AI is here to make your life easier. To make certain manual time-consuming tasks faster. Well, data integrations are such a task. Indeed, it is in a field like data integrations-the connections between different software tools such as marketing automation and CRM-that the power of AI really shines through.
Because of the huge increase in data volumes, traditional forms of data management are becoming less and less adequate. Manually managing everything takes more and more time, time that can be spent in better ways. The scale at which data must be processed today, including integrations, will continue to grow.
Where traditional integrations can still transfer data efficiently today, AI adds intelligence in addition to volume benefits. This means you can not only connect systems faster, but also let them create valuable insights and automate processes with smart time-saving features.
Think of a tool that not only passes data from marketing to sales and vice versa, but meanwhile recognizes patterns, makes predictions and tells you directly where your opportunities lie.
Let’s take a look at how AI can be used to reshape the world of data integrations, and what this means concretely for marketing and sales.
What does AI add to data integrations?
Easing and automating certain processes so you have less manual work to do is one of the factors that makes using an AI so popular. The algorithms that learn quickly and take on tasks to make your work easier and more efficient.
But not only that, because when AI is used properly, you will also be able to take more relevant data and use it again for other insights, with minimal human intervention. This also reduces errors and reduces costs. Of course, here is the caveat that you need to control the process well, when errors occur you need to address this quickly to avoid major problems.
With AI, your data integrations act as a kind of superconnector. Normally, an integration connects different systems to send data from A to B. But with AI on board, that connection works much smarter. For example, AI can help not only transfer customer data, but enrich it with context and insight, allowing marketing and sales to work with better information.
Some of the processes an AI could help with are the following:
Improve data quality: cleaning and structuring data
Low-quality data, such as missing data or duplicate profiles, leads to misunderstandings and faulty analyses. Without AI, data cleanup is an intensive task where mistakes are made quickly. AI makes this process much more efficient by analyzing patterns in data and automatically detecting and correcting errors.
For example, by deploying AI, you can detect and merge duplicate customer profiles, or complete missing data. This creates consistent, accurate data that teams can use to make better decisions.
Data extraction: finding and collecting relevant data
Finding and collecting data is a labor-intensive process without AI. Companies often work with data spread across multiple systems and databases, which in turn have different structures and content. This can make data collection time-consuming, and certain data can easily be overlooked. With AI, you can more easily discover and analyze patterns in data, and use the relevant information to then adapt to the new data source.
Compare it to a library where AI not only finds the books you need, but also knows exactly where they are located. It provides organized access to data, so employees don’t have to search endlessly and can act faster based on complete information.
Data mapping and transformation: translating data between different systems
Data mapping and transformation-the translation of data between different systems-requires a lot of manual effort without AI to align data correctly. Differences in date notation, field names and data formats can cause errors and inconsistent information.
Using AI simplifies this process by automatically matching fields and structures, allowing data to flow smoothly from one system to another. It creates a universal “language” between systems, so to speak, which prevents data from being lost and ensures smooth integration.
In the process, an AI continuously learns from previous data that it transforms, making the process increasingly efficient and better at further stages.
Automating documentation: tracking the ‘lifecycle’ of data
Documenting your integrations is of enormous importance throughout your projects. It is important to be able to track where data comes from, how it is processed and where it goes. Without AI, tracking this “lifecycle” is a complex and time-consuming task. When it is not tracked consistently, companies can sometimes have a hard time figuring out where errors in data come from, especially if documentation is not in order.
AI can help by automatically mapping the steps and transformations of data, providing transparency. This makes it easy to track how decisions come about, and provides a clear understanding of data flows for audits and compliance. In addition, over time you can also have an AI figure out where an error occurred in order to quickly get to the source of a problem, this also allows it to be resolved faster, without having to do a scavenger hunt first.
Intelligent data governance: ensuring compliance with rules and policies automatically
Data governance requires that information remain properly protected and accessible according to regulations. This requires a lot of time and effort without AI, as data must be manually checked and classified to comply with the law. By deploying AI, these controls are automated through automatic classification and security so that sensitive data can only be accessed by the right people. This prevents data from getting into the wrong hands and ensures your business is compliant with laws and regulations without too many manual steps.
Contextual data understanding: adding meaning and connections to data
Raw data without context offers limited value because connections and meanings often remain invisible. With AI, it takes hardly any more time to make connections between different data sets, adding context to data. For example, it can recognize that certain customer behavior indicates a purchase signal, providing valuable information for sales and marketing teams. This not only gives teams access to data, but also insights that help them proactively respond to customer needs.
These insights allow teams to fine-tune their approach so they can come up with a personalized message to people who are actually interested in it. This is how you make your marketing and sales more effective and increase your chances of getting deals.
What are the challenges?
AI is not a magical trick of course. It’s not like you push a big red button and your sales go up. There are also obstacles along the way that you have to consider if you really want to get good results with AI.
Garbage in, garbage out
AI is as smart as the data that goes into it. If you input bad, incomplete or incorrect data, you won’t get golden insights out of it. Just as a chef cannot create a great meal with bad ingredients, an AI model will not make accurate predictions if it is not fed properly. Much of the power of AI lies in data quality.
Privacy and ethics
One sensitive issue is hyperpersonalization. Customers like it when you understand them, but it can get creepy when AI seems to know everything about them. Therefore, it is important to make ethical choices in your integrations and provide customers with appropriate privacy. This is how you create a balance between personalized experiences and trust.
Convenience and lack of human interpretation
An AI program can analyze an enormous amount of data and recognize patterns, but it cannot always understand the nuances the way a human can. For example, the model may offer insights that seem logical but have little value within the business context or are unintentionally misleading.
Without human interpretation, you run the risk of making decisions based on patterns that are not really useful. That’s why it’s important to combine AI results with human expertise to add context and test decisions against the realities of your business operations.
The future: AI takes data integrations to a new level
With AI, we can look forward to deeper, more intelligent integrations than ever before. It is important not to lose sight of the human aspect in this process. Precisely because computers can do more than ever, and process more data that allows us to make better decisions, laziness lurks. The trick is to use this technology to build a more human, personal relationship with your customer.
Using data to send customers personalized offers and service at exactly the right time, without becoming uncomfortable with excessive profiling. The result? A customer experience that feels natural and focused on actually supporting their needs.
The time is right for AI in data integrations
The use of AI and machine learning in data integrations is no longer a nice-to-have; it is an opportunity to work more efficiently and improve business results. Marketing and sales can use AI to collaborate on a deeper level, better understand customers and take action at the right time. Even though it may sound complex, with a phased approach, any team can capitalize on it.
Want to learn more about how AI can enhance your data integrations? Get in touch and discover the possibilities!
