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.

Responsible Decisions

Data analytics misconceptions are running rampant in marketing today. The ability to accurately compile, comprehend, and apply relevant data is a critical skill for today's marketers, however, so an enterprise need to ensure that its entire marketing team understands those processes.

The benefits of using data analytics, including the ability to analyze vast amounts of data quickly, are still valid. For that reason, marketers should continue using big data. But they should also closely evaluate every analysis to determine whether there are valuable insights that make business sense.

Here are three ways marketers can make responsible, well-informed decisions when analyzing data:

1. Ensure that the data is adequately cleaned. Before running any processes or analyses, ensure that the raw data passes all five of the qualifications previously mentioned. Ask the right questions, and remove any incomplete or inconsistent data that will limit its search functionality later in the process. Automate data-collection processes to ensure validity and timeliness. Taking the necessary steps to guarantee that the data is of the highest quality before starting to analyze it will save time—and improve the quality of the resulting analytics.

2. Standardize your data analytics chain. When marketers' data comes from many sources—customers, market research firms, competitors—there's always potential for inconsistencies. Accordingly, marketers need to standardize the relationships between every actor in their data analytics chains: Create regulations by which all sources should abide; automate data input processes to ensure unified integration with data vendors; and, ultimately, be clear and communicate these standards up front to create a process that reduces mistakes as much as possible.

3. Train marketers and business decision-makers how to properly read data. Guaranteeing your marketers have strong decision-making capabilities starts with recruiting and retaining the most qualified individuals. Training those employees so they understand the data collection process, the data's context, and the inherent flaws in the data is paramount when using data for business decisions. Marketers must learn that, although data can provide a good starting point for making a decision, the best results occur when they are able to balance quantitative findings with well-developed business acumen.

* * *

Data analytics is driving the wave of marketing innovation, and it's likely that this trend will only grow in the coming years. But, although advanced analytics offers countless opportunities for marketers to better understand their audiences and improve their customer experience, data is only a piece of the puzzle. Relying solely on analytics tools when making business decisions can lead to misdirected targeting, causing major consequences for an organization's bottom line.

Business decisions should involve a healthy balance of data and the intuition that comes with years of experience with a company. If marketers can strike that balance, they will be able to make better-informed decisions in the long term.

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Three Ways Marketers Can Use Both Data and Common Sense

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image of Brad Ptasienski

Brad Ptasienski is a director in the Advanced Analytics practice at business and technology consulting firm West Monroe Partners. He has more 10+ years of experience delivering strategic data-management and technical solutions.

LinkedIn: Brad Ptasienski