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Challenges of Data Integration and ways to overcome them

Mar 30, 2021
Data Integration

Data Integration

As work culture is shifting towards digitalization, data integration has become an integral part of all working enterprises. It is important to ensure that you’re providing high-quality data to your users, especially if you work in the commercial and scientific domains as it helps greatly in customer retention and booming your business. However, many people still struggle with it. Research firm Gartner has found that the average cost of poor data quality on businesses amounts to anywhere between $9.7 million and $14.2 million annually. At the macro level, bad data is estimated to cost the US more than $3 trillion per year. In other words, bad data is bad for business. So, where is it that people are struggling with data integration? 

Challenges with Data Integration:

Let’s take a look at the top 5 challenges of data integration and how you can overcome them.

Challenge 1: Massive Data Volumes

One of the biggest challenges of data integration is the inability to keep up with increasing volumes of data. As the volume increases, multiple issues like lack of space for retention, the requirement of increased effort to organize data, and the inability to efficiently analyze data for useful insights arise. 

This challenge can be solved by the use of Big Data analytics, machine learning, and cloud infrastructures. While you’ll still need to formulate a fine strategy to manage your data, leveraging these technologies will reduce your workload while ensuring efficiency in your system. 

Challenge 2: Multiple Data Sources

Another significant challenge that you may face while integrating data is collecting it from multiple sources. Many times, the sources from where your data is streaming in are more than a few. This leads to increased efforts and time investment in collecting and organizing data from these sources, which could have otherwise been utilized in analyzing the data and working on effective business insights. 

This challenge can be resolved by analyzing your sources to find out which the important ones are and how frequently the data is required from these sources. Then, you can focus first on the ones with essential data and work your way towards the rest. You can also implement a custom-made data integration platform that will remove most of your workload and will help you to efficiently handle your data.

Challenge 3: Failure to Integrate Real-Time Data

Many times, real-time data is needed to provide your customers with an excellent user experience. Especially in the commercial domain, it is required to collect real-time data to provide targeted advertisements and product suggestions to your clients on time. However, the inability to integrate data when you require it causes you to do not meet your customer’s demands and hence reduces the probability of customer retention. 

To resolve this challenge, various automated data integration tools and platforms like Microsoft SSIS, AWS Glue, Five Tran, etc., can be adopted. Using these tools will help you to provide real-time data to your users without losing any of your resources or clients. 

Challenge 4: Outdated and Duplicate Data

Both outdated and duplicated data stem from one root problem – organizational silos. If you have departments that don’t regularly communicate with each other and have their systems to input data, it will inevitably lead to duplicated and outdated data. Even if these issues seem trivial, they can end up costing you a lot of money and customers along with increasing damage to your reputation in the long term. 

This challenge can be effectively resolved by taking the following steps:

  • Creating a central cloud-based platform where all departments can upload data and view the already-updated data simultaneously.
  • Increasing the culture of inter-departmental communication in your workspace.
  • Setting aside dates regularly to update the existing data in the system and remove the duplicate copies, if any.

Challenge 5: Manual Integration of Data

Many data scientists majorly spend their time collecting and setting up the data instead of analyzing it. This manual effort occupies their time which would have otherwise been invested in analyzi ng the data for useful insights.

This challenge can be resolved by adopting automated tools so that manual effort in collecting and prepping data is reduced to a minimum and time saved is invested in working on useful insights.


Data integration has become a necessity these days. However, you must keep in mind that it is a continuous process. You need to keep a regular check on the tools you use and the approaches you adopt to ensure that everything is working well. Whether your company is growing or well-established, I highly recommend that you keep improving your data integration and data management strategies as your business evolves. At every step, new challenges will show up as your data volumes increase. So, it always helps if you take a step back to evaluate the current status of your data integration strategies and then work on them accordingly. By being aware of the challenges you may face with data integration and knowing the ways to overcome them, you can do wonders for your organization. 


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