The Two Times Data Quality is Important

In the information age, one of the biggest concerns faced by companies large and small is the issue of data quality. It’s no secret that bad data can have far reaching ramifications be they minor inconveniences or costly disruption. With that in mind, many companies have spent much time and money attempting to develop fool proof methods to ensure the quality of data or to easily track down the cause of bad data.

The most important question these companies should be asking is, when is contact data quality (including phone numbers) most important? Or, more accurately, what are the two key moments in data’s lifespan when quality is the center of it all? The answer to this is so simple it’s almost shocking really.

In spite of this simplicity though, it’s always best to discuss something as abstract as data with a concrete example for context. In this case, let’s look at data being used by Best Lead Generation company – a rather data-heavy industry to say the least.

The quality of the data used by this lead generation company is set during the time of its creation. This is obvious but so often overlooked. If data is submitted in an outright incorrect or incomplete state, it is worthless. The thing with information is, excess can always be eliminated, but lack of information is a much harder problem to solve. In the case with Best Lead Gen, an invalid or incorrect phone number or email address couldn’t simply be corrected after submission without a lot of backtracking and some added expense. Even then, the data may not be reparable – it may simply have to be removed.

Most companies are aware of creation being “a time” when quality is affected, but will often regard its journey from creation to use as a lengthy set of points where this quality is affected. While this can be true in the case of computer errors causing miscommunication, focus should strictly be on the creation of data, rather than its path afterward.

Too often, companies will rely on strict data entry tools to ensure that data is always of the desired quality – forms may ensure that a phone number contains the correct number of digits, the email containing a @ and so on. While forms with rules like this are definitely helpful and a good idea to have, they cannot account for all human error or validate phone number for connection status.

Likewise, reliance on the IT department to ensure and regulate data quality is no answer to this problem. Quite honestly, this just isn’t their job. IT personnel specialize in facilitating the data and the machines that store, retrieve and transmit it. They simply aren’t equipped or trained for data quality analysis or control.

Following this logic, the other most important time is when the quality of the data is determined. This is when the data is actually used. If a company needs to reach out to a vast number of people, bad numbers or numbers wrongly claiming to be connected, could waste a lot of time, and cost the company many potential customers.

Fortunately, being in a data-oriented business by nature, lead generation companies understand these two key times when data quality is the prime variable. They also understand how to handle the relationship between the two and what to do in the very real instance bad data still gets past them. This is why companies that understand data – companies like our hypothetical Best Lead Generation company – have protocols in place to handle this. They understand that having means to identify bad data is just as important as the delivery of the data itself.

Similarly, it’s hard to respond to these reports if the data creation isn’t consistently performed by a strictly enforced set of protocols. As said previously, web form rules can help to eliminate some of the issues by enforcing rules through the entry forms themselves, but this isn’t enough by itself. When it all comes down to it, though, the quality of the validation procedures used is where the real prevention of bad data resides.

So, now that we understand the two key points of data quality and what to do with this knowledge, let’s take a good look at how wrong this can go if this knowledge goes unapplied.

A cable company launches a telemarketing campaign to reach out to it’s customers with a special offer for a new kind of plan which could legitimately save the customer money while providing them with upgraded service. However, many of the numbers are ringing forever, or simply not connecting. Since the cable company uses actual human beings to make these calls, vast amounts of man hours are being wasted on these uncontactable numbers. Man hours aren’t free. Many of the connected numbers are cell phones which fall under the FCC’s Telephone Consumer Protecting Act. Calling these cell phones could put the cable company at risk for fines or worse yet class action law suites.

Without a way for the cable company to identify disconnected and invalid numbers the cycle continues. Sadly, many companies whose services aren’t directly data-related don’t take things as seriously, and similar scenarios to the above happen far more often than they should. Well, tech-savvy business professionals are paying more and more attention to issues such as these, and awareness of data quality issues and protocols is growing exponentially.

Perhaps in the near future, all businesses will understand the two key points of data quality and take them as seriously as companies like Best Lead Generation whose data is their very livelihood.