Good vs. Bad Data: What Does That Even Mean in the B2B World?

October 8, 2019 Jason Hubbard

This week we have a guest author taking over to talk all about Data, something on everyone's mind as everyone is gearing up for the big end-of-year push! Jason Hubbard from SalesIntel shared some thoughts about data in todays B2B landscape here:

Data is essential for business progress and subsequent evolution. That's a settled debate. In fact, the prominence of data in the business world has risen to a point that new terminologies like data-driven decision making or data-driven marketing are now casually thrown around. And while business leaders and executives across the spectrum are rushing to leverage data for their next business venture, few realize that data, for all practical purposes, is a two-faced sword. As important and resourceful reliable data is, it can be equally damaging and wasteful when data quality is compromised.

Confused? Let's take a look:

Good Data

In academic terms, good data is supposed to have three core properties:

Consistency

Data consistency means data written to the database must conform to all defined rules. For example, phone numbers should be 10-digit long, emails address should have a specific format, etc.   important because well, a 9-digit contact number isn’t going to help anyone. 

Completeness

Data Completeness requires that all the data is present in any given dataset. that is, there aren't any required field missing. For example, if you have a contact number, job designation, but the name is missing, it's of little use.

Correctness

This is probably the toughest criteria to satisfy. As the name suggests, data correctness means all the data in a database should be correct. Since algorithms can only verify the syntax, data providers often resort to manual-verification of data to ensure data correctness.
 
Now if you closely follow these three properties, you would realize that every bit of consistency or completeness you achieve is only transient. Any change in data can have a cascade impact on the entire dataset. Suppose you maintain a database of contact numbers. What if they change their number? Or maybe they have two different numbers for personal and official purposes- both of which have different entries in your database. The data is complete but is it consistent?  This is why constant care has to be taken to ensure reliability and thus achieving high-quality data is regarded more as a process than a one-off goal.  

Now as you might have guessed, data that fails to meet one or more of these benchmarks is referred to as bad data.

Sources of bad data

There are basically two sources of bad data- either you have poor data from the start or the quality of your data has deteriorated over time. As this article from SalesIntel explains, the first kind may be attributed to errors in manual data entry or purchasing unverified lists while data deterioration may be due to natural data decay or poor CRM maintenance. In any case, bad data isn't good for your business health.

Impact of Good and Bad Data

Since most of the business operations these days are data-driven, bad data can wreck even the best business ideas and strategies while good data yields good results. Two of the core use cases include:

Prospecting clients

Prospecting clients with bad data is akin to finding a needle in a haystack. Most of the time you would be dialing wrong numbers, have bounced emails, and your marketing team would be left scrambling for correct contact information. With good data, however, sales and marketing teams can precisely reach out to potential clients and close deals quickly.

Resource allocation

In the absence of high-quality reliable data, businesses risk allocating resources to unproductive operations. Worse still, bad data can skew their entire marketing campaign to reflect unfavorable results. With good data, they get a clear assessment of their sales and marketing efforts and thus make more informed decisions for resource allocation.

It's not that businesses aren't aware of these facts. Talk to any executive and they concur the value of quality B2B data for sales and marketing operations. The reason many of them are still stuck with traditional contact lists is that they aren't sure where to find high-quality verified contacts. Then there are also concerns related to costs. Well, there is a simple solution to both- request a free demo from data providers. Once you put such data in action, you can easily asses the improvements in productivity and conversions- a clear benchmark to decide whether buying B2B data is worth it or not.

 

-- Jason Hubbard, VP of Growth, SalesIntel

About SalesIntel -- We started our mission in 2018 to make sales and marketing professionals’ lives easier. We realize that sales and marketing people shoulder the most important responsibility for the existence and growth of their organizations, namely, revenue growth. Our goal is to make their lives easier by providing them with the highest quality data on the market.

 

 

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