The Challenges of Joining Data from Multiple Sources into Unified Records

As the amount of data that organizations collect grows exponentially, it becomes imperative to be able to leverage all of that data to gain valuable insights. However, with data residing in multiple systems and formats, uniting it into a singular record can be a challenging task that requires a thorough understanding of several technical skills. In this article, we will dive deep into the challenges associated with combining data from various sources into unified records.

Why is it Essential to Join Data from Multiple Sources into Unified Records?

Before delving deep into the challenges, it's important to understand why it's necessary to join data from different sources into unified records. Suppose you've been tasked with analyzing customer behavior to target your marketing campaigns more effectively. To accomplish this, you'll need information on their in-store purchases and online activity. Since the information is stored in different systems, combining it into a singular database can be complicated. If you manage to link both sources, however, you'll gain a more comprehensive view of each customer, enabling you to create targeted campaigns based on their activity and preferences.

Moreover, in the age of big data, having a unified data source can be the key to making informed business decisions. When data is siloed or dispersed across multiple systems, it's difficult to get a bird's eye view of the data or analyze it holistically. In contrast, when data is combined into unified records, it becomes much simpler to spot trends and uncover actionable insights.

The Challenges of Joining Data from Multiple Sources into Unified Records

Combining data from multiple sources into a single, unique record is not without its challenges. Here are some of the most significant obstacles that organizations face when trying to create a centralized, accurate database:

Varying Data Formats

The first challenge that arises when attempting to combine data from multiple sources is that the formats of the data may vary. This presents a major hurdle since different systems store data in different formats. For example, one sales platform may store dates in a format such as DD-MM-YYYY, while another stores them in YYYY-MM-DD. To overcome such inconsistencies, it's essential to ensure data uniformity across all sources being linked.

Missing and Inaccurate Data

Another significant challenge is missing or inaccurate data. As data is hoarded between several systems and teams, it's common for missing data to occur. Moreover, inaccurate data can also lead to data mismatching, and in the case of a customer profile, it would mean someone who hasn't purchased anything in the last three years all of a sudden made a purchase yesterday. If missing or inaccurate data is not addressed during the process of joining data, it can result in incomplete customer profiles or errors in the analysis of data.

Handling Large Volumes of Data

Processing large volumes of data becomes a challenge, especially when it's poorly structured or arriving from multiple sources. The manual integration of tens of thousands of records can be an error-prone and time-consuming task, and one that is highly prone to human errors. To work around this problem of handling large volumes of data, organizations should rely on automated tools capable of processing large datasets accurately.

Ensuring Data Security

Another challenge is ensuring the security of the data being linked. The more sources involved, the more there are chances of data security breaches. This is because, in the process of linking data, some data with sensitive information may be in transit or being shared between different systems. Hence, it's essential to ensure that the data is not breached during the linking process.

Incrementally Joining Data from Multiple Sources

Clearly, the challenges associated with combining data from multiple sources into unified records are significant. As a result, it's often better to incrementally join data from several sources into your entity resolution system. This incremental linking technique will help to reduce data integration complexity, data security, and data loss risks.

To achieve this, we suggest the use of an automated tool that can integrate multiple data sources incrementally. Such tools work by linking records gradually, beginning with fewer records to test the system and adding more records as the process advances. Incrementally linking records, ensures accuracy, as this method employs human validation stages for records that don't receive a high matching confidence score.

In addition to boosting data accuracy and reducing risks, incremental linking increases your ability to organize and track changes to your data. With incremental linking, you don't lose insight into the changes made or get swamped with processing bulk amounts of data all at once.

Integrating Data with an Entity Resolution System

It's also crucial that when you link data, it's done within the context of a broader data management strategy. An entity resolution system is an ideal example of an overarching data management strategy that provides comprehensive, accurate, and real-time views of customer identities. As a result, an entity resolution system removes the complexities associated with combining data harmoniously from multiple sources.

An entity resolution system can eliminate repetitive data and identify unique customers in real-time. It does this by continuously comparing the data from various sources by using probabilistic matching algorithms, similarity-based scoring, heuristics, and machine learning algorithms. In addition to its record linking capabilities, an entity resolution system can also identify and merge duplicates, store data from disparate sources, and improve data quality significantly.


Linking data is not without its challenges, but it's critical to achieving valuable insights using big data. The key to successfully combining data from multiple sources into unified records is the use of automated tools that can handle large volumes of data efficiently. Whether incrementally linking records or integrating data within an entity resolution system, the key to addressing these challenges efficiently is by developing and implementing efficient data management strategies.

By addressing data uniformity issues, data security risks, and complexities in data collection, combining data from multiple sources into unified records will become less complicated. With the availability of automated tools that handle large volumes of data, incrementally linking data, and merging duplicates, organizations can achieve a unified view of their data, resulting in valuable insights to drive better business decisions.

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