The Right Data Fuels the Artificial Intelligence Race

In 1957, Army mathematician Wiliam D. Mellin argued that because computers can’t think, the quality and accuracy of their output was only as good as the programming and data going in. In other words, “garbage in, garbage out.”

It’s now more than 50 years later, and engineers have made great strides with artificial intelligence, neural networks, machine learning and natural language processing.

Looking back, one thing is certain. Mellin was wrong. Data…good data…is not only critical for simple processing machines, it may, in fact, be even more important when systems can “think.”

The Commoditization of Artificial Intelligence

While self-driving cars, fast-food robots, smart drones and a host of “smart” applications and devices have received all the publicity, there’s been an artificial intelligence (AI) war brewing behind the scenes. For the last few years, the biggest software vendors have been in a scramble to buy, lease and partner with AI technology companies.

Vendors are investing billions of dollars and years of R&D on what they see as the opportunity to change the way users work with their systems. Oracle Adaptive Intelligent Applications…Salesforce Einstein…IBM Watson…Microsoft Azure and Cortana Intelligence…Adobe Sensei are just a few of the trademarked AI solutions that vendors are integrating into their core products.

And 2017 is the year we expect to see AI shed its mantle of “coolness” and become a key trend in marketing and technology. Given the growing demand for better insights and faster decision-making, Forrester expects investments in AI to grow more than 300% over the 2016 numbers.

But while it may be premature to hype the next generation of “smart” tools as the equivalent to channeling your personal data scientist, vendors expect AI to bring greater simplicity into their complex, functionality-rich tool sets and software suites. They hope too that AI algorithms that learn from users’ actions and even anticipate their next steps will lower the technical threshold for entry and open more opportunities in the mid-market.

From Lead Scoring to Predictive Intelligence

While the vendors talk about “cognitive technology,” “intelligent productivity” and “deep learning,” we’ll watch the promise evolve over the next several years. I think, however, the developments in lead scoring help us begin to see the potential and better understand data’s critical role.

For the difference between lead scoring or even predictive lead scoring and predictive intelligence is the difference between ranking and prioritizing one’s existing leads and peering into a crystal ball to find net-new companies and prospects with a high likelihood to purchase…and then knowing where they are in the buyer’s journey.

It’s predictive intelligence that allows sales and marketing teams to target the right people with the right information at the right time.

Third-Party Data with the Empirical Edge

Third-party data augments your own data. It can help fill in and expand incomplete records, provide the volume of data needed for pattern analysis and learning, and acts as a filter or series of filters to target your search.

But while a lot of sales and marketing departments look for things like contact records based on intent to purchase and persona-based data, HG Data’s dataset is different because it analyzes past purchases behavior. Rather than build our data on algorithms that predict intent based on engagement and touch points, we extract, translate, curate and analyze both structured and unstructured documents and use data science techniques, such as entity resolution and disambiguation, to capture the complex relationships between companies—tech buyers and tech vendors – at scale, providing immediately actionable insights for targeting messages and campaigns.

Because of our ability to analyze the relationships between buyers and vendors, our dataset is empirical…derived from documented and verified past purchase behavior and the technology currently in place.

Empirical data makes predictive better. When sales and marketing teams start their analysis with precise data, they focus on a smaller funnel with higher conversion rate.

HG Data continues to connect its data to other public and private data resources.  It is why the largest B2B audience data marketplace in the world hand-selected HG Data’s segments to be included in their offering. And why the HG Partner API is helping companies bring our data into their applications.

Because the quality of our data is so high, and valuable to our customers, we know that it’s going to provide a good additional lift with these new AI platforms and AI offerings in these existing platforms.