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February 14, 2024

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

Data practitioners come across many challenges throughout the data management lifecycle. Let us learn about the most common day-to-day challenges we face and how to 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. 
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To tackle this, enterprises need systems that enable real-time analysis, aggregation, and transformation of incoming data streams. Enterprises can harness the power of continuous information flow by lessening the gap between traditional architecture and dynamic data streams. 

Unstructured data formatting issues

Increasing data volume gets more challenging because it has large volumes of unstructured data. In Web 2.0, user-generated data across social platforms exploded in the form of audio, video, images, and others. 

Unstructured data is challenging because it lacks a predefined format and doesn’t have a consistent schema or searchable attributes. Like structured data sets that are stored in the database, these don’t have searchable attributes. This makes it complicated to categorize, index, and extract relevant information. 

The unpredictable varying data types often have irrelevant content and noise attached to them. These require synthetic data generation, natural language processing, image recognition, and ML techniques for meaningful analysis. The complexity doesn’t end here. It is difficult to scale storage and process infrastructure to manage the sheer increase in the volume. 

However, various advanced tools have been impressive in extracting valuable insights from the chaos. MonkeyLearn, for example, implements ML algorithms for finding patterns. K2view uses its patented entity-based synthetic data generation approach. Likewise, Cogito uses Natural Language Processing to deliver valuable insights.

The future of data intergration

Data integration quickly dissociates from traditional ETL (Extract-Transform-Load) to automated ELT, cloud-based integration, and others implementing ML. 

The ELT shifts the Transformation phase to the end of the pipeline, loading raw data sets directly into the warehouse, lake, or lakehouse. This enables the system to examine the data before transforming and altering it. The approach is efficient in processing high-volume data for analytics and BI. 

A cloud-based data integration solution called Skyvia is pioneering the space and enabling more businesses to merge data from multiple sources and further them to a cloud-based data warehouse. Not only does it support real-time data processing, but also greatly improves operational efficiency.

The batch integration solution covers legacy and new updates, and is easily scalable for large data volumes. It fits perfectly well for consolidating data in the warehouse, CSV export/import, cloud-to-cloud migration, and others. 

Since 90% of data-driven businesses could be inclined towards cloud-based integration, many popular data products are already ahead in the game. 

Further, in times to come, businesses can expect their data integration solution to process virtually any kind of data without compromising operational efficiency. That means data solutions should soon support advanced elastic processing that can work on multiple terabytes of data in parallel. 

Next, serverless data integration will also get popular as data scientists look forward to nullifying the effort needed to maintain the cloud instances. 

Stepping stones to a data-driven future 

In this post, we discussed the challenges from disparate data sources, divide-driven streaming data, unstructured formats, and others. Enterprises should act now and implement careful planning, advanced tools, and best practices to achieve seamless integration. 

At the same time, it is worth noting that these challenges are potential opportunities for growth and innovation if worked upon in time. By taking up these challenges head-on, enterprises can not only utilize the data feeds optimally but will also inform their decision-making.
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