Data quality is important for many reasons. Organizations use data to make decisions, manage programs, select products to develop, and guide improvement. Data is typically considered high quality if it is fit for the intended decision making, uses in operations, and planning. The problem with low data quality is that it leads to bad decisions.
Data Quality
In large systems, there are often many teams involved in marketing, web design and development, analytics, and testing. This often leads to inconsistent data and to disruptions in data collection.
To complicate the matter even more, it’s common for minor disruptions in the data to appear that only affect a small segment of website visitors that can’t easily be seen in aggregate. For example, there might be a JavaScript error that only affects mobile device users with a specific browser.
Usually, an analyst might have to manually check the data regularly to ensure consistency. However, that’s not a good use of an analyst’s time, and they can still miss issues that only affect smaller data segments. This often results in issues being discovered when it’s too late.
There are many types of anomalies that can exist in data. Here we are defining an anomaly as a pattern difference between a data subgroup and the rest of the data.
Data Quality and Fraud
Data quality can eliminate false anomalies within your data so that you can better identify true anomalies – the kind that often means a fraudulent transaction.
However, if your data is of low quality, trying to detect fraud by finding anomalies will probably show false positives. Or worse, you can also find false negatives, which means you have overlooked significant anomalies.
Real-Time Fraud Detection
Keep in mind that real-time results are key when it comes to fraud detection. So, the data-quality tools for your fraud analysis also need to be able to work in real-time.
If data-quality issues lead to false negatives or false positives in your fraud detection processes, you could solve those data quality problems manually in order to find fraudulent transactions. But cleaning up your data manually would take a long time, and by the time your clean data is ready for fraud detection, the fraudsters could be long gone.
One way that bad data could impact an e-commerce business is by reducing the effectiveness of marketing activities because CRM data can easily be entered incorrectly. Contact profiles can also be unchecked for too long.
Behavioral trends among your online customers change quickly. And with anomaly detection correlated with social media, you’re able to gain a clear advantage over your competitors and keep up with market trends.
The Importance of Anomaly Detection
It all comes down to creating real-time insights into e-commerce consumer behavior. When you have these, you can keep your competitive advantages. Don’t leave conversion rate optimization completely to things like copywriting, design, and technical website improvements. Take advantage of your AI-driven anomaly detection and data collection and capitalize on new trends.