The key differentiator for today’s businesses is how they manage their valuable customer data and leverage it for both customer and business value. A whopping 66 percent of senior managers say AI is important to remaining competitive over the next five years.

With a data-driven, customer-centric business and marketing strategy, and the right accompanying technology and employee skill sets, brands can increase their operation efficiency, improve decision-making, tailor the customer experience at scale, and increase ROI and profitability.

Much of the concern for the modern business then becomes how good is the data you are using to feed your systems to do things like personalization at scale and predictive analytics to decide next-best action in the customer journey? Is that data clean, accurate, and reliable enough to be used to leverage for business and customer value? Today, just 20% of organizations report their data accuracy is 80% or higher. 

This question gets even more pertinent when talking about advanced AI/ML algorithms that are either stand-alone like ChatGPT, or embedded into more enterprise-grade data management infrastructure platforms like the customer data platform (CDP). AI systems need to be fed with clean, quality data for it to make good recommendations and decisions from. As the old saying goes, garbage in, garbage out.

But, are you aware that AI/ML tools built into some CDPs can be used to detect anomalies in your data and fix errors and inconsistencies, allowing you to leverage AI for data quality management by automating the data cleansing process?

Using a CDP for Validation and Data Cleansing

While senior managers and marketers agree that AI is critical to their future success, only 12 percent of company’s report having a mature AI strategy, and only 9 percent are confident in their AI governance. The concerns for getting AI up and running more enterprise-wide include getting poor results, unintended consequences, privacy violations, security risks, and future regulations.

All of these concerns can be addressed by deployment of an advanced CDP platform to automate the data cleansing process, and to provide the security and data privacy governance controls to be compliant wherever the data may travel.

But data cleansing, and the validation of that data, can be very time-consuming and resource-heavy tasks. This is where AI/ML models come in. AI/ML models are really good at detecting patterns, trends, and correlations within a dataset, especially ones that deviate from the norm. AI/ML can analyze huge volumes of data, and compare it against existing patterns and flag potential issues. AI is smart and will be able to identify relationships between data points that a human may not have picked up. In fact, AI/ML can identify common sources of errors or patterns that may contribute to data inconsistencies. This gives companies the information they need to improve their data collection processes, update data entry guidelines, or identify training requirements for employees.

By using AI-powered tools embedded within a CDP platform, brands can automate the data cleansing and validation process, as AI/ML can be trained on historical data to recognize data quality issues and automatically correct them. AI/ML can do things for data like standardizing formats, filling in missing values, and reconciling inconsistent data.

Automation of data cleansing with AI isn’t just about saving time and manual effort, it’s also about reducing human errors and shortening the time of the data preparation process so it can be used more quickly. When an organization starts to continuously monitor data quality metrics and applying predictive analytics, companies can detect potential issues before they become more severe.

CDP Data Cleansing Functionality

What type of CDP platform your company chooses to go with will affect your ability to do data cleansing, so it’s important to look for certain features that will enable you to automate your data cleansing process. This higher level of data quality you can achieve before that data goes into other platforms will lead towards better results, decision-making, and outcomes.

Some enterprise-grade CDPs offer content affinity engines, which allow you to enrich your customer data based on customer web behaviors. AI-powered CDPs may also come with predictive customer scoring, which helps detect high value and high potential value customers to focus your efforts on. In some CDPs, if you have the skills, you can also run your own SQL queries, allowing you to build prediction models of your own.

AI-driven CDPs may also have a variety of built-in AI/ML models that allow you to build your own predictive models from. This can include pre-built multi-touch attribution models, real-time next-best action recommendation, CLTV predictions, data preparation, and CTR prediction for digital ads.

Conclusion

By this point hopefully every marketer and business leader knows the value of data to their organization. Customer data is the differentiator, and how smart businesses are in managing it, and then leveraging it for both business and customer value, will be the difference between brands that thrive in uncertainty, and ones that flounder. 

Data can be monetized both directly and indirectly, so in challenging economic conditions it is incumbent on brands to ensure their data is clean, accurate, and of quality so it can be used to feed advanced AI and personalization engines.

By using a CDP powered by AI/ML algorithms, brands can automate data cleansing, refine their data quality strategy, and implement preventative measures to ensure their data is clean and accurate. With that nice clean data, AI will be empowered to take your organization to the next level of data-driven automation.