The data science market is flourishing, with an increasing number of companies across sectors placing data at the forefront of their digital transformation strategies. The rise of data analytics has seen the demand for data scientists and data engineers tripling over the past five years, rising by as much as 231%. Yet as many businesses rush to hire the talent they need to make their plans a reality, many are still on a journey in realizing the full value that their data can offer.
Organizations that previously used legacy architecture often face challenges when attempting to retrofit their systems to the cloud. As a result, it can be difficult to adapt, and habits and biases from the on-premises world can limit the understanding of what’s possible in the cloud. Data scientists, data engineers and developers are all having to acclimate to their new cloud environments and a rapidly evolving ecosystem of tools and frameworks. With many learning on the job, businesses risk not maximizing the potential of their cloud architecture.
If harnessed correctly, the cloud can revolutionize data science and create an exciting frontier for companies to better understand customers, monetize data in new ways, and make predictions about the future. Data teams now have access to a vast pool of elastic computing power, as well as numerous sources of internal and external data. Managed cloud services are also available to reduce the complexity of building, training and deploying machine learning and deep learning models at scale. Here are six tangible strategies that businesses can follow to make the most of data science in the cloud.
It’s critical for businesses to enable iteration and investigation into data without compromising governance and security. Before they start working on a dataset, many data scientists intuitively want to make a copy of the original. But all too often copies are made and forgotten about, creating problems when it comes to compliance, security and privacy. A modern data platform should enable data teams to work on snapshots, or virtual copies, without needing to duplicate entire datasets, all while maintaining fine-grained controls to ensure that only the right users and applications have access to the data in hand. Businesses must create processes that minimize duplications to ensure internal and external data governance policies are being met.
Pre-existing biases from operating on-premises can often hold businesses back and hinder them from focusing on what they wish to achieve with their data. For example, one common misconception is when data scientists say: “I’d love to retrain my model several times a day, but it’s too slow and will delay other processes.” But that’s not an issue in a world of elastic infrastructure. When migrating to the cloud, it’s therefore important for companies to recognize the full breadth of new capabilities on offer in order to dispel any previous biases that are no longer relevant within a cloud model.
Removing any preconceived perceptions will empower businesses, to make the most of their data and be ambitious. Once in this position, data teams must start with what they want to achieve, not what they think is possible, and move forward from there. That’s the only way they can push boundaries and take full advantage of the cloud.
Closely tied to data governance is the concept of silos – these occur when data sits separately from each other, meaning no one person or team in an Organization has a holistic view of all the data in its possession. The proliferation of tools, platforms and vendors is great for innovation, but it can also lead to redundant, inconsistent data being stored in multiple locations.
