What is Data Integration?

Imagine you bought a brand-new sports car, but the manufacturer has neglected to include side-view mirrors. Your view through the front windshield is clear, and you can see the cars directly behind you, but you can't tell if it's safe to change lanes. You are missing a critical piece of data that you need to make a good decision. This is what it's like when your data isn't integrated: part of your view is crystal clear, but you have a blind spot the size of a truck.

Data integration involves combining data from different sources while providing a unified view of the combined data, enabling you to query and manipulate all of your data from a single interface and derive analytics and statistics. While the sources and types of data continue to grow, it becomes increasingly important to be able to perform quality analysis on that data.

Solving Issues With Data integration

Data integration seeks to solve many of the following issues that come about when you have disparate information stored in different applications across an organization.

Data Silos

A data silo, much like the grain silo it is named after, is a repository of data that is isolated. Generally, in businesses, this means that the information is under the control of a business unit or department and is not available across the organization. This can also occur when an organization has stored information in software that is incompatible. For example, maybe you have some of your marketing data in Salesforce, other data in Marketo, and still more in a database maintained by your Marketing team. But since these systems don't communicate, the information in each application is siloed, so you might be able to draw conclusions from the Salesforce data and the Marketo data, but you won't be able to bring those information sources together to understand the information in totality.

Slow Analysis

Business leaders agree that today's decision making is heavily dependent on good information. Yet, even though they rely upon good data, companies are often frustrated by the amount of time it takes to integrate it. If your data is spread across multiple teams, databases, and applications, it can take a long time to gather and process the data so you can analyze it. And if the process takes long enough, the data will be outdated by the time you have a chance to analyze it. Business decisions need to be made in real time, and the way to do that is to have a system in place to integrate data before you need it.

Deep Dives

When your data is scattered across different sources and applications, it's difficult to have a complete view of it. For example, maybe you have customer data from different devices and apps. Maybe you have data on purchases from your different storefronts and online sites, but you want to correlate that data with your customer information and you want to enrich it with timestamps and geographical information for a deep analysis of your sales data. If your systems aren't integrated, or if the data isn't compatible, you won't be able to correlate this information without considerable time and effort.

Benefits of Data Integration

With integrated data, you get the benefit of a blind spot-free, 360-degree view of all your data, and because you've integrated it, it won't take weeks to compile a report that you need to make a critical business decision. In addition, your data is available across your organization — your Marketing team can see the Sales data, and the Sales team can see Marketing data. More importantly, the Executive team can make sound decisions based on the aggregate data from all departments. In addition, because the data is being cleansed and processed for integration, the data quality is higher and it is handled in a way that meets compliance standards.

How to Integrate Your Data

Method 1: Traditional ETL and In-House Systems

It is possible to create scripts to scrub the data and then load it into a data warehouse or use traditional extraction, transformation, and loading tools to integrate data from different sources. However, these methods are very time-intensive, expensive, and error-prone. Traditional methods require data scientists to spend an enormous amount of time cleansing data because the data at the source and target may not use the same schemas, formats, or types. And traditional ETL tools are usually batched, rather than real-time. These methods are also expensive because they require a robust infrastructure and skilled manpower.

Method 2: Modern Automated Systems

Modern data integration uses data pipelines and supports a variety of integrations to replace outdated traditional methods of manually managing data sets, scrubbing them, and loading them into the individual data lake or data warehouse environments. Now, you can store, stream, and deliver the data you need, when you need it, from any cloud data warehouse — Amazon Redshift, Snowflake, Google BigQuery, Azure, or a number of other options. You can define data types and destinations, enrich the data stream, and check for errors while the data is streaming. Then, you can get the real-time insights you need to make good business decisions. Perhaps of equal importance is the security that a modern ETL solution can offer. Any time data is moved from one place to another, the security risk increases greatly.