Learn to identify, manage, and nurture high-quality data with these industry best practices.

In the world of market research, data is king. Data allows businesses to connect with consumers, provide valuable products and services, and rise above the competition. But, alas, not all data is helpful. Inaccurate, incomplete, inconsistent, and otherwise skewed data can muddy the waters, making the path to intelligent, informed business decisions unclear and risky. That’s why market researchers must have a solid grasp of data quality and demand it from their partners.

Data quality measures how well a data (or dataset) fulfills an intended purpose; different purposes will require different levels of quality. Objectives can be as varied as gauging brand awareness, mapping seasonality to trigger sales campaigns, and understanding consumer purchasing behavior across demographics. Does this mean that data quality is subjective? No, it means quality is subjective and objective. On the objective side, there are critical measures of good and bad data.

Let us delve deeper into the concept of data quality and discuss data quality metrics and means of improving data quality.