The Future of Data Matching: AI, Machine Learning, and Beyond 

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Why do we often hear the cliche lately in the business landscape that data is the new oil? A detailed analysis was done to understand the efficacy of using data in the best way; the output is that data-driven organizations acquire customers 23 times faster than their competitors who are not using a data strategy.  

Moreover, if organizations are not analyzing the customer’s data, they cannot predict their future moves. For context, have you ever thought about how Amazon could recommend within a month that you need to buy detergent for your home, or you might need a machine scaler to clean your washing machine? Because they have your data at their disposal. That’s why the narrative that data is the new oil is so strong.  

So, if you want to get ahead of the curve and acquire customers where you are most unlikely to find them, you need the right strategy to use their data. How do you crack this enigma code?  

Enter: Data Matching 

What is Data Matching?  

Data matching is the process where all the records of the organization of customers, staff and others are identified, standardized and merged to deliver a possible outcome. Due to this practice, it is possible to improve marketing efficiency, undertake better decisions, and improve the cost dynamics of the organization.  

How Data Matching Works? 

Data matching, also known as record linkage, works by identifying and linking the same records belonging to a single individual from the real world. It is done sequentially in the form of (i) data preparation, (ii) feature extraction, (iii) similarity calculation, (iv) matching strategy, (v) Resolution & Linkage 

What is the future of Data Matching?  

To understand the future of data matching, you have to first analyze the present. For context, at the moment, data is so important that it can prevent a molehill from growing into a mountain. So, if there’s a small problem in your business, data gathering and intelligence can restrict the same from growing into a full-blown crisis, but only when you have the best data models and software to support the same. That’s where data matching ticks all the boxes because it can deliver;  

1. Accuracy of Data 

Right now, it is very hard to get accurate data, especially during the time when your organization is growing at an unprecedented scale. So, if you are processing extensive datasets, in that case, you need the right solution to help solve the same problem which can perform computationally extensive operations. In this regard, data matching can help perform complex calculations.  For example, think of a government organization that is claiming that they have increased the renewable energy production by over 20%.  

However, the claims are not in line with the outputs. To put that simply, there aren’t enough renewable power stations to establish their claim. For that matter, to help analyze everything and provide the right output, the need for data matching will be inevitable. So, not only you will need tons of data but also the process to refine the data to extract meaning from the same.  

Also Read: The Science of Data Deduplication: Best Practices for Enterprises 

2. 360 Degree View of the Data 

It is very hard to get a 360-degree view of the data. For example, the present data mining and analysis models are not that advanced to handle structured (e.g., databases), unstructured (e.g., images, videos), and semi-structured (e.g., JSON files). Due to this problem, healthcare data sets are largely siloed. With the help of data matching software, it is even possible to analyze different formats like EHR systems, lab results, and imaging data, which are not possible now. Hence solving the problem of data variability.  

3. Operational Efficiency  

Data redundancy could unnecessarily increase the storage cost and could prolong the process of going through data sets for meaning. For example, think of a single data set stored in multiple places. Now, multiply the same with innumerable data sets; you get the time delay.  

But how do we overcome this problem? Operational efficiency can only be possible when you have an algorithm integrated that can efficiently process and compare diverse data sets based on their attributes. With the help of data-matching software, it is possible to attain even very complex calculations and improve operational efficiency.  

Conclusion 

MatchX has been helping undertake data matching in a personalized and scalable manner. From advanced data ingestion to intelligent data profiling and automated quality improvement, MatchX can do everything that you want to stay ahead in the competition when you want your data to not just tell stories but even predict the future of your products/services.  

By leveraging automation, advanced matching algorithms, and real-time synchronization, MatchX, allows enterprises to maintain a competitive edge, improve data quality, and make informed decisions that drive success. Contact us or Visit us for a closer look at how VE3’s solutions can drive your organization’s success. Let’s shape the future together.

EVER EVOLVING | GAME CHANGING | DRIVING GROWTH