Since the efficacy of all strategic business decisions hinges on the quality of the data they are based on, having access to clean, reliable data is paramount. No one would contest this assertion.
And yet, biased and noisy data continue to plague organizations. A global survey of more than 1,000 executives revealed that 79% face problems with data quality in their businesses.
In this article, I examine the difference between biased data and noisy data and provide examples of how each type warps business decisions.
Noisy data refers to data that contains errors, irrelevant information, or random variations that do not provide meaningful insights. This can include outliers, missing values, duplicate records, and incorrect entries.
Data noise can be caused by data entry errors, inconsistent data collection methods, measurement or sensor errors, or integration of data from disparate repositories that use varying standards.
Biased data refers to data that systematically misrepresents the population or phenomenon being studied. Bias can occur due to prejudices in data collection, processing, or interpretation, leading to skewed results.
Data biases can be caused by sampling bias, where certain groups are under- or overrepresented; measurement bias due to flawed data collection tools or techniques; confirmation bias where data is interpreted in a way that confirms pre-existing beliefs; or algorithmic biases introduced by biased training data in machine learning models.
Now let’s consider how accurate, noisy, and biased data could affect the decisions of a global retailer launching a new marketing campaign for a line of eco-friendly products.
This allows the company to target customers who have shown interest in eco-friendly products or have purchased similar items in the past. It can tailor the marketing messages to resonate with the specific values and interests of these customers, such as highlighting sustainability and environmental impact.
Accurate data also allows the retailer to select the most effective marketing channels (e.g., social media, email, in-store promotions) based on where the target audience is most active and engaged.
The messaging will also reflect the preferences and values of a limited demographic, potentially alienating other customers. Moreover, the company might choose marketing channels that predominantly reach the high-income urban demographic, missing out on opportunities to connect with a broader audience.
If the retailer’s customer data includes irrelevant or extraneous information, such as outdated contact details, random clicks on ads, or inaccurate purchase records due to data entry errors, then it will be “noisy” and result in misdirected efforts.
For example, the marketing campaign might be directed towards customers who are not interested in eco-friendly products, as the noise in the data makes it difficult to identify genuine interest. The messaging might be inconsistent or irrelevant, as the noise in the data obscures the true preferences and behaviors of the customers.
Likewise, the selection of marketing channels might be haphazard and even costly, based on unreliable data about where customers are most active and engaged.
| Accurate data | Biased data | Noisy data | |
| Targeting | Effective | Skewed | Misdirected |
| Messaging | Relevant | Alienating | Irrelevant |
| Channel selection | Efficient | Inefficient | Haphazard |
| ROI | Excellent | Suboptimal | Poor |
Accurate data leads to highly effective and targeted marketing campaigns, resulting in better engagement and sales.
Biased data results in a campaign that is skewed towards a particular demographic, potentially ignoring significant customer segments and missing out on broader opportunities.
Noisy data causes misdirected marketing efforts, inefficiencies, and lower campaign effectiveness due to the presence of irrelevant or incorrect information.
iTD’s BI, Data & Analytics data scientists apply the latest developments in data observation, governance and management to deliver actionable business intelligence that powers accurate and timely decisions. Contact me at itbi@italentdigital.com and let my team help you with your most pressing data challenges!
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