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Data Integration 101: What it Means and Why it’s Important

What is data integration? In short, it’s the set of practices and tools that turn data into something you can actually use.

Let’s take a closer look at what data integration means, why it’s important and how you can achieve it.

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The Problem with Data

To understand why data integration exists, you have to appreciate the major problem associated with most data sets.

On its own, data is not usually very valuable. Unless you have very small amounts of data – which you can interpret by hand, but which do not usually contain enough information to generate meaningful insights – data sets are usually much too large for an individual, or even a team of data experts, to make sense of them just by looking at the data.

Think about it. Most of the datasets that you collect to help drive business insights are huge. A single server log file could be thousands of lines long. (The Syslog file on my laptop is about three thousand lines – and that’s just my humble personal computer, not a server.) And you’ll probably be analyzing hundreds of log files, not just one.

Server access logs, which help you understand who is visiting your website and when, can be similarly large.

So can transactions and sales records. Even data that is manually entered, like a database of customer information, could be quite large if you have a number of customers and collect many data points about them.

Why Data Integration?

The astounding size of most data sets is the main reason why data integration exists.

Data integration allows organizations to take vast quantities of data from disparate sources, then transform it into insights that are directly relevant to their business.

How Do You Achieve Data Integration?

Data integration is achieved through a variety of tools and processes.

For example, data aggregation, which means collecting data from multiple sources and merging it into a single location for analysis, is one important step in most data integration processes.

Data transformation, or the process of translating data from one format to another, is often another important step in data integration. For instance, if you have mainframe data that you want to move to a Hadoop environment, you may need to transform the data first to make it compatible with Hadoop.

Data visualization is an important resource, too. Its tools help analysts to interpret data and recognize trends more easily than they could by studying data in text form.

Data Integration and Business Value

data integration, data transformation, data aggregation, big data, data visualization

Ultimately, the goal of data integration should be to drive business value.

Without data integration, no amount of big data or analytics tools will guarantee that your organization can transform its data into actionable information.

With the proper data aggregation, transformation and visualization tools at your disposal, however, you can use all of the data around you to achieve what matters: Insights and clarity into your customers and your business.

For more Big Data insights, check out our recent eBook, 2018 Big Data Trends: Liberate, Integrate, and Trust Your Data.