5 Big Data Integration Obstacles
Data integration is essential if you want to turn big data into something that actually drives business value. However, successful data integration doesn’t happen on its own.
Here’s a guide to overcoming common data integration obstacles.
What Is Data Integration?
Put simply, data integration refers to the tools, processes, and strategies that enable organizations to turn large, disparate data sets into usable information.
Without data integration, it can be impossible to interpret the large quantities of data that organizations commonly collect.
Data integration typically involves processes like data aggregation, transformation and visualization.
Data Integration Obstacles
Even if you have modern data integration tools at your disposal, you will still likely encounter challenges that can hamper effective data integration. Those challenges might include the following:
- Difficult data transformations. If you collect data from multiple locations – which most modern organizations do – your analytics tools might not support all of your data types in their original format. For example, modern analytics tools were generally not designed to work with mainframe data sets. The answer to this challenge is to transform the data – but that may also prove problematic if you try to do it by hand. That’s why automated data transformation tools are so important.
- Too little data. It can be easy to overlook the importance of data scale when performing data integration. Data integration tools generally aim to boil your data sets down to reveal the main insights and hide all of the data behind them. This is why it’s important to ensure that you have sufficient data available to drive the insights you need. If you don’t, solve that issue before you begin data integration.
- Poor data quality. Data integration tools can help you to interpret your data. But if you have bad data, the interpretations you arrive at will be flawed. Make sure you solve data quality issues within your data sets before starting data integration.
- Too many data sources. One of the main purposes of data integration is to help you combine data from disparate sources into a single set of insights. However, there are limits on the diversity of data that you can support in this way. It doesn’t always make sense to integrate all types of data. If you have two data sets that have very little in common, you are probably best off analyzing them separately.
- Slow data integration. Maximizing business value often requires real-time data insights. If your data integration processes deliver slow results, they may not be able to keep up with your business needs. This is why investing in automated data transformation and offloading tools can be worth it. They help you to achieve the speed required for real-time results.