How to Solve 6 Biggest Data Integration Challenges

Here, we included six biggest data integration challenges with their solutions. Data integration offers a lot of valuable information for C-level executives. It helps you implement new ideas, take the services you offer to the next level and do more for customers. But most importantly, it helps you improve your bottom-line.

With real-time insights, you can improve your processes. You can also add innovative ideas to your offerings to boost your profits.

In summary, data integration offers much valuable information. It lets your business prove ideas, instead of guessing. However, data integration automation is no cakewalk. It has its fair share of challenges. They are hard to overcome without the right resources. In this article, we will focus on the biggest data integration challenges. These challenges can hurt your bottom-line. 

6 Biggest Data Integration Challenges With Solutions:

Integrating Legacy Systems

Integrating legacy systems with modern ones or with data warehouses is one of the biggest challenges that companies face today. Most organizations spend millions of dollars on building data pipelines. And, which allow them to extract, load, and structure data suited for reporting and analysis.

The main problem with legacy systems is that they lack connectors. Also, few compatible connectors are available for ETL teams. They can use these to extract data from legacy systems. They can also use them to create data maps for these processes.


Data mapping tools like Astera Centerprise offer integration through connectors with over 40 sources. Using this software, ETL teams can easily extract data from legacy systems; such as COBOL, IBM DB 2, Netezza, and other systems.

Unstructured Data

Most businesses create petabytes of data every single day. But the majority of this data is of no use to them because it isn’t properly structured. This means they are not harnessing any essential insights from the data they already have available. Resultantly, they can’t beat the competition.


Today, many unstructured tools are available in the market. They can help companies extract data from PDF, Text files, printed documents, and online streams. Also, they can structure data. They can format it to the organization’s standards.

Duplicate Data

Duplicate data can affect the accuracy of business visualizations. As a result, C-level executives will make the wrong decisions. These can hurt their business in the long-term. Normalizing data or filtering out duplicates is essential for any OLAP process. 


Data integration tools now offer transformations. They help ETL teams easily get value from their data. They do this by normalizing it, creating segments, and filtering out unnecessary data. The result? They receive structured, normalized data that they can use for further processes.

Also Check: How Can Achieve Accelerated Cloud Data Management?

Poor Quality Data

Poor quality data can reduce business efficiency and lead to more losses. Also, this data takes more storage space in data marts and lakes. That’s why it is essential that it is removed from essential data before moving to the data warehouse. And, without the right tools, data integration teams will take weeks to clean poor quality data. However, with the right tools, this process can take some hours, if not minutes.


Today, most data integration tools offer a staging area where poor quality data can be separated from essential data. And, through relevant transformations, data quality rules, and validation standards.

Also Check: Data Fabric – What It Is And Why It Matters?

Lack of Data Governance

Data integration often involves multiple people who don’t always have security clearance. For example, medical records are integrated through IT teams in most medical centers. But these intermediaries has no authority to view these records. This is a clear breach of policy.


HIPAA compliant data integration software can avoid such data breaches. Also, you can set roles and duties to say who can view what details. They can view them during staging and later during OLAP.

Also Check: How To Get The Exact Data Aggregation In Power BI?

Performance & Time Constraints

Time is money!

In business, time is the only way to make more money fast. Today, businesses want performance, and for that purpose, they need high-performance systems that can process data quickly. However, implementing a data integration process is not easy. Manual ETL jobs take a lot of time, and therefore overall organizational performance can suffer. Top-level management is not able to make the right decisions when they don’t have data available.


Data integration automation and the use of codeless data integration tools. Don’t hire ETL experts. They can cost over $80K per year. Instead, get data integration software. It costs no more than $30K. It does the same job but at a much faster rate. Moreover, companies can train all their employees to use this software within days.

Also Read: All About MuleSoft Certified Integration Architect (MCIA)

Solving Challenges Once and For All

Data is growing. The demand for data integration will grow, as well. If you want to stay in business, this is the right time to harness more insights from your data. You must overcome these six core data integration challenges. Otherwise, you won’t get the most from your applications, functions, and processes.

Fortunately, you have the solutions to all these data challenges available. Your organization can easily conquer the most complex data integration challenges with the right culture, mindset, and automated tools.

Also Read: Excel For Tech Enthusiasts: Mastering Data Statistics With AI Integration

Image by Gerd Altmann

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