The data universe is expanding. It's no secret that the data businesses create, capture and analyze has been growing in volume and diversity, with no signs of slowing down.
The ubiquity of data in today's business environment dictates that even small businesses should be thinking of how they can use data for a competitive advantage. Increasingly, tools are becoming available to help with the collection and analysis of this data.
In this guide, we'll cover:
Data integration is simply the process by which data is collected from multiple sources, normalized and prepared for analysis. Data integration software are tools that collect and transform the data for common storage, typically in a data warehouse, from which it can be extracted for analysis, as depicted in the diagram below:
Traditionally, this is done through the extract, transform, load (ETL) process by a database administrator (DBA), who sets up the criteria the data should adhere to prior to storage. The criteria the DBA sets up, or defines for the data, are based on the most critical insights a business seeks to derive from the data.
The ETL process is an involved one in which data is collected, or "extracted" from the original sources, which often exist in widely varying formats. These include not only .CSV and XML files, but also online sources such as social media.
Once the data is extracted from the original source, it is "transformed" into a format that fits the parameters the DBA has defined for the data warehouse, or wherever the data will reside.
Conversely, the ELT (extract, load, transform) process manages the process in a different order—one in which the data is loaded into the database, where it's transformed (as opposed to having predetermined rules set up within the database, such as a data cube).
Data integration environment in TIBCO Jaspersoft
Increasingly for large enterprises, data lakes are becoming a popular data storage strategy for those dealing with big data.
The data is then integrated with other transformed data for like comparisons and analysis.
As a baseline, data integration tools should offer the following:
|ETL (extract, transform, load)||Collects data from outside sources, transforms it and then loads it into the target system (a database or warehouse). Because primary data is often organized using different schemas or formats, analysts can use ETL tools to normalize it for useful analysis.|
|ELT (extract, load, transform)||Collects data from outside sources, loads it into the database or warehouse and then transforms it to conform to requests for analysis. This feature allows the data to be manipulated/integrated within the warehouse itself, rather than prior to migration.|
|Data capture/connection||Allows software to "connect" to multiple—and sometimes disparate—data sources (including relational databases, XML, .CSV, data lakes, Hadoop, SQL etc.) for the purposes of data extraction.|
|Data transformation||Normalizes data across disparate sources by standardizing data, converting values and correcting numeric values to conform to minimum and maximum values.|
|Data quality management||Helps organizations maintain clean, standardized and error-free data. Standardization is especially important for BI implementations that integrate data from diverse sources, as this ensures that later analyses are correct.|
Some data integration software offers additional features, including more self service options (such as drag-and-drop development for citizen data analysts).
Typically, data integration resides in the realm of the DBA, who sits in the IT department.
Small businesses. These are businesses with little to no IT department. While traditionally, they have less need to manage vast amounts of data in a data warehouse, this trend is shifting, given the explosion of data in recent years. More and more tools designed to help "citizen" data administrators extract, integrate and manage data without the need for extensive programming knowledge are becoming available today.
Midsize businesses. These buyers are still likely to benefit from data integration tools that offer some level of self-service functionality, so that a robust IT department isn't required to architect complex data storage solutions. Real-time data demands and ad hoc granular data analysis are becoming the norm.
Enterprise businesses. These buyers will have a robust IT department capable of handling the traditional ETL process, which involves time and effort. Ironically, these larger enterprises may have more of a demand for real-time delivery of multistructured data as opposed to the “batch" delivery methods ETL is associated with. Increasingly, tools are becoming more and more sophisticated, with broader functionality sets from delivery to governance, to meet these demands.
Data Integration software provides two clear benefits to users:
Data integration as a field is undergoing some change. According to Gartner, data integration and quality tools as a market grew 2.5 percent in 2016 to $4.4 billion, though more traditional data integration tools, which serve merely as "connectors" for batch movement of data, had slower growth (report available to Gartner clients).
This is due in large part to the increasing "mass proliferation" of data according to Gartner, which has put greater demand on data integration tools to expand their offerings to serve various data delivery speeds, deployments and types.
Essentially, slow, plodding, structured data delivery is on the outs. More and more, enterprises are seeing the need for data integration flexibility, including virtual and real-time data delivery, as well as the ability to deal with hybrid data sources (cloud and on-premise). Also, businesses are looking more and more for data integration tools that can handle "multistructured" data, or data that comes in a diverse array of structures.
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