data is the new battleground. For companies, the situation is clear – their future depends on how quickly and efficiently they can turn data into accurate insights. This challenge has put immense pressure on CIOs to not only manage ever-growing data volumes, sources, and types, but to also support more and more data users as well as new and increasingly complex use cases.
Fortunately, CIOs can look for support in their plight from unprecedented levels of technological Innovation. New cloud platforms, new databases like Apache Hadoop, and real-time data processing are just some of the modern data capabilities at their disposal. However, innovation is occurring so quickly and changes are so profound that it is impossible for most companies to keep pace, let alone leverage those factors for a competitive advantage.
Its clear that data infrastructures today can’t be static if they are to keep pace with the data requirements of the business. Today’s competitive environment requires adaptive and scalable infrastructures able to solve today’s challenges and address tomorrow’s needs; after all, the speed with which you process and analyze data may be the difference between winning and losing the next customer. This is significantly more important today than 10 or 15 years ago since companies used to make a strategic database choice once and keep running it for a decade or two. Now we see companies updating their data platform choices far more frequently to keep up.
If companies are to thrive in a data-driven economy, they can’t afford to be handcuffed to ‘old’ technologies; they need the flexibility and agility to move at a moment’s notice to the latest market innovations. However, it’s not enough for companies to simply be technology agnostic; they also need to be in a position to re-use data projects, transformations, and routines as they move between platforms and technologies.
How can your company meet the agility imperative? To start, let’s consider the cloud question.
In a data-driven enterprise, the needs of everyone – from developers and data analysts to non-technical business users – must be considered when selecting IaaS solutions. For example, application developers who use tools such as Microsoft Visual Studio and .NET will likely have a preference for the integration efficiencies of Microsoft Azure.
Data scientists may want to leverage the Google Cloud Platform for the advanced machine learning capability it supports, while other team members may have a preference for the breadth of the AWS offering. In a decentralized world where it’s easy to spin up solutions in the cloud, different groups will often make independent decisions that make sense for them. The IT team is then saddled with the task of managing problems in the multi-cloud world they inherited – problems that often grow larger than the initial teams expected.
One way to meet a variety of stakeholders’ needs and embrace the latest technology is to plan a multi-cloud environment by design, creating a modern data architecture that is capable of serving the broadest possible range of users. This approach can safeguard you from vendor lock-in, and far more importantly, ensure you won’t get locked out of leveraging the unique strengths and future innovations of each cloud provider as they continue to evolve at a breakneck pace in the years to come.
Once perhaps considered a tactical tool, today the right integration solution is an essential and strategic component of a modern data architecture, helping to streamline and maximize data use throughout the business. Your data integration software choice should not only support data processing “anywhere” (on multi-cloud, on-premise, and hybrid deployments) but also enable you to embrace the latest technology innovations, and the growing range of data use cases and users you need to serve.
