5 modern challenges in data integration and how CIOs can overcome them

By the time you finish reading this post, an additional 27.3 million terabytes of data will be generated by humans over the web and across devices. That’s just one of the many ways to define the uncontrollable volume of data and the challenge it poses for enterprises if they don’t adhere to advanced integration tech. As well as why data in silos is a threat that demands a separate discussion. This post handpicks various challenges for existing integration solutions.

The growing volume of data is a concern, as 20% of enterprises surveyed by IDG are drawing from 1000 or more sources to feed their analytics systems. Therefore, entities that are still hesitating to take the first step are most likely to be locking horns with the below challenges. Data integration needs an overhaul, which can only be achieved by considering the following gaps. Here’s a quick run-through.

Disparate data sources

Data from different sources comes in multiple formats, such as Excel, JSON, CSV, etc., or databases such as Oracle, MongoDB, MySQL, etc. For example, two data sources may have different data types of the same field or different definitions for the same partner data.

Heterogeneous sources produce data sets of different formats and structures. Now, diverse schemas complicate the scope of data integration and require significant mapping to combine the data sets. 

Data professionals can either manually map the data of one source to another, convert all data sets to one format, or extract and transform it to make the combining compatible with other formats. All of these make it challenging to achieve meaningful and seamless integration. 

Handling streaming data 

Streaming data is continuous and unending, and consists of an uninterrupted sequence of recorded events. Traditional batch processing techniques are designed for static datasets with well-defined beginnings and ends, making it difficult to work on streaming data that flows uninterruptedly. This complicates synchronization, scalability, detecting anomalies, pulling valuable insights, and enhancing decision-making.