Just a few years ago, data analytics was the reserve of only the most tech-savvy marketing departments. Today, however, even the smallest businesses rely on data-driven marketing decisions to keep up with the competition.
As advanced analytics capabilities expand and become simpler to use, the barriers to entry decrease. Businesses of all sizes and marketers with little or no data experience are now able to harness advanced analytics for marketing purposes. Although this development opens the door to countless possibilities, it also increases the potential for error when relying solely on this information for business decision-making.
There's a common misconception in marketing that the more data you have, the more accurate your decisions will be, but that isn't necessarily true. Marketers are increasingly called on to recognize the pitfalls of Big Data, but that recognition poses a difficult question for many marketers: How can I be confident in my decisions if I can't guarantee that my data is 100% accurate?
To avoid becoming the next Google Flu Trends, marketers need to be aware of common misconceptions that cloud data analytics.
Misconception 1: Raw data is never flawed. Numbers never inherently lie, but there are five determinants of data quality: completeness, consistency, accuracy, validity, and timeliness. If the data fails any of those qualifications, a marketer is in danger of skewing his or her interpretations and making misinformed organizational decisions and bad investments.
Misconception 2: Raw data must be sourced from a single place, department, or company. In actuality, that often is not the case. Rather, most raw data comes from many different "links" in a long data analytics "chain." Data travels from customers to market research firms to corporate marketers, leaving plenty of room for error and miscommunication. Frequently, each data source has a different system for "cleaning" the data, and some of those systems may be inferior to others. Such an unregulated data analytics chain often creates consolidation and integration issues further down the chain.
Misconception 3: There is little to no potential for human error in data analytics. Improper data analysis is on the rise, because marketers with limited data analysis knowledge have access to uncomplicated tools like Google Analytics. Someone with little experience analyzing data could be confused about the data's context, and so place higher emphasis on variable or trivial aspects. Or a marketer may depend too heavily on the "infallibility" of data analytics and ignore common sense when making important business decisions.
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