Every enterprise’s primary objective is to fight for every chance to grasp their audiences’ attention. Not quite so long ago, organizations employed regular marketing campaigns to enhance their brand image as well as performance. However, such methods have turned outdated with time and therefore fail to make impact. Currently where business landscape is transforming every hour, beating competition and earning good returns have become complicated than ever. Companies need to market directly to the source, especially if they cater to the millennial generation.
In order to garner data, businesses must learn how to target their audiences. And to do that, they need to decipher myriad aspects of their audiences – what they’re interested in, what they require, 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 discover a group. Discovering how business ecosystems’ target audience doesn’t have to be complex. And data mapping has a crucial role to play here.
Essentially, data mapping is a process wherein, various bits of data are arranged or organized into a manageable as well as easy-to-comprehend system. Data mapping tools are leveraged to match data fields with the particular target fields. They are meant to bridge the differences between two systems, or data models, so that when data is transported from a source, it is extremely accurate and usable at the destination.
Data mapping is highly important for ensuring success of multiple data processes. One error in data mapping can ripple throughout various organizations, leading to replicated errors, and ultimately erroneous analysis.
Organizations, therefore, need to use data mapping tools to harness the value of data better. By using these tools, they can use different types of data, in various formats without difficulty. Now for instance, a companies’ database can have a phone number which could again reside in different ways as one can ever imagine. Using a data mapping tool can help all business users recognize phone numbers in actuality. Additionally, it can enable them put all in the same field instead of having them drift around by other names. With this, business users can take organized data and extract value from it in a faster manner. This enables them know their customers better, learn the commonalities they share, and find out some discrepancies that are needed to be handled if any.
With the aid of all this information, business can make smarter decisions and alleviate overhead costs while providing high customer value. However, conventional tools for data mapping are not precise and they involve a lot of manual effort to begin with. They take a lot of time to map data, which directly impacts speed of processes and ultimately makes organizations difficult to do business with. Artificial intelligence/machine learning mechanism can be of tremendous value here.
In the earlier example of phone numbers, merging and data cleaning were discussed. These data-driven processes are often powered by machine learning mechanisms. They empower every business user (even the non-technical ones) to make predictions instead of performing a single task, turning it much better.
As far as the example is concerned, machine learning-powered data mapping solutions can be used to identify a phone number and assign it to its proper category for future purposes.
Machine learning enables organization to go beyond the task of just recognizing phone numbers. It can identify errors such as missing values and group information from the same source.
This is what merging and data cleaning encompasses – to cleanse the data without excessive manual intervention and present the cleansed data in a better way. Not only this process saves a lot of time but also makes the process easy and accurate.
Simply put, transformative machine learning-powered tools enable business users create intelligent data mappings within minutes. In the absence of this technology, the process will become more error-prone and slow. Organizations will not need their IT teams to map data, and this can free them to focus on more strategically demanding tasks. So, artificial intelligence/machine learning can help organizations embark on an innovation journey.