How to Use AI-Powered Solutions to Overcome the Challenges of Data Mapping

In the current disruptive times, businesses are facing difficulties in effectively managing and utilizing data. Moreover, the process of data mapping has become an even bigger challenge for businesses to perform and manage. Particularly, organizations find it difficult to handle the intricacies of data, the ever-increasing volume of data, onboard customers faster, create satisfying customer experiences and grow revenue. 

Thanks to AI data mapping technologies, organizations can easily overcome the challenges and deliver the promised value. The advanced methods, driven by artificial intelligence and machine learning offer business users a useful toolset to combat the intricate complexities of data mapping, improving efficiency, increasing accuracy, and the overall effectiveness of data utilization across the business ecosystem. 

Let’s discuss the challenges in detail and learn how AI-data mapping solutions enable organizations to overcome the challenges and drive the business forward. 

What Is Data Mapping and What Are Its Associated Challenges? 

The process of data mapping enables businesses to organize multiple bits of data into a manageable as well as easy-to-understand system. In doing so, it matches different data fields with target fields while in storage. 

Truth is, not every bit of data goes by the same organizational standards. They may refer to a particular phone number in as a plethora of ways as anyone can imagine. The data mapping process identifies phone numbers for what they are and outs them all in the same field instead of having them drift around by other names. With the help of this method, organizations can take the organized data and put a bigger picture together. 

What’s more, the process of data mapping enables organizations to connect a particular data field from one source to another data field in another source. Now, it’s true that data can be analyzed for business insights, it must be homogenized in a way that it becomes easier for decision-makers to actionable steps. Data comes from multiple sources, and every source can define data points in various ways. Additionally, it bridges the gap between two separate systems so that when data is moved from a source, it is hugely accurate and useful at the destination. 

Data mapping processes suffer from plenty of challenges to boot. Here are a few:

1. Large Assortment of Data: Data is often present in various formats and structures, and comes from various sources. Now, that makes organizations difficult to map and collate large amounts of data effectively and efficiently. 

2. Voluminous Data: After the data revolution, the volume of data has increased magnanimously. With traditional solutions in place, companies find it difficult to map and integrate these large volumes of data quickly and efficiently. Also, it requires immense effort and a large number of IT teams. 

3. Quality of Data: When the data is inconsistent and inaccurate, the process of data mapping leads to errors. Consequently, organizations find it difficult to make decisions and deliver the value as promised to customers. 

4. Complexity of Data: Along with the wide variety, data has become complex too. Now, it’s again difficult to handle the complex data relationships and dependencies and hence data mapping suffers. 

5. Slow Onboarding: Legacy solutions hamper the speed of onboarding. IT teams need to create custom coding and build data mappings to map as well as integrate customer data. As a result, organizations take weeks or even months to onboard customers. 

6. Lower Customer Satisfaction: When the customer onboarding process is slower than usual, customers become unhappy and dissatisfied with the service. That’s because they receive the value much later than they’ve been promised. 

7. Data Security and Compliance: It’s challenging for organizations to accurately map disparate data while complying with data privacy and security regulations. 

How AI-Powered Solutions Enable Organizations to Overcome Challenges

AI data mapping has transformed the way organizations map and use data. Let’s find out how.

1. AI-data mapping solutions rely on machine learning algorithms to enable business users to implement data mappings quickly and efficiently. These solutions use the previous data mappings to predict and implement new data mappings, which saves time and effort. 

2. AI-data mapping solutions enable business users to map complex customer data streams while freeing IT to focus on more high-value tasks. Also, the process of onboarding becomes faster as users need to just point and click through easy-to-navigate screens to create onboarding connections. 

3. IT resources no longer need to indulge themselves in creating long custom codes and building extensive data mappings. As a matter of fact, they can use their saved time to focus on more strategic tasks. 

4. Now, when users can onboard customers faster, they quickly identify and meet their changing demands. Ultimately, customers become happy and satisfied with the service. 

5. Finally, AI-powered solutions enable businesses to map and use sensitive data, ensuring compliance and security regulations. 

To conclude, AI-data mapping solutions enable businesses to overcome data mapping challenges, enabling them to deliver value and accelerate growth.