Coming into the new year, we’ve seen and heard plenty of buzz around AI. But the underlying data is often overlooked; it’s a key foundational piece that impacts AI at scale. Therefore, the proverbial data statement, ‘garbage in, garbage out,’ and the implications of bad data quality, is arguably the most understated AI trend.

“Successful models depend heavily on rich, deep client data. Many B2B marketers put themselves at a disadvantage by trying to make predictive work on a limited set of data or data that is just plain wrong.” - What’s Possible With Predictive Marketing Right Now, July 2016, Forrester

How do you ensure your AI technology utilizes clean data to deliver on its promised value? To help guide you, I’ve shared a few tips about what you need to keep top-of-mind when it comes to data quality and AI.

Insights improve as your data quality improves

The great thing about AI is that your insights improve as you add more high-quality data into your systems. The only way that happens, however, is by embedding strong foundational data, combined with marketing intelligence, into your sales and marketing workflow. This means enriching your system of record and channels of engagement with strong foundational data to drive higher impact insights. 

“The business value of good data ranges from high to stratospheric. While every data source has different characteristics and presents a different set of challenges around quality and accuracy, most B2B marketers still have immature data management practices that fail to get value from these varied data types.” - TechRadarTM: B2B Marketing Technologies, August 2016, Forrester 

By adopting an ‘always on’ mentality with your AI-based system of intelligence, you can ensure that your customer-facing and employee-facing systems are ready to serve up the latest insights to your teams to help drive smarter and faster growth. This means adopting AI technologies that are not add-ons, but rather are embedded into your existing marketing engine. Just as higher-quality data drives better insights, smarter AI creates its own demand for more and better data, creating a virtuous cycle to the benefit of your business.

Don’t limit your customer data

Not all data is created equal. All the data you have in your system of record, as clean or dirty as it may be, is not enough to yield the insights that will guide your decision making. Imagine what you could learn if you were to connect Salesforce to multiple systems: a consortium of like-minded companies that shared anonymized and aggregated data, providing the ability to unlock more insights about your audience. What would you be able to learn from the richer, more complete, and more accurate data? What blanks could you fill in about key business or contact attributes? 

Network Effects, a largely B2C concept that is being adopted by B2B companies, is a great example of the benefit derived from such a consortium. In this case, the value of a product or service increases for all users with each new user that joins and contributes to the platform. While this may seem like a concept from a utopian future where marketers collaborate and share information, we’re already seeing some semblance of it in practice today. Not only are hundreds of enterprises joining a network of record, but innovators like Sam’s Club and First Data leverage networks to co-market by identifying audiences most likely to buy their combined product suite.

Think outside your own box and incorporate external data sources that add to the accuracy and richness of data that feeds into your AI solutions. The true winners from AI will be the ones who ultimately find ways to capitalize data at scale from the networks available to them.

Tactics dictate strategy–focus on both to meet your goals

Data and AI are fundamental for understanding where you should focus your marketing dollars, whether that’s by channel, geography, industry, or even program. But once you cross the t’s, dot the i’s, and maybe even know your ARR target for the new year, how do you actually get there?

It ultimately comes down to tactics. This approach identifies those ongoing decisions and the metrics by which you navigate — the monthly march of close rates, pipe velocity, and coverage ratios — as key factors in consistently focusing on achieving your business goals. After all, you can have a tremendous ability to see opportunity, but whether that opportunity will engage with your marketing efforts in a manner that drives real growth is another story altogether. 

The best-laid plans are only really fulfilled by the best focus you provide on execution, i.e. your tactics. The tactics you focus on help you identify where you are getting traction with your audience, which in turn, will inform adjustments to your strategy. But to derive insights from those outcomes you need to make sure you're gathering quality data about tactical initiatives. After all, you can’t execute on your tactics without certain types of data, such as phone numbers for inside sales reps, addresses and zip codes for territory planning, email addresses for validation, and business intent to understand your target accounts, etc. By factoring in all these types of data and doing so accurately, you’re able to take action, leading to growth in pipeline and revenue.

In fact, at Radius, we’ve found that it’s crucial to address these foundational data challenges both in terms of quality and scale. Our experiences working with larger companies like Zenefits, who are trying to make sense of millions of records across hundreds of systems in real time, has established the need for more comprehensive data stewardship.

"As most companies do, we struggled keeping our CRM and MAS data in order, which impacted our bottom line. With Radius Data Stewardship, we continuously address the gaps and inaccuracies in our data. Our sales and marketing teams are confident they’re working with the most accurate data to drive revenue.” - Viviana Faga, CMO, Zenefits

Paving the way for better AI with data at the helm

Companies are increasingly looking to AI to solve their business challenges, but their success is largely dependent on the foundational data that drives these AI technologies in the first place. Drawing meaningful insights from customer data should be at the forefront for companies looking to grow revenue and intelligently fuel their pipeline on a sustained basis. 

At Radius, we’ve seen this time and time again with lessons learned from companies like Zenefits, First Data, and Sam’s Club. Here’s the question for businesses looking to become true AI winners: does your data meet your needs and draw results from your AI investments? As Scott Brinker from Chiefmartec said, “The specific data you feed these algorithms makes all the difference. The strategic battles with AI will be won by the scale, quality, relevance, and uniqueness of your data. Data quality will become ever more important — as will services and software to support that mission. Markets for accurate and timely 2nd-party and 3rd-party data will thrive, available on-demand via APIs. AI finally puts big data to good use.”

About the Author

Mark Woollen is Chief Product Officer of Radius. He is a CRM executive with marketing, product and sales roles at Salesforce, Oracle, Siebel and venture-backed start-up for products generating up to $2.5 billion revenue.