I think most of us can agree that integrity, in general, is a good thing, but when it comes to data quality, integrity means everything. In our eyes, data quality is the highest priority and is the driving force behind everything we do. So in this article, we’re going to walk you through what data integrity actually means, and how it plays a pivotal role in providing our customers with the highest quality IP address intelligence and firmographic data on the market.
Garbage In, Garbage Out – The Real Cost of Bad Data
In the world of data analytics, the expression “Garbage In, Garbage Out” implies that bad input data will only lead to bad output data. In the world of business intelligence, this can have disastrous effects on any sales or marketing strategy that requires accurate company data to succeed.
In the past few years, we’ve seen a dramatic rise in AI and machine learning in programmatic advertising, content personalization, and marketing automation. These technologies can be a great benefit to many companies. However, they all rely on high quality data in order to realize their full potential and even the best of these technologies is relatively ineffective without it. In fact, a recent study by Trifacta found some sobering results about the cost of bad data on organizations:
- 59% of respondents said that bad data could lead to miscalculating demand
- 29% said it would lead to targeting the wrong prospects.
- Only 26% said their data was completely accurate and ready to use right out of the box.
Let that last point sink in for a second – only 26% of respondents said their data was accurate. That means a whopping 74% are working with data that needs to be cleaned, verified, or normalized before it can be used. Now granted, it’s been a while since I was in school, but if memory serves: scoring 26% on anything was not considered a passing grade.
“It’s Hard To Sell Apples If You Only Show People Oranges”
Aside from the time and money costs associated with low quality data, basing your sales and marketing efforts on it may actively cause you to lose out on potential revenue.
To illustrate this, let’s say you’re personalizing a simple welcome banner on your website based on company name. In most cases, if there is no data for the personalization engine to display, it will fall back to a generic “Welcome!” message. However, if the company data in the personalization platform is wrong, it will display the wrong company name instead.
We can even take this one step further. If your company sells products that are industry specific, it would make sense to tailor the content on your website depending on the industry of your website visitors. You could even go as far as changing the navigation menus and CTAs to resonate with your target audience. But if you misidentify the company the visitor is from, you could be showing them all the wrong products and displaying all the wrong content. Suddenly all that great personalization goes right out the window, and your website ends up doing more harm than good, and may even push the visitor toward competitors sites instead.
The KickFire Approach to Data Integrity:
If you’ve read our other article about how TWIN Caching works, then you probably already know that it doesn’t operate alone. It requires a skilled team of Data Integrity Specialists working to constantly update, maintain, and normalize the data. Here are just a few of the many factors that contribute to our data integrity process.
Data Normalization
If we asked 1,000 different people to type United States, most of the time it would come out just fine, but undoubtedly we would see at least a few like this:
- USA
- United STates
- United States
- united states
- UNITED STATES
- United States of America
- US
The list could go on, but the point is that if we simply left it up to each individual person to type it out on their own, we’d be in trouble. The same goes for company data. If we just took all the company data at face value without doing the legwork to make sure it’s clean, accurate, and normalized it would be a mess and nearly useless to our customers. However, our team of Data Integrity Specialists follows strict standards set by ISO (International Standards Organization) to make sure that every company name, address, SIC/NAICS code, phone number, etc. is usable right out of the box. This makes life much easier for Developers that might otherwise spend hours cleaning, organizing, and normalizing the data.
So, when you want company names of your website visitors uploaded into your CRM, for example, you don't have to go through the thousands of entries one by one to see if they are all spelled correctly. Instead, your Dev team can simply copy and paste them knowing that they’re going to be accurate and up-to-date.
Artificial Intelligence vs. Human Intelligence
While we love our machine learning algorithms, there are just some elements of the IP analysis processes that require a human touch. For example, in order to do their jobs properly, machine learning algorithms require vast amounts of data that they consume in order to train themselves on what to look for. This training process must be overseen by a team of data experts whose job is to ensure that the data our algorithms are trained on is good and will result in an algorithm actually learning.
Once an algorithm is operating on its own it will inevitably run into something it hasn’t seen before. When this happens, our data experts step in to analyze and train the algorithm on the new data. This helps the algorithm improve while further ensuring that we only pass along accurate data to our customers.
Real-Time Analysis
The world never stops changing, so we never stop listening. In our research, roughly 7-10% of IP addresses change ownership every month, as companies are bought, sold, acquired, dissolved, and change names every day. All of these events have an impact on the data we provide to our customers. Because we report on more than 26 different company data points, we have to be constantly listening to and reporting on what’s happening in the corporate world 24/7.
Confidence Scoring
This goes back to our view that “no data is bad, but bad data is worse.” We won’t give bad data, we only pass along IP data we are confident about. We have a complex system that produces a confidence score, which is a measure of how confident we are that a certain IP address is in use by a specific company.
In Conclusion
Data integrity is not just a meaningless buzzword for us, it’s the cornerstone on which we base our entire approach to IP address intelligence. By putting an emphasis on the quality of our data, we are able to provide our customers with the most accurate, up-to-date, and most importantly, useful IP intelligence and firmographic data to power our customers’ sales and marketing strategies. If you want to learn more about KickFire’s unique approach to data integrity and IP address intelligence, check out this video!