Data Quality vs. Data Volume: The Balancing Act of Consumer Behavior Data

In the fast-paced world of data-driven decision-making, businesses are increasingly relying on consumer behavior data to gain insights into their customers. Geolocation intelligence, in particular, has emerged as a powerful tool, enabling companies to analyze consumer behavior based on geographical data. However, the journey from raw data to actionable insights is not without its challenges. As there are billions of people on earth generating trillions of observed data points, geolocation data is massive. Because of its sheer size, the diversity of its sources, and its temporality, this type of data is subject to a great deal of noise and inaccuracy. So, how do businesses maintain high standards in filtering out inaccurate or less useful data while ensuring they have enough data to model the world they are evaluating?

The Complex Landscape of Data Quality

At the heart of this challenge lies the need to ensure data quality. Bad data costs U.S. businesses $3 trillion. Not only that, but researchers predict that up to 40% of business objectives fail due to inaccurate data leading to more lost revenue. In fact, according to Gartner, the average financial impact of poor data quality on organizations is $9.7 – $15 million per year, and this only includes costs that can be measured like time spent on data quality, resource re-allocation etc. They don’t include the unknown opportunity costs that are hard or virtually impossible to measure.

Clean, accurate data forms the bedrock of any meaningful analysis. Data providers need to invest significant resources in filtering out anomalies and discrepancies within their datasets. Anomalies, or outliers, can skew results, leading to flawed insights and misguided decisions. The consequences of poorly informed decisions make it imperative to constantly monitor for and remove these anomalies to maintain the integrity of the data.

The Impact of Aggressive Anomaly Removal

However, if we are too aggressive in removing potentially anomalous data points, we may skew too far and inadvertently jettison some non-anomalous data points as well. The decision of how to filter out anomalies directly affects the quantity of data available for analysis. A reduction in data volume can potentially limit the depth and scope of insights derived from the dataset, especially for analysis of smaller locations over a shorter time frame.

Finding the Middle Ground: Thoughtful Filtering

A balanced approach becomes essential in navigating the interplay between data quality and volume. Instead of adopting a blanket strategy of aggressive anomaly removal, Azira uses a nuanced approach, relying on advanced detection and removal methodologies. This careful cleansing involves analyzing the distribution of observations with image recognition techniques and statistical analyses and removing groupings of points that are statistically unlikely.

The Azira Platform harnesses the power of self-learning artificial intelligence, enabling it to analyze massive amounts of data from sources such as location and demographic data, to provide highly accurate and actionable consumer insights, which are continuously refined as consumer data changes. By employing sophisticated algorithms and NLP models, Azira can uncover hidden patterns and trends, allowing businesses to make well-informed decisions based on the ever-evolving consumer landscape.

By taking this balanced approach with cutting-edge technologies, we can ensure meaningful data volume without compromising on data quality. This method attempts to reduce the amount of discarded data, acknowledging that not all outliers are, in fact, anomalies. In fact, some outliers might hold valuable insights into niche consumer behavior patterns or emerging trends.

The Path Forward: Maximizing the Value of Consumer Behavior Data

In the dynamic landscape of data analytics, it is critical to stay vigilant about data quality while constantly supporting data volume. To accomplish this, Azira regularly evaluates the impact of anomaly removal strategies on both data quality and volume while continually monitoring existing and finding new data sources to ensure a robust data pipeline.

As businesses look to maximize the value of consumer behavior data, here are some best practices: