According to a recent TechRadar article, the demand for data scientists and data engineers has tripled over the past five years, rising by 231 percent. Clearly, organizations are investing heavily in data analytics talent. However, many have yet to fully realize all of the benefits their data can deliver. 

How can companies accelerate data analytics ROI? One consideration is taking more advantage of the opportunities presented by the cloud. With that in mind, here are six strategies to help you make the most of data science in the cloud.   

  1. Don’t Compromise on Data Governance

Many data scientists are in the habit of creating a copy of a dataset before they begin working on it. However, if these copies are forgotten about (which is often the case), numerous issues can arise in the form of compliance, security, and privacy. A better approach is to have teams work on “snapshots” or virtual copies. These eliminate the need to duplicate entire data sets and help ensure that only the right users and applications have access to the data in hand. 

  1. Start with What You Want to Achieve

When migrating to the cloud, it’s important to recognize all the new capabilities the model delivers and remove any previous biases that are no longer an issue within a cloud model. From there, it’s easier for teams to think about what they want to achieve—rather than just what they think is possible. 

  1. Create a Single Source of Truth

In order to get the most out of a cloud deployment, it’s critical to eliminate data silos and obtain a global, consolidated view of all enterprise data. 

  1. Capitalize on New Tools and Technologies

Data science frameworks and tools are maturing rapidly. As companies search for a cloud platform, it’s important to select one that can scale and evolve in tandem with these new technologies. 

  1. Embrace Third-Party Data

The cloud makes it much easier for companies to incorporate external data from partners and data-service providers into models. This can deliver a number of benefits, including helping organizations uncover new revenue streams and better monetize their data. 

  1. Don’t Overcomplicate the Process

AI, ML, and deep learning play a critical role in certain business needs, but that doesn’t mean they are essential for every function. As a general rule, start with the simplest solution to the problem at hand and increase complexity as needed. This approach also makes it easier for data science teams to transition from legacy architecture and accelerate the delivery of insights.

For more on data science in the cloud, take a look at some of these previous APEX of Innovation posts.