Data Engineering

Data Noise and The Negative Implications on Business

Published on:
May 15, 2023
5 min. reading time

Data noise can lead to inaccurate forecasting, poor decision-making, wasted resources, and negative customer experiences, all of which can greatly impact a business’s bottom line. Despite the undeniable red flags, many business leaders fail to recognize the importance of ensuring the quality of their data by taking steps to minimize the impact of data noise.

There is a well-known book called “Noise” by Daniel Kahneman, Olivier Sibony and Cass R. Sunstein that explores why people make bad judgments and how controlling both noise and cognitive bias can help you make better ones. It is important that we identify not only the noise in the data but also our biases and other subjective factors that can create “noise” for our decision making.

Many professionals fail to recognize the negative implications of data noise on their business for a number of reasons including a lack of understanding, limited resources, time constraints, a lack of accountability and a laser focus on outcomes over accuracy. Equipping your business to combat data noise requires a growth mindset as well as a commitment to building models that are accurate, reliable, and resistant to real-world data noise.

What is data noise?

Data noise refers to unwanted or random data that is present in a dataset. This noise can come from a variety of sources including measurement errors or inaccuracies, human error in data entry, collection error, natural variations in the data, or deliberate attempts to manipulate the data. Noisy data can lead to incorrect predictions, biased models, and misleading insights, all of which make it difficult to extract insights and draw accurate conclusions.

Despite the many negative implications on business, many professionals continue to overlook the impact of data noise. This may be attributed to a lack of familiarity with statistical concepts such as bias, variance, or overfitting as well as failing to recognize how data noise contributes to poor accuracy and reliability of their models.

How can data noise impact my business?

Wasted resources

Data noise can result in wasted resources by leading businesses to allocate resources to the wrong areas, incur unnecessary costs, make poor or incorrect investment decisions, and misaligned incentives. This can lead to wasted time, money, and effort which can all impact the businesses bottom line.

Poor customer experiences

In order to provide exceptional customer experiences, businesses must make informed decisions that are backed by accurate data. Making decisions on noisy data will lead to misinformation that may result in poor customer experiences that don’t appeal to consumer pain points, wants, or needs. For example, inaccurate data may result in incorrect orders or shipments, leading to a poor customer experience and lost revenue.

Incorrect pricing

There are a number of factors that can contribute to incorrect pricing as a result of data noise including fluctuations in demand, inaccurate cost estimates, incomplete or missing data, biases in data, and overfitting. For example, if your business leverages noisy data to set pricing, you may end up overpricing or underpricing. Overpricing will result in lost sales, and under-pricing can result in lost profits. While basing pricing on accurate and reliable data is imperative, it's important to note that it is equally as important to consider external factors such as market competition, consumer behavior, and regulatory changes that may impact pricing decisions.

Inaccurate forecasting

Data noise can result in accurate forecasting by introducing errors, biases, and variability into the models’ inputs and assumptions. For example, if your sales data contains data noise, it may become incredibly difficult to accurately forecast future sales. This may lead to overestimating or underestimating demand which can lead to overproduction or underproduction, resulting in lost sales or working capital. To mitigate these risks, it is essential to identify and remove outliers, leverage robust modeling techniques, incorporate domain expertise, and monitor and refine models over time.

What can I do to reduce the impact of data noise?

Regular maintenance and monitoring

Regularly monitoring and maintaining your data can greatly reduce the impact of data noise. This means creating processes and systems that allow internal stakeholders to review data on a regular basis so errors can be corrected, and noise can be minimized prior to negatively impacting the business. This process involves setting up quality checkpoints and implementing governance processes to ensure all data is accurate and reliable.

Data cleansing

One of the most effective ways to minimize the impact of data noise is data cleansing. Practicing data cleansing means your data is scrapped and rid of any inaccuracies prior to drawing conclusions, meaning your data is cleaned before using it.  This process involves removing outliers, inputting missing values, and normalizing the data. Practicing data cleansing frequently will reduce the impact of data noise while improving the accuracy and reliability of the data.

Data processing

Similar to data cleansing, data processing is an additional technique that can be leveraged to reduce the impact of data noise within your business. Data processing techniques including feature scaling, dimensionality reduction, and data transformation can all be used to reduce data noise and improve the quality of data.

Robust modeling technique

Robust modeling techniques can help mitigate the effects of data noise by making the models less sensitive to outliers or additional noise sources. By using robust modeling techniques like outlier detection and removal, non-parametric models, regularization, ensemble models, and data augmentation, you can build models that are more accurate and robust to real-world data. These models can be used to reduce the impact of noise while helping improve the accuracy of predictions.

Human expertise

Leveraging human expertise is an excellent way to mitigate the impact of data noise as subject matter experts can be used to identify and remove noisy data and data analysts can leverage their expertise to interpret data and identify potential sources of noise. From identifying sources, to training and validating the model, human experts can help ensure the model is accurate and reliable, helping to reduce the impact of data noise.

Is your business equipped to combat data noise?

In conclusion, businesses can improve the negative impact of data noise by cleaning, maintaining and monitoring data, leveraging robust modeling techniques, and leveraging human expertise. Now that you’ve taken the time to learn about data noise and how it can negatively impact your business it’s important to step back and reflect. Is your business equipped to combat data noise? Do you have concrete systems and processes in place that produce accurate and reliable models that are resistant to real-world data noise?

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