6 Challenges with Data Integration

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Almost every business has developed the habit of collecting data that is generated from their business, say, transactional, social media, warehouse status, etc. These collected data may range from various formats and structures but the ultimatum of business owners is to integrate these data to get a 360 degree view of their customers and the data.

This unified view allows you to understand your business on a deeper level by deriving analytics and also helps making big business decisions. This is attained by data integration.

Data integration and integrity software market: Global industry analysis, insights and forecast published the following information. As per the report, the global market was valued at US$ 7,920.7 Mn in the year 2018 and is anticipated to reach US$ 20,044.9 Mn by 2026. In addition to this, the global market is expected to register a remarkable CAGR of 12.5% throughout the forecast years.

As much as businesses understand how important data integration is but why aren’t most businesses unable to implement it? Here are the 6 major challenges

The Heterogeneity in Data

Heterogeneous data is a group of wide range of dissimilar types of data.

Enterprises are collecting data in large numbers and this dissimilarity in data formats is due to the emergence of schema-less data management. Yes, NoSQL. This is different from the traditional relational data management platform. As NoSQL format collects data either by hierarchy or by “Key-Value” format, which allows this approach to be less time consuming, less storage consumption and quicker operations. This schema-less approach has created a big uncertainty of data when it comes to management.

Not just the uncertainty but when it comes to data integration, the data generated by organizations are extracted from various departments or various data handling systems. These data management systems might not be handling the data in the same format, they could be different from one another.

Every database collects data in different formats, say, structured, unstructured and semi-structured. Integrating these data is a tedious process without a proper ETL tool in place. This does all the cleansing and loading of data into the warehouses for ingestion process.

The Large Volumes of Data

Integrating data is a time consuming process, especially when the process involves data from various formats structural formats. However, it is not the only obstacle, the volume of data is the factor which plays a major role in the time consumed for integration.

The traditional methods involved analysts reading, cleansing and loading the data into the warehouses all by themselves, this certainly consumed a lot of time. Not just time consuming, they were expensive and prone to error.

However, with the emergence of modern data management platforms, the whole process of extracting, transforming and loading is carried out easily.

Businesses which deal with large volumes of data could be handling the data in different database, integrating these large volumes of data from different database is certainly a time consuming task. Pulling the data every and loading them all at once might not be the answer when it comes to dealing with large data, but incremental loading is. Incremental loading is distributing the data into fragments and loading them at every checkpoint, this checkpoint selection could be made as per your business’s preferences.

Incremental processing tackles any schema change issues with the existing ingested data. Let’s take an example, your ecommerce business gets number of orders per day and the status of each order changes every now and then. These columns need to be updated as new statuses of the product comes in, here the incremental ingestion comes into play.

The old column is pulled which consists the product order confirmation data and new status updates are duplicated into the columns as it comes. This process removes the old column which consists of just the order confirmation details, this is basically to avoid replication of data.

Data Latency

Data latency refers to the delay between the time data is generated, and the time it is available for analysis. This can be a major challenge when integrating data from multiple sources, as there may be delays in the extraction, transformation, and loading (ETL) process. This can result in data being stale or outdated when it is available for analysis. To overcome this challenge, businesses need to ensure that they have a reliable and efficient ETL process and real-time data integration capabilities.

Data Security and Privacy Concerns

One of the biggest challenges with data integration is ensuring the security and privacy of the data. As businesses collect more data, they are also becoming more vulnerable to security breaches and cyber attacks. This is especially true when integrating data from external sources, which may have different security protocols and regulations. Ensuring that sensitive data is protected during the integration process is crucial to maintain the trust of customers and stakeholders.

Data Complexity

Data integration becomes more complex as the number of data sources increases. Data may be stored in different formats, schemas, and languages, requiring different processing and transformation levels. As a result, businesses need to have a robust data integration strategy that can handle the complexity of their data. This may involve using data integration tools that can handle multiple data formats and schemas and implementing data integration best practices such as data mapping and profiling.

The Quality of the Data

Integrations are brought to a business to study how it fares in the market with the use of analytics. This invalid or incompatible data might not be showing but they could be present in the data you’ve garnered. Businesses might not be aware of it but the analytics obtained from those data would mislead your business as analytics are studied to make important decisions.

As addressed previously, replication of data is one major part of invalid/fake data analytics. If one bogus data is mixed up with all valid ones, it will still play a major role in the analytics front throughout every cycle of operations.

Not every database is capable of handling these structures of data, hence all these variety of structured data are brought together into one. Again, this is also a time consuming process but once the integrations are made, the process works seamlessly and the data can be garnered with a proper analytics tool.

The quality of the gathered data is kept intact by having fitting data analytics management person for your business who scrutinizes the data as it comes. This is possible only when the data is present in small volume but what might be the case when it ranges upto millions of data? A specialized ETL tool needs to be in place to bring order to the data and study them real time.

We at Sprinkle, a data pipeline platform built for the cloud, is capable of integrating data from any sources, combine datasets, automate data pipelines and provide actionable search driven insights.

Our seamless real-time ingestion process helps integrate your data and analyse your business.


1. What is data integration?

Data integration is combining data from different sources, such as databases or applications, into a single, unified view to gain insights and make better decisions.

2. What are some challenges of data integration?

Data integration has several challenges, including heterogeneity in data, large volumes of data, and data quality.

3. What is heterogeneity in data?

Heterogeneity in data refers to the differences in data formats and structures across different sources. This can make it difficult to integrate the data.

4. How can heterogeneity in data be addressed?

Heterogeneity in data can be addressed by using an ETL (Extract, Transform, Load) tool that can handle different data formats and structures and transform the data into a standardized format.

5. What is the importance of data quality?

Data quality is important because it ensures that the data used for analytics and decision-making is accurate and reliable. Poor quality data can lead to incorrect insights and poor decision-making.

6. How can the quality of the data be ensured?

The data quality can be ensured by using a proper data analytics management system and having a specialized ETL tool in place to cleanse and transform the data into a standardized format. Additionally, regular data monitoring and auditing can help identify and address any quality issues.

Written by
Soham Dutta


6 Challenges with Data Integration