Every company’s primary objective is to fight for every chance to captivate their audiences’ attention. Long-time back, businesses performed regular marketing campaigns to improve their brand image and performance. However, these methods have turned outdated and just don’t cut it anymore. In the current age of digital transformation, beating competition isn’t easy. Companies need to market directly to the source, especially if they cater to the digitally-empowered millennial generation.
In order to garner data, businesses must learn how to target their audience. And to that, they must decipher various aspects of their audiences – what they’re interested in, what they need, and when they’re more likely to respond and engage.
Even though data can’t get overly detailed about an individual, it’s beneficial to know a group. Discovering how organizations’ target audience doesn’t have to be complex. Data mapping has an important role to play here.
What is Data Mapping?
Data mapping is a process wherein, different bits of data are organized into a manageable as well as easy-to-comprehend system. Essentially, data mapping tools are employed to match data fields with target fields while in storage.
Truth is, it’s impossible to state that all the data goes by the same organizational standards. For instance, a phone number can reside in as many different ways as one can ever imagine. With the help of a data mapping tool, users can recognize phone numbers in actuality. Further, it can put them all in the same field rather than having them drift around by other names. Incorporating this technique can help business users take organized data and put a bigger picture together. This helps them know more about their target audiences, learn what sorts of things they have in common, and even find out a few discrepancies that are needed to be dealt with.
With the help of all this information, the business can make smarter decisions and reduce overhead costs while delivering maximum value to customers.
However, conventional tools for data mapping are not accurate and they need a lot of manual effort. As a result, the mapping results get compromised. Transformative technology such as artificial intelligence can be of great value here.
How Can Artificial Intelligence Improve Data Mapping?
In the earlier example of evaluating phone numbers, unification and data cleaning were discussed. Such processes are often powered by artificial intelligence-powered tools.
Users can leverage artificial intelligence to “learn” from their existing data maps to predict and suggest data mapping rules automatically. They can employ machine learning algorithms to make predictions instead of performing a single task, making the task much faster and better. This highly intuitive way of creating intelligent data mappings from previously garnered information minimizes the possibility of errors such as duplicities, missing values, etc. to boot. The correctly mapped data can further be transformed and integrated into a unified database (at the speed of business) to drive informed decision-making and ultimately facilitate higher ROI.
In regards to the example stated above, artificial intelligence-powered data mapping tools can be leveraged to recognize a particular phone number and assign it to its proper category for future use and analysis.
Machine learning allows users to go beyond the task of just recognizing phone numbers. This technology can also identify intricate errors such as missing values or typos and group information from the same source.
This is what unification and data cleaning encompasses – to cleanse the data without manual intervention and present the cleansed data in the most precise way. Not only this process saves a lot of time but also makes the process more effective. Simply put, AI data mapping tools not only accurately map different data sources to the target fields, but also maintain data integrity to streamline decision-making and completely change the way organizations do business.
Finally, the data can exhibit in a way an organization needs to see it. For example, geospatial data is one route ML-powered tools can take and create without any input. Actually, geospatial data is a data that results from translating data into a map and plotting out physical locations and routes that the customers use every day.
Ergo, machine learning-based data mapping tools enable business users create intelligent data mappings within minutes. Without this technology, data mapping results will be error-prone and slow as a lot of manual effort will be required.
No matter the user has technical expertise or not, they can leverage these tools to map data with speed and precision. The mapped data can be integrated without difficulty. The entire process does not need the support of IT teams as the ML-powered tools are pretty easy to use. This frees IT teams to focus on more strategically demanding tasks, which, in turn, helps companies embark on a journey of innovation and growth.
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