enterprises. As more organizations have sought to democratize data throughout every level of their organization, the availability of cloud-based platforms is proving to be a vital catalyst.

While the availability of powerful, scalable analytics in the cloud can be a great equalizer, it also requires extensive thinking around data security.  Successful cloud deployments have several key ingredients. First, authentication strategies including single-sign-on are vital to ensure that the same data restrictions that one has in the applications landscape remains in the cloud. Secondly, careful work around the tagging and proper treatment of data, especially PII date governed by regulation and policy is essential, which means having a well thought out governance organization and process.  

Additionally, a secured data catalog will enable the right users to access the right data in a frictionless and auditable way. And finally, modern data architectures must deliver on the promise of DataOps for analytics, enabling seamless promotion and sharing of data assets and analytics in a governed manner.

We know enterprises want insights at scale throughout their organization and across their ecosystem, and need flexibility and choice when deploying analytics. Qlik is meeting those needs head on with our latest innovations in multi-cloud and augmented intelligence. With our SaaS-based platform, Qlik delivers real-time data and analytics to organizations where and how they need insights, helping them lead with data when solving their most difficult challenges.

About the author

As the CDO at Qlik, I walk in our clients’ shoes by making sure Qlik's data processes, management and tools are aligned. If you’d like to discuss how you can start democratizing data in your business with a modern data architecture using proper data governance processes and tools, please get in touch with me at [email protected].