For your data quality to be accurate, you will need first to have parameters that will determine that you are really gathering quality data. Here are some of the most important parameters for data quality:

Completeness – a percentage of data that includes one or more values. It’s important that critical data (such as customer names, phone numbers, email addresses, etc.) be completed first since completeness doesn’t impact non-critical data that much.

Uniqueness – When measured against other data sets, there is only one entry of its kind.

Timeliness – How much of an impact does date and time have on the data? This could be previous sales, product launches or any information that is relied on over a period of time to be accurate.

Validity – Does the data conform to the respective standards set for it?

Accuracy – How well does the data reflect the real-world person or thing that is identified by it?

Consistency – How well does the data align with a preconceived pattern? Birth dates share a common consistency issue, since in the U.S., the standard is MM/DD/YYYY, whereas in Europe and other areas, the usage of DD/MM/YYYY is standard.

What is Data Quality and How Do You Measure It for Best Results?

Marketing research firm Ascend2 performed a survey of 250 mostly senior people in marketing, to see what they thought about data quality. Over 80% of the respondents were in management roles, with more than a quarter of the sample holding C-level positions. Fully half were associated with large companies with over 500 employees, and more that 85% had over 50 employees. Respondents were also equally split between short versus complex sales cycles.

The results showed very clearly that data quality has risen to become a critical consideration in marketing success nowadays. Here are some of their key findings:

Improving data quality is their most important strategic objective. With 62% of respondents rating this as their top objective, data quality now ranks far above more traditional marketing objectives such as improving marketing data analytics (45%), improving user experience (43%), optimizing the lead funnel (26%), and even acquiring an adequate budget (20%).

Data quality is also their biggest challenge. Respondents also ranked data quality as currently being their most critical challenge, in smaller numbers (46%) but in similar proportions to the other factors such as those mentioned above.

But things are getting better. Fully 83% of respondents feel that their marketing data strategy is at least somewhat successful at achieving objectives, with over one-third (34%) rating their own efforts as “very successful (best-in-class).” Similar numbers also feel that their tactical effectiveness is improving as well. While 14% feel that they have been unsuccessful in achieving objectives to some degree, only 3% consider themselves to be very unsuccessful.

Data quality is a downstream process. Respondents clearly favored cleaning up contact data versus constraining how it is collected. Nearly half (49%) felt that validating contact data was the most important tactic for improving marketing data quality, while less than a quarter (24%) felt that standardizing lead capture forms were important. Other upstream measures such standardizing the data upload process (34%) and developing segmentation criteria (33%) were also in the minority.

Call in the experts. An overwhelming majority of respondents (82%) outsource either some or all of the resources they use to improve marketing data quality, with over a quarter (26%) using no in-house resources at all.

Special thanks to Carolyn Healey and ServiceObjects