That there is a major problem with quality of marketing databases is now a cliché. Marketing bloggers have quoted ad nauseam the Sirius Decision study claiming that “25 % of the average B2B marketer’s database is inaccurate”. It is not hard to conclude that poor quality data are a huge drain on customer acquisition and retention campaigns in terms of dollars and missed opportunities. What gets glossed over though, and swept under the rug, is the inadequacy of the remedies to this data quality problem. The current solutions to fixing B2B marketing data quality are incomplete and for the most part ineffective. Here is why and what you can do about it.

To truly understand the quality issue, it is necessary to first categorize the defects commonly observed in B2B marketing databases. First, there are errors due to inconsistent use of acronyms in certain data fields (e.g. employer - IBM v/s International Business Machine). Second, there are defects due to incorrect contact information – email, phone number, or postal address. This can be due to data entry errors or because of changes to contact information. Third, certain critical data fields (e.g. email address or employer name) may be empty and not populated. This is called a data field coverage error or gap. Fourth, during data capture (e.g website form) data fields may have been entered in the wrong fields (e.g. Employer and Department are transposed). Finally, there maybe duplicate records for the same person, due to minor variations in the name, or employer or title or other such fields.

Almost all discussion about B2B data quality centers on the above defect categories. Quality improvement solutions focus on correcting these type of errors. Enterprise data hygiene service providers such as Experian, Dun and Bradstreet, Informatica offer complex, expensive solutions suitable for large Fortune 1000 companies. Smaller startups such as Trillium, Talend, Tamr offer data quality improvement tools as part of their data integration or predictive analytics solutions.The approach used by these service providers is roughly the same and use automation and manual processes.

The “good news” is that for the most part these tools work and fix the five main categories of the defects discussed above. The “bad news” is that they overlook one critical cause of quality problem. The single biggest cause of decay of B2B marketing data quality is the fact that business professionals are constantly on the move, from one job to another.

According to a 2015 U.S. Bureau of Labor Statistics Survey, people born during the latter years of baby-boom generation, held an average of 11.7 jobs from age 18 to 48. Employment continuity, in new jobs taken, as one grows older, is alarmingly low. Of the total new jobs started by 40 to 48 year olds, 32 percent ended in less than a year. A lot of people, even older workers, are constantly on the move, from job to job. This poses significant challenges for B2B marketing.

Every day the information in your B2B database is becoming obsolete. Some business executives may have moved to another company. Others may have assumed a new role within the same company. A merger with another company may have caused a move to another location. A few unfortunate ones may have been fired and are now looking for a job. Still others may have simply called it a day and retired. This is the reality of the business world today - the never ending constant flux and changes in employment status.

The result is continuous, steady, gradual data quality decay. Even if you start with pristine quality B2B data overtime it will become out-of-date. The current techniques to fixing data quality do little to arrest this steady obsolescence.

How do you stop this seemingly never ending cycle? Social Media evolution provides the way out. A few service providers have developed innovative solutions to use data from Social Media to automatically pin-point quality issues resulting from change in employment status. Once you know which records are out-of-date, it is possible to overlay traditional data compilation processes to then fix then quality.