Data Quality Predictions for 2020
Data quality can help us build and maintain customer relationships that fuel real business growth.
- By Geoff Grow
- January 24, 2020
On the heels of one of the most eventful periods in data quality, we've seen increasing regulations, greater strategic recognition of the value of verified business data, and continued growth in our industry. Against that backdrop, here are some of the trends that I see for data quality this year.
Validation as a Service (VaaS): In an era of accelerating consumer data privacy regulations, businesses now face two urgent issues regarding their contact data assets: (1) the need to validate this data to avoid consequences ranging from compliance penalties to brand damage, and (2) the need to access referential datasets that are beyond the capabilities of all but the largest organizations. For example, there are now potentially severe penalties for calling or emailing an incorrect contact, and validating these contacts requires access to massive authoritative databases.
Combining cloud-based data with a subscription-based access approach, businesses can connect to real-time resources such as the U.S. Postal Service or North American Line Information Database phone porting records with their business systems, using third-party services on a per-transaction basis as needed. This concept is gaining traction in other areas as well as marketing automation and CRM platforms become gateways to a greater variety of outside resources.
Location Intelligence: The ability to combine contact addresses with geospatial data such as location type, population density, and high-resolution mapping help us know more about a location than was previously possible. A recent survey showed that 85 percent of business executives view location intelligence as very or extremely important, and in the future I see its use continuing to expand in areas such as shipping optimization and last-mile delivery, intelligent transportation, tax calculations, and utility delivery.
Machine Learning: Tricksters are getting better at what they do, which means it's becoming even more difficult for businesses to decipher and authenticate a real identity from a fake one when approving online transactions. With the help of machine learning, we can analyze billions of authoritative records and derive meaningful insights about valid transactions. With this information, we can better understand the client profile so genuine transactions are easier to identify.
The opposite is also true. With this same information, we can better understand the fraudster profile so bogus transactions are easier to weed out. Now, data scientists can tap the power of machine learning to identify subtle clues in online transactions. Specifically, fraudulent online transactions that were previously difficult to spot with traditional data analysis can now be detected.
Know Your Customer (KYC): In the financial industry, the phrase "know your customer" describes regulations for customer identity verification when accounts are opened to prevent crimes such as money laundering. However, in the data privacy era, it has a broader meaning as well: you need to know who your customer is to ensure compliance with regulations such as the GDPR, CCPA, and TCPA. This means that your address-capture process must be automated, foolproof, and capable of catching common errors and correcting bad data at the point of entry.
Device Reputation: This is a buzz phrase you are hearing more of in the area of fraud prevention. For example, if the order you received has a mailing address in Utah and an IP address in Uzbekistan, you may have reason to flag it or investigate further. Device reputation also plays a role in areas such as compliance (for example, when a contact's device or location is in Europe and may fall under GDPR), bot detection, and geolocation for content licensing.
The One Big Trend
Do you notice a common thread across many of these 2020 trends? Many of them have to do with protecting businesses from compliance penalties in this new era of data privacy. Examples abound, ranging from recent multimillion dollar judgments against well-known firms to the new laws coming online in the future.
There is much more to life – and data quality – than regulatory compliance. We don't open the doors of our businesses every morning just for the sake of regulatory compliance -- which leads me to the one big trend that will drive data quality from here: customer engagement.
Most data privacy regulations, in my view, spring from the tragedy of the commons: a phenomenon where, for example, open land is grazed into oblivion because everyone tries to make sure their cows get there first. Our equivalent to this is interruptive marketing: we pester too many people who don't want to hear from us until consumers and governments fight back with legislation.
I believe that in the future, data quality will increasingly mean maintaining accurate, relevant data for people who truly want to be engaged with our brand, and respecting their preferences – which, in turn, has the potential to build and maintain customer relationships that fuel real growth. My hope is that trends for 2020 and beyond revolve more around data that truly helps us engage our customers and prospects and, in turn, improves the ROI of our contact data assets.
Geoff Grow is the founder and CEO of Service Objects. Originally founded in 2001 to solve problems of inefficiency and waste through mathematical equations, Service Objects has validated and improved more than 3 billion contact records for over 2,500 clients. You can contact the author here.