Data Quality, AI, and the Future
What do you think of when you hear the term “artificial intelligence” (or AI for short)? For many people, it conjures up images of robots, science fiction, and movies like “2001 – A Space Odyssey,” where an evil computer wouldn’t let the hero back on his spaceship to preserve itself.
Real AI is a little less dramatic than that, but still pretty exciting. At its root, it involves using machine learning – often based on large samples of big data – to automate decision-making processes. Some of the more public examples of AI are when computers square off against human chess masters or diagnose complex problems with machinery. And you already use AI every time you ask your phone for directions or a spam filter keeps junk mail from reaching your inbox.
In the area of contact marketing and customer relationship management, some experts are now talking about using AI for applications such as predictive marketing, automated targeting, and personalized content creation. Many of these applications are still in the future, but product introductions aimed at early adopters are already making their way to the market.
Data quality is key in AI
One thing nearly everyone agrees on, however, is that data quality is a potential roadblock for AI. Even a small amount of bad data can easily steer a machine learning algorithm wrong. Imagine, for example, you are trying to do demographic targeting – but given the percentage of contact data that normally goes bad in the course of a year, your AI engine may soon be pitching winter coats to prospects in Miami.
Here are what some leadership voices in the industry are saying about the data quality problem in AI:
- Speaking at a recent Salesforce conference, Leadspace CEO Doug Bewsher described data quality as “AI’s Achilles heel,” going on to note that its effectiveness is crippled if you try using it with static CRM contact data or purchased datasets.
- Information Week columnist Jessica Davis states in an opinion piece that “Data quality is really the foundation of your data and analytics program, whether it’s being used for reports and business intelligence or for more advanced AI and related technologies.”
- A recent Compliance Week article calls data quality “the fuel that makes AI run,” noting that centralized data management will increasingly become a key issue in preventing “silos” of incompatible information.
The ROI of accurate and up-to-date contact data is larger than ever
Naturally, this issue lies right in our wheelhouse. For years, we have been preaching the importance of data quality and data governance for contact data – particularly given the costs of bad data in time, human effort, marketing effectiveness, and customer reputation. But in an era where automation continues to march on, the ROI of good contact data is now growing larger than ever.
We aren’t predicting a world where your marketing efforts will be taken over by a robot – not anytime soon, at least. But AI is a very real trend, one which deserves your attention from here. Some exciting developments are on the horizon in marketing automation, and we are looking forward to what evolves over the next few years.
Find out more about how data quality and contact validation can help your business by visiting the Solutions section of our website.