The Two Times Data Quality is Important

The Two Times Data Quality is Important

There are only two times data quality is important.
There are only two times data quality is important.

#1 – Data Quality is Important at the Point of Creation

In the information age, one of the biggest concerns companies face, 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 disruptions. With that in mind, many companies have spent much time and money attempting to develop foolproof methods to ensure data quality or to track down the cause of bad data easily.

The most important question these companies should ask 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.

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

Most companies know creation is “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, the focus should strictly be on creating 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 contains a @, and so on. While forms with rules like this are helpful and a good idea, they cannot account for all human errors or validate phone numbers for connection status.

Likewise, reliance on the IT department to ensure and regulate data quality does not solve this problem. Quite honestly, this isn’t their job. IT personnel specialize in facilitating the data and the machines that store, retrieve, and transmit it. They aren’t equipped or trained for data quality analysis or control.

#2 – Data Quality is Important at the Time of Use

Following this logic, when is the other most important time of determining the data quality? This is when the data is 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, in a data-oriented business, 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 real instances when bad data still gets past them. This is why companies that understand data – like our hypothetical Best Lead Generation company – have protocols to handle this. They understand that having the means to identify bad data is just as important as delivering the data itself.

Quality Enforcement

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

Ramifications of Not Understanding the Two Points

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

A cable company launches a telemarketing campaign to reach out to its customers with a special offer for a new plan, which could legitimately save the customer money while providing upgraded service. However, many 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 wasted on these uncontactable numbers. Man, hours aren’t free. Many 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 class action lawsuits.

The cycle continues without a way for the cable company to identify disconnected and invalid numbers. 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 when data quality is important and take them as seriously as Best Lead Generation, whose data is their very livelihood.

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