NexSales – B2B LeadZine

It is always a delight to find your data neatly compiled and easily accessible. Same goes with businesses; they prefer to have their data well organised and relevant. In this post, we have compiled a list of quotes about Data Quality from the insiders. If data quality wasn’t one of your top priorities yet, reading this post will make it.

-          “Our marketing effectiveness leads to our sales effectiveness, which leads to our service effectiveness. Data quality is the key to the success of that. If you don’t have quality data, that whole chain breaks down.”
Hamstrung By Defective Data, Chuck Scoggins, Hilton Hotels

-          “How do companies assess data quality? That’s the problem, many do not. Few have a formal method for tracking data quality; they base their assessment on gut feel or may have looked at it as part of a major IT project. Most, however, do not know if they even have a problem.”
How Good is Your Supply Chain Data Quality?,
Kate Viasek, SupplyChainDigest

-          If more than four-fifths of companies know how important data quality is then the importance of data quality should need no emphasis. Philip Howard, Importance of Data Quality

-          “Following best practices in data quality led directly to a 66 percent increase in revenue.” Serius Decisions, The Impact of Bad Data on Demand Creation

- Regardless of how you get a lead, the skill set to qualify a lead and develop it is exactly the same. Geoffrey  James, Sales Machine

Knowing how important data quality is not enough. It helps if you’re doing something about it. The key is to identify the frame work within which you build your data. According to The Bank of England, there are 6 types of data quality dimensions, which are listed below:

1. Relevance: Relevance is the degree to which statistics meet current and potential users’ needs.
2. Accuracy: Accuracy in the general statistical sense denotes the closeness of computations or estimates to the exact or true values
3. Timeliness and Punctuality: Timeliness reflects the length of time between availability and the event or phenomenon described. Punctuality refers to the time lag between the release date of data and the target date when it should have been delivered
4. Accessibility and Clarity: Accessibility refers to the physical conditions in which users can obtain data. Clarity refers to the data’s information environment including appropriate metadata
5. Comparability: Comparability aims at measuring the impact of differences in applied statistical concepts and measurement tools/procedures when statistics are compared between geographical areas, non-geographical domains, or over time
6. Coherence: Coherence of statistics is their adequacy to be reliably combined in different ways and for various uses

While above were the tips and best practices of Data Quality, few weeks back, we compiled a similar list of Lead Generation Quotes; you might want to take a look at.