Building a Data Science-Friendly Organization

To succeed with analytics, organizations need to start their journey with a set of business questions that they want to answer with data analytics, says Dean Samuels, a lead technologist at Amazon Web Services (AWS). This should then be matched to available data sources which can be used to provide those insights, before making a decision on the best technology to ingest, store, analyze, and process the answers.

Unfortunately, many organizations do this backwards when they make technology-driven decisions rather than business-driven ones, he explained in response to a query by CDOTrends: “They may want to get on the technology bandwagon without focusing on what they want to achieve as a business.”

With that out of the way, the actual data science journey begins when organizations start collecting meaningful data. On that front, Samuels suggests that businesses adopt a “working backwards” methodology when it comes to setting up their data infrastructure.

This typically entails focusing on the desired result and using it to figure out what needs to be done. “The approach ensures our teams are focused on our customers wants and needs. We encourage our own customers to take the same approach and work backwards from their end state,” Samuels said.

“They need to leverage a modern data platform. This is to ensure that their data infrastructure can scale and provides flexibility to provide the insights they need. Their data infrastructure should be able to ingest and store, process and analyze – with storage decoupled from compute, and visualize their data, all whilst being secure.”

As awareness of the value that citizen data scientists can bring increases, what are some ways that organizations can best support them? The key here is having the leeway for experimentation, according to Samuel. “Experimentation is key to innovation. Data scientists should try out new ideas by having a well-defined hypothesis, data, metrics to prove or disprove the hypothesis, and the ability to pivot to the next idea.