There is no arguing that big data is a valuable resource. However, all that data has to be processed properly into meaningful, actionable information so that it can then be used to optimize business processes, analyze the market, and create new revenue streams, among others. This is why data integration, while a challenging aspect, is a critical process in handling big data.
Simply put, data integration is the process of extracting and combining data from separate and often dissimilar sources into relevant information for the end-user. For example, the manager of an organization’s marketing department may take various statistics from the marketing, sales, and operations teams in order to create a complete performance report.
But apart from gleaning beneficial information from a pool of data, efficient data integration is even more crucial in today’s world due to the following trends.
More Data Sources from More Locations
A few years back, data used to be gathered from limited sources, like legacy applications, websites, and on-site resource planning software systems. And while these data sources are still around these days, more and more data can now be collected from hundreds of thousands of starting points from around the world. These include mobile applications, social media analytics, emails, published electronic journals, among many others.
These exabytes (perhaps even more) of data need to be processed quickly and efficiently if data scientists are to make anything of them. Efficient real-time data replication, which simply means that the data is immediately copied to multiple locations the moment it is created, is at the heart of data integration. It’s a process that helps ensure that all stakeholders have access to the latest form of the data at any given time. This, in turn, helps ensure that the most accurate information is derived and distributed to the end-users.
More Diversified Data Types and Models
It used to be that data warehouse architects, designers, and developers only dealt with flat files and data from online transaction processing (OLTP) systems, moving them from relational databases into relational warehouses with dimensional schemas. Today, with the emergence of file systems like Hadoop HDFS, Google File System, and GFS2 by Red Hat, NoSQL databases, machine data, and various other data types, data integration is even more important to understand the data and convert it to actionable information.
Moreover, since the data can come in structured, semi-structured, or unstructured formats from millions of sources, data integration can also help with the classification and management of the data so that it can be easily identified by its users.
Skyrocketing Volumes of Data
From YouTube videos and text messages to online shopping carts and monthly power consumption readings, almost everything we do in the digital age can be converted into data. In fact, IBM reported that we are generating 2.5 quintillion bytes of data every single day.
Data integration not only helps us make sense of massive volumes of data, it also allows us to cope with the speed by which data comes in, interpret data that comes in a variety of forms, and maintain veracity even with an increasing number of data sources. The first of these is especially important because for time-sensitive and mission-critical processes like air traffic control, for example, even 2 minutes is too late.
Big data is more than just collecting the biggest quantity of facts and figures; rather, it’s an opportunity to find insights hiding behind the data. With the help of data integration, organizations can answer questions related to business success, and be more responsive to customer needs and market trends.
