Data permeates every business. No matter your product, service, or sector, there’s no shortage of data in this digital day and age. Artificial intelligence (AI) offers us the ability to use that data to an exponentially greater degree than ever before.

The initial impulse for many is to implement AI ASAP. But as the volume of data at AI’s disposal continues to increase, the importance of data quality is magnified. Like the air we breathe, data often carries “pollutants” that creep in from our source systems and processes. Implementing AI without improving data quality is like asking a competitive athlete to run a marathon in a smog-choked city: Amid that pollution, they cannot perform well and may not finish the race. The noise in poor-quality data is amplified in AI, undermining the value of insights and outcomes and even risking the legitimacy of the results.

The accuracy and integrity of the data we choose to provide our AI systems is a primary driver of the value AI creates in insights, predictions, and automation. To position your business to make the most of AI, you need to first make sure that the information it will be working from is accurate, clear, and clean – and safe. Read how.

AI, ML, and why data quality matters

AI’s core functions all perform optimally when consistently high-quality, comparable data is used. As the role of use cases rapidly expands into broader “user stories” and scenarios that change and expand daily, the old reliance on a static set of use cases is over – so we need consistent data quality to answer the next challenge, not just the last.

To get the most business value from AI, a good place to start is a firm understanding of the differences and similarities between AI and its subset machine learning (ML). 

• ML centers on recognition and repetitive processes, and its value is in automation. It takes what people do and does it faster. Humans’ place in the equation is in training machines and supervising the learning. The machine then makes unsupervised inferences based on what it learned.

• AI counts ML among its variety of tools and methods. AI goes beyond simply following processes and data analysis, because it seeks correlation and causation, identifying patterns, cognition, and neural networks. Between AI and ML, AI differentiates itself through its predictive ability.

The common ground between AI and machine learning is that they both center on data – clean, carefully evaluated data. Both machine learning and generative AI are probabilistic, but neither is deterministic.

Generative AI distinguishes itself by excelling at synthesis and content summarization, which propels its probabilistic capabilities. But it’s not adept at being predictive. If businesses want predictive, they must elevate the quality and quantity of data used to train more complex models.

AI has advanced over several decades to use complex models as it evaluates data and produces insights and predictions. These are often referred to as machine learning models which must be “trained.” Data actually teaches the machine to look for certain trends, outliers, and statistical relationships, so it must be trained with quality data that does not introduce noise into the model. Similar issues arise with generative AI models where noise in the data creates false or nonsensical insights. (More on this concept later.)

Improved data quality empowers leading outcomes

Business goals vary across and within sectors – as well as from company to company. But leaders would do well to learn from leading practices outside their industry how the data pulled into AI applications can help them meet their goals.

Multi-region grocery stores offer a valuable example of what AI can do with quality controlled, timely data.

Customers sign up to a grocery chain’s reward program. When a customer enters a store’s geodome, their cell phone signal is captured in real time. Their presence and frequency are tracked, indicating a pattern. 

Based on that pattern, the store can send the customer a live offer enroute for something they’ve bought before, to see if this prompts a visit and a purchase.

The data gleaned over time – visits and purchases – can be fed back into the AI model so that offer type, timing, and more are improved, potentially leading to increased purchases. 

Multiply that by thousands of reward program members, and it’s easy to see how just one way of applying AI can lead to increased market share.

How can I prepare my data for AI success?

Back to basics

Businesses should already have high standards in place for any data they work with: accurate, reliable, complete, accessible, auditable, traceable. Those standards do not change when the user is AI instead of human. Maintain best practices around data management and governance: validate sources, check any calculations, and watch for any data that is siloed, outdated, or formatted in a way that’s incompatible with AI applications. Consider whether there are any industry frameworks or standards that might be a useful reference – or that must be followed to remain competitive or compliant.

Improve data with the end in mind

To be effective, AI use cases must be aligned to specific desired outcomes, and data must follow suit in driving toward them. Consider the areas in which your organization might benefit the most from AI, then identify the data that dovetails into those areas. What KPIs (key performance indicators) are necessary to monitoring your goals or evaluating your questions? What data inputs will support the outputs you’re looking for?

Staff for success

As we’ve discussed previously in our “AI Readiness: People” article, the human side of AI can’t be left up to chance. Make sure you have the right people with the right skills to understand the data, build models that use it well, and derive data-driven insights and decisions. Train all staff on the importance of data quality and consistency.

Institute guardrails

Make sure you’re protecting your organization against:

• Threats FROM or INVOLVING data: There are limits to both AI’s process of manipulation and the integrity of the underlying data. For example, chatbots are prone to “hallucination” – confident responses that make no sense, based on random or fabricated information or on training data that is false or misapplied in context. Tools may be trained on data sets with some inherent bias; for example, a search engine may show certain results higher if paid to do so, or a tool pulling from data sets with gender-based, socioeconomic, or racial discrepancies may reflect that same deficiency. The takeaway is to “Trust but verify”: Remember that there is a margin for error and verify information where possible.

• Threats TO data: Because AI systems often process and store sensitive data, they can be enticing targets for cyberattacks. Take care to develop and enforce robust cybersecurity measures to protect your and your clients’ data and AI systems: regular security audits, employee training on cybersecurity best practices, and advanced security protocols. Understand and maintain compliance with data privacy regulations, such as the EU’s General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), to avoid legal complications. Make sure that employees know not to upload sensitive data – such as PHI, PII, or customer data – into the AI large language models (LLMs) that exist in the public domain; consider private and/or enterprise versions of these LLMs, which can exist within the four walls of an organization. And another threat to data in an AI world is the misuse of the data in AI models and outcomes: Poor-quality or poorly controlled AI use can make good data do bad things.

Read more insights from our Cybersecurity team on AI risks and guardrails.

Plan to improve

As with any new business capability, the work isn’t over once a system is in place. Look for opportunities to improve data and how it is used. Consider: How long does it take to get the data answers you need, and why? Is it human error producing data errors? Or cumbersome processes? How can AI remedy these issues?

In conclusion

No matter the industry, AI can help increase market share, expand operations, and execute strategy. Data quality improvement is the necessary work that will make sure your organization is ready to use AI – and make sure that AI is best qualified to help you.