There’s a lot of talk about data literacy in the enterprise. Ishit Vachhrajani, Enterprise Strategist at Amazon Web Services (AWS) and the former Global CTO at A+E Networks, prefers the term “data proficiency.”

“It’s not just about being data-aware or being data-informed, but really developing proficiency to put that data to use, and do that at scale,” Vachhrajani told Tim Crawford in Episode 6 of the Ahead of the Pack podcast.

Data proficiency should apply across the enterprise, beginning with the C-suite and extending beyond the roles that traditionally deal with data, he said. But the challenge for many organizations is infusing this mindset.

The best applications of machine learning occur when algorithms augment human activities instead of replacing them. That’s the case with T-Mobile, which has deployed machine learning to help contact center agents better serve customers.

In this episode of the Ahead of the Pack podcast, Heather Nolis, Senior Software Engineer with T-Mobile, talks with Tim Crawford about the telecommunications company’s approach to artificial intelligence and machine learning and the impact they’re seeing across the business.

The cloud has disrupted every element of the technology stack, including databases. The days of expecting one monolithic database to serve all of an organization’s needs are quickly drawing to a close, in favor of purpose-built databases and tools that address specific use cases.

“It is no longer a one-size fits all world,” Shawn Bice, Vice President, Databases at AWS, said in Episode 3 of the new Ahead of the Pack podcast series. “While the way we built applications for the past 30 years may be familiar, don’t let that turn into a blind spot that stifles future innovation.”

Bice said any concern about moving business-critical workloads to cloud-native architectures should be tempered by all of the learnings gathered from early adopters.

“You have an amazing opportunity to learn from the first movers,” he said. “How they built applications for the cloud is vastly different than traditional application development. A lot of these early adopters have carved out the lanes to follow.”

Instead of purchasing technology and expecting it to solve a variety of problems, modern environments allow IT teams to start with the use case and then pick the right technology to solve the problem.

“Always start with the business problem,” Bice said. “That might sound trivial, but when you truly understand the use case, that lets you think about the access pattern. That leads you to find the database API that’s most specialized for that access pattern.”

With this approach, he added, “you’re putting yourself in a position to innovate faster than ever before, at a lower cost, with a faster time to market, because you’re not limited by any one thing you can do.”

How the auto racing brand is using data and machine learning to change the experience both on the track and in the stands.

CIOs and their C-suite colleagues understand the value of data. Many have also come to realize that legacy IT infrastructure is not sufficiently flexible or scalable to help them turn data into insights and actions that benefit the business.

In Episode 2 of the AWS-sponsored Ahead of the Pack podcast, host Tim Crawford talks with two experts on the building a modern data architecture: Herain Oberoi, GM of Databases, Analytics, and Blockchain Marketing with AWS, and Elliott Cordo, VP of Technology Insights with Equinox Media, part of the Equinox Group of lifestyle and fitness brands.

Enterprises are looking for ways to capitalize on data to accelerate their business agility. Artificial intelligence (AI) and machine learning (ML) are playing a significant role in helping organizations capture these opportunities.

In the first episode of the new podcast series, Ahead of the Pack, Sri Elaprolu, Senior Manager of the Amazon Machine Learning Solutions Lab, speaks with host Tim Crawford about the promise of ML and the real-world business benefits it brings to the enterprise.