Tech Talk with Mountaintop Data CEO Sky Cassidy

"Technical people originally used words like 'data' in their professions for very specific things. But in communications and marketing, it can quickly lose its meaning."

Why are you working to make sure data is used properly?

Technical people originally used words like "data" in their professions for very specific things. But in communications and marketing, it can quickly lose its meaning. I think of data as a pronoun. You should never use it to describe what you're talking about unless you've already been more specific in the conversation. Otherwise people won't know what you're talking about because it's undefined. For people who communicate across job disciplines, this is important. Confusion is the enemy in sales and marketing.

What are the different kinds of data? 

I like to break data into six kinds: quantitative data, qualitative data, big data, dark data, analytics and database. Quantitative is numbers, and qualitative is non-numerical data. They're the two broadest categories. Big data are large masses of typically unstructured data. Dark data is all of the information that is created but never looked at or used again, and database data is used in direct sales and marketing, which includes clients of clients and their relevant information.

Now analytics is tricky because it isn't data. It's the process of analyzing raw data to draw conclusions about information. That's like saying a calculator is numbers, but it just crunches the numbers. 

What is the difference between quantitative and qualitative data? 

Qualitative data is information like email addresses, product categories or the URLs people visited. Quantitative data is best used to compare things. When I'm talking to our sales team, and I ask about sales this quarter, you can't just say "high." I need a number, and I need to be able to compare that to other numbers from past quarters. When you break things down to raw numbers, now you have value. When you collect qualitative with quantitative, then you get the whole picture. 

What are mistakes you see marketers making with their data? 

There are always mistakes people make when they get into statistics because they want the data to say something. Once you make something quantitative, when you break it down to numbers if they don't maintain the information connected to that, it can become useless or even intentionally misleading. You can say you got 200 new leads from a marketing campaign, but you don't know what those leads are or if they'll come to anything. 

Why is using data ethically so important? 

Cherry picking data might help you keep your job, but if you're reporting data to your bosses that just makes you look good, it's not actually helping your company. If you run an email campaign and want to look good, you can say you got a lot of opens. But did you actually get a lot of responses? How can you effectively adjust the campaign then? Let's say in future, if you decide to use the data correctly, the numbers will be much lower, but you will have a higher impact. 


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