Being A Data-Driven Organization, What it Truly Means

In the newest installment of the Data Unlocked Podcast, Jason Davis, Simon Data’s co-founder and CEO, links up with Colin Zima, Chief Analytics Officer and VP of Strategy at Looker. They discuss what it truly means to be data-driven. Through that lens, they also look at how analytics can empower non-technical roles to make data-driven decisions. As a data scientist, Zima has a career’s worth of insights into when to use your data and what it represents. Let’s look at his thoughts on how to be a data-first organization. 

 

 

What Data-Driven Actually Means

The Misconception

 

A 2020 Mckinsey survey found that only 10% of companies can measure their data science efforts against measurable KPIs. On top of that, only 30% of enterprise CEOs think their analytical strategy aligns with their operational capabilities from a data perspective. So what’s the problem? 

 

The challenge here is a misconception of what data-driven means. When people imagine a “data-driven company,” they think of AI and automation based on an influx of data from online behavior. To keep up, you need next-level data intelligence and machine learning. However, that’s a far cry from what’s really going on.

 

Zima explains data-drivenness is not ‘passive intelligence running your business for you.’ Instead, companies that have a more incremental approach to data are the ones he respects the most. Take time to record something that will make a difference for your company or your customers. Next, analyze that data and make an informed decision. Starting small but scaleable is the best approach to being a data-driven company. 

 

“The most important thing is just getting the data you need, then getting it in people’s hands so they can make slightly better decisions.

Colin Zima, Chief Analytics Officer and VP of Strategy at Looker

Easy Solution

An easy way to incorporate an incremental approach to data is by looking at which channels outperform the others. Then, make changes based on simple data that non-technical roles can understand and execute. Those two metrics are critical but often ignored. Your organization doesn’t need a machine to tell you which customers are going to turn. It needs accessible data that is easily understood. “The basics aren’t being focused on enough because people are waiting for these big transformational use cases,” Zima said. 

 

Moreover, Zima stresses the weight that management style has on data-drivenness. For example, suppose an organization has a dictatorial view on decision-making. In that case, very few people see and therefore understand the data used to make the decision. The question becomes: are you making people more data-driven or the organization more data-driven?

 

Why You Need to Data Empower “Non-Technical” Roles 

 

Speaking of abnormal data usage, Zima mentioned plenty of applications outside of the marketing world. Specifically, he’s a big fan of data-enabling customer success and support teams. Many people in customer success don’t think of themselves in a technical role. However, that doesn’t mean they can’t benefit from seeing statistics about customers.  Data-enabling these teams can give them “superpowers” and empower them to do much more. 

 

“I think (data-enabling customer success teams) is almost the perfect example of these areas that have wanted magic.  ‘I want to be able to read my support tickets and analyze and understand them.’ The real problem seems to be about data integration: pulling data from multiple sources, understanding what the customer is trying to do, taking all of this information, and contextualizing it into solving a problem.”

Colin Zima

 

Data enabling your entire organization also ensures that the right people are coming in contact with the data they need to solve problems. Currently, the teams that best understand the problem are too disconnected from the data needed to solve it. The great thing about consulting your employees first is that, while data science can solve complex problems in a vacuum, people can consider variable situations. That way, you understand which issues need data science vs. analytics, when to buy analytics software, and when to hand it over to domain experts.

 

Tools for the Job 

Colin Zima calls Looker the Google of data analytics. Just like Google, Looker is a generalized tool. It is great for analyzing large quantities of data and offering a broad overview. While Looker tries to be both wide and deep, at the end of the day, it’s hard to do both really well. Looker is a broad tool and it works great for analyzing depth of data, but it wasn’t made for specific searches. For a wide view, you want Looker, but for a marketing-specific problem, you want a tool that does that well. 

 

Zima goes on to say a company needs both sets of tools to be successful. The key is to have a broad tool with a specialized tool sitting on top for those more specific use cases. That’s what Simon Data does. It sits on top of existing databases and performs more specific marketing functions. Simon design works with your existing tech stack. The two work together seamlessly to offer top-notch data analytics.

 

📻  Listen to the full Data Unlocked Podcast on Spotify to hear the rest of Colin and Jason’s discussion about data analysis and what being data-driven truly means. Follow the podcast on Linkedin to get the latest news and behind-the-scenes tips from business leaders. 

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