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.

Armed with real-time insights, you can optimize your processes; and add more innovative ideas to your offerings to boost your profits.

In summary, data integration offers a breadth of valuable information; that lets your business implement new ideas with proof instead of guesswork. However, data integration automation is no cakewalk. It comes with its own fair share of challenges, and they are hard to overcome; if you are not equipped with the right resources. In this article, we are going to focus on the biggest data integration challenges that can impact 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, there are not many compatible connectors available that ETL teams can use to extract data from legacy systems; or create data maps that can make these processes repeatable.


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 that can help companies extract data; from PDF, Text files, printed documents, and online streams. Also, they can structure data and format it according to the standards of the organization.

Duplicate Data

Duplicate data can affect the accuracy of business visualizations. As a result, C-level executives will make the wrong decisions for the business; which 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 to help ETL teams easily extract value from their data by normalizing it; creating segments, and filtering unnecessary data. The result? They get structured, normalized data that can be used for further processes.

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.

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. Moreover, you can set roles and responsibilities to govern who can view and what type of details; they can view during the staging process and later during OLAP.

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. Instead of hiring ETL experts that can take over $80K per year; get data integration software that costs not 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.

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. Unless you overcome these six core data integration challenges; you won’t get the most value 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.

Image Source: Image by Gerd Altmann from Pixabay

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