Marketers Have Too Much Data And Too Little Knowledge

“Data” is a commodity. It is everywhere, and anyone with access to a computer that’s connected to the Internet can acquire all sorts of data. However, what marketers need to make decisions is “knowledge.”

Webster defines data as: “factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.” Webster defines knowledge as “the fact or condition of knowing something with familiarity gained through experience or association; OR acquaintance with or understanding of a science, art, or technique.”

You might think that “information overload” is a new subject. It is not. Wikipedia traces it back to the 3^rd and 4^th centuries B.C.  Seneca the Elder commented “the abundance of books is distraction.” Johannes Gutenberg, inventor of the printing press, was a major contributor to data overload.

Sky Cassidy, CEO of Mountain Top Data, a two-decades old Los Angeles-based business-to-business marketing intelligence company that provides marketing lists, data cleaning and data maintenance services, offered six categories of data in a recent interview:

• Numbers (Quantitative Data): “This is a super general category,” says Cassidy, “and I’m starting with it to drive any data scientists still reading this totally crazy, but it’s referenced to a lot.” When it comes to all kinds of reports, most data of any type is converted into numbers. Numbers are great for analysis because they can be played with to create more data, and KPIs of every type and sort can be made from those TPS reports.
• Non-Numerical Data (Qualitative Data): “If it can’t be represented by numbers, you can bet it’s qualitative data,” says Cassidy. The number of website visits or leads would be quantitative, but the URLs people visited, the timestamp, and other information that is more than just a count is qualitative data.
• Big Data: This is exceptionally large sets of data, typically unstructured masses of data, such as buying habits collected by stores that track what is bought, when, for how much, the type/category of product, and possibly the person who bought it. The data collected over time on a single shopper through their use of a rewards card or something similar would not be considered big data, but that same data on every shopper in the U.S. would be big data.
• Dark Data: This has a huge overlap with machine data. Dark data refers to information that is created but never looked at or used. An example of dark data is the billions of emails that are stored on servers never to be seen, or things that were created automatically like program logs and surveillance videos. Basically, this is the data equivalent of everything you put in that storage shed because you might need it, but you never end up looking at again.
• Analytics: This is a tricky category because analytics is not data; it is a process for analyzing raw data in order to make conclusions about that information. It is things like looking at your website traffic information to get an idea of what products and services people are most interested in, to gauge when a campaign is effective in driving traffic, what ads are driving the most traffic, and so on and so forth.
• Database: This is typically data used in direct sales and marketing. Also referred to as list, marketing list, sales list, direct marketing data, campaign list, or target list. This is the database of prospects and/or clients that includes things like, company name, address, phone number, contact name, title, email, website, company size.

Sara Spivey, CMO of Bazaarvoice, quoted in a May 18 FORBES article entitled “Why Too Much Data Is A Problem And How To Prevent It” (by Kimberly Whittier), said:

“Part of the problem is that, historically, companies had limited data, so they would look at their information and start mining it. This created a bad practice, as the amount of data proliferated at a rapid scale. Now, the marketer is sitting on top of a mound of data and trying to sift through it to mine for “ahas,” which becomes untenable. The better approach is to start with the business objectives and the key questions you want to answer – and then go seek out the right data. Unfortunately, the process at many companies is the reverse of what it should be.”