“Tech Connects,” Dice’s podcast, digs into the tech hiring, recruiting, and career topics that matter to you. Subscribe on ACast, Spotify, Apple Podcasts, iHeartRadio, Amazon Podcasts, and YouTube!
The latest episode of the ‘Tech Connects’ podcast is here! Our latest guest is Shadi Rostami, who’s SVP of engineering at Amplitude, which builds a unified data analytics platform, among other products. She’s built and run engineering teams and spearheaded the development of products and services incorporating Big Data, cloud computing, and much more. That background gives her spectacular insight into the rise of “data democratization,” which is the ability for employees throughout the organization to gather and analyze data without much training or assistance from data scientists, data analysts, and other experts.
Many companies over the years have pledged to design tools and platforms to make data democratization more of a mainstream thing, and we chat about the current state of those efforts—is data democratization gaining momentum, or is there still much work to be done? Is it possible to make an entire organization data literate? And how does that change the jobs of data scientists and other experts? Let’s listen in!
If you’re interested in analyzing data for crucial insights, it’s more important than ever to be aware of how the push for data democratization and literacy are changing organizations of all sizes. Here are some key takeaways from our chat with Shadi:
First, as companies collect and analyze more data, there’s more pressure on data analysts and scientists to deliver results for the organization. This results in the “data breadline” in which employees are lining up for their local data expert’s precious time and expertise. It’s not necessarily a sustainable system, which is why many companies are actively trying to figure out how to best give their employees the tools and datasets they need to perform effective data analysis.
Second, companies need to walk something of a tightrope when it comes to empowering their workers and making them data literate. You can’t just throw raw data at someone untrained in analytics and expect them to mine crucial insights; but you also can’t just give them a dashboard and expect them to understand what’s going on. Easy-to-use tools for self-serve data analytics, combined with strategic help from data scientists, can go a long way toward helping an organization succeed on the data front.
Third, effective data analysis is also a result of a company’s culture. It’s not just about hiring the right data experts and signing up for the right tools; companies need to really think about practices and culture around data, and how to make sure everyone in the organization is best served by the processes in place. It means that data experts and their company need to examine usage metrics and pause to analyze results. A good feedback loop will ensure everyone is getting what they need.
