The Secret To AI: High-Quality Data
“In the world of AI, good data is our compass. With it, we can confidently navigate the future, instead of fearing it.”
Every day, someone says that generative AI, data science and artificial intelligence are changing your world, and you should take action. It could be a media headline, a conference speaker, or a popular online post. Generative AI will greatly affect our lives. However, leaders often lack clear guidance and next steps.
Many business leaders find messaging on AI and data science confusing, technical, and lacking clear guidance. It often creates stress for leaders and their organizations because they feel they should be doing something—but what?
Rather than stress about the future of AI, one thing an organization can do today is strategically focus on its data strategy. The fact is that a lack of quality data causes most organizations to struggle to meet business needs. That’s even without AI in the picture.
Good data quality is crucial in AI. It’s the foundation that will make it faster and more affordable to adopt. Without quality data, you’ll find it nearly impossible to effectively execute AI in your organization.
Marketing Illustrated: A Case In Point
Let’s evaluate an email marketing use case as an example. Email marketing is a powerful tool for marketers across both B2C and B2B. Companies regularly use email campaigns to accomplish a range of objectives, such as enhancing brand awareness and boosting sales.
Marketers have optimized everything from email copywriting to audience segmentation. However, most marketing automations are not leveraging dynamic Customer 360 data.
For example, imagine a marketer begins a campaign by claiming their brand is superior. They also offer a limited-time discount on a new product. But they send the email to a list that’s so diverse, it includes customers and prospects that it really shouldn’t.
This includes the following recipients:
- Customer A – A long-time customer who has experienced issues with a product. They have contacted customer support multiple times within the past two months. They are not very happy with the brand right now.
- Customer B – Has been purchasing products for several years. They highly appreciate new product launches and frequently make purchases, sometimes beyond their financial means. The fact is they are behind on their bills and often make many returns, causing a financial loss to the company.
- Customer C – A prospect on the email list who has recently visited the website. If analyzed, behavioral data would show there is a high likelihood of conversion. However, they are on product pages that are different than the one discounted in the campaign.
- Customer D – A prospect with behavioral data that indicates there is a high likelihood they will not purchase from the company.
A lack of true, dynamic Customer 360 data limits the marketer’s options. Their latest email campaign will reach inboxes, but it may not work or could even backfire and have a negative impact. The question is, “Can AI address this issue?”
AI can improve marketing automation when used with a strong Customer 360 data strategy and a reliable governance process. This combination allows for high-quality and timely data.
However, AI is not a magic bullet. It can’t fix data access and quality problems. AI can’t automatically tie together customer support and finance systems.
To prepare for a future with AI, focus on your data now. This will help you adapt to the upcoming world of AI and data analytics.
AI With Quality Data
When a company has good data, using AI becomes practical and affordable, creating new opportunities. AI can analyze the tone in customer support conversations. It can determine if someone who recently had issues is satisfied or dissatisfied with the brand. This could result in predictive machine learning driving simple decisions about whether to market to them.
Using behavioral data, generative AI can create personalized content to improve the likelihood of converting specific individuals or defined personas. AI can predict profitable customers and if customers with valid cards are more likely to convert. Of course, this is all based on Customer 360 data being accurate and accessible to AI models.
Where To Begin
To prepare your data for AI, choose a domain like Customer 360 or Supplier 360 and ask some questions:
- Do you have a defined set of attributes for the domain?
- Do you have a system of record that is your trusted source for all the attributes in that domain?
- If you have multiple methods of record, can you access a Customer 360 view across those systems?
- What is the quality of your data from an accuracy and completeness perspective?
If there are gaps in your responses to these questions, it indicates that there are tasks requiring your attention and action. The starting point is defining the domain, understanding where the data sits, and lastly understanding the quality drivers behind it.
That will be the starting point for your strategy. This will result in a working strategy, which includes making sure your marketing stack can produce the correct data. To improve data, make it accessible, and integrate investments in data quality into regular business operations.
Who Can Provide Help
Business leaders often seek technology vendors to solve their data challenges by purchasing software that can address all their problems. The list of vendors outside their doors is growing daily. Tech leaders agree that they must deal with strategy and governance before adding more technology to the mix.
Listen to vendors to understand their approaches to data and data designed to support AI efforts. Use this knowledge to solve strategy and governance problems first. Then, approach your stack architecture with the assumption that a single vendor cannot solve everything.
Lastly, contemplate the process and culture changes you will need to successfully implement your data strategy. If your organization needs help, hire a digital strategy firm or consultancy that works with any vendor.
About the Author
Derick Schaefer is the Senior Vice President of Technology at Mod Op Strategic Consulting. He focuses on using technology and process to develop customer-focused business strategies. Previously, he was the Chief Technology Officer at Trintech Inc. and held VP roles at Digital Insight and NCR. Derick founded a successful startup named Synthesis and also spent over 10 years working at Microsoft.