Digging into the data goldmine

Many organizations today are sitting on large volumes of data — and they’re looking to turn that data into actionable insights. But most of the data they collect simply goes unused. This unused data, known as dark data, comprises more than half of the data collected by companies.

For businesses aiming to increase the usability of data collected, what steps should they be taking? If the nirvana is to have your systems make automated predictions and decisions based on analytics, how do you reach this promised land?

In the latest episode of the Cloudspotting podcast, three data experts join hosts Alex and Sai to break down the complex topic of utilizing data in your organization.

Tune in to hear about the following:

• How businesses are using data and its predictive qualities
• The data journey – from infrastructure to data science
• Using data lakes and data warehouses
• Data accessibility across a whole organization
• How the cloud combats data inertia

Mark McQuade, Practice Manager of Data Science & Engineering at Rackspace Technology, discusses the first step for organizations on their data journey. “Businesses have all this data, whether it be from IoT, social media or on-premises databases. The data is all there, but untapped, meaning they don’t know what to do with it. From a high level, the first step in a data journey, in my opinion, is getting into a data lake. A data lake is one central location to store all of your data in its raw format. You don’t have to transform that data in any way before putting it in that spot. Then you can start building on it and making those data-driven decisions.”

Alex Galbraith, Senior Manager of Solutions Architecture at Rackspace Technology, explains the impact of cloud on the data space. “Cloud made a huge impact. People talk about data gravity, which is always a bit of a misnomer for me. I think it’s more like data inertia, and the cloud has made that go away.”

Ben Morgan-Smith, Consulting Data Architect at Rackspace Technology, reveals how big data could start influencing conversations in real-time. “Where the whole big data world starts to get mind-blowing is when you can have specific machine-learning-initiated insight that allows you to influence real-time interactions with your customers. Then the power becomes quite scary. And this is where you start having to think about ethics just as much as you do about technology.”