The term Data Integration defines the process of merging data from multiple different sources into a unified view: from the assimilation, cleaning, mapping and transformation of data, to the more easily usable intelligence processing by those who access it.
Today, companies implement Data Integration initiatives to be able to analyze and use information more effectively, in particular with the spread of new Cloud and Big Data management technologies. Data Integration is a must for the modern company, essential for improving decision-making processes and increasing the competitive edge.
There is no universal data integration strategy. However, all integration solutions typically include some common elements, such as a network of data sources, a master server, and clients accessing data from that server.
In a typical data integration process, the client sends a request for data to the master server. The master server then assimilates the necessary data from internal and external sources. The data extracted from the sources is subsequently combined in a logical, unified and usable form, and finally delivered to the client.
Why Data Integration is Important
Even if a company receives all the data it needs, these often reside in several distinct sources. For example, to gain a 360-degree view of the customer, the information to be combined can include data from corporate CRM systems, web traffic logs, marketing software, customer-facing applications, sales support systems, and to customers and even partners, just to name a few examples. The information from all of these sources must be unified for analysis needs or operational interventions, a task that is far from simple for the developers and data technicians in charge of doing it.
Let's now analyze a typical use case of data analysis. Without unified data, to process a simple report you need to access multiple accounts on different sites, access data within native apps, copy, reformat and clean them, all before starting the analysis.
In order to perform all these operations, in the most efficient way possible, you cannot do without Data Integration. Here are the main benefits of a well-orchestrated data integration strategy:
1. Data Integration improves collaboration and systems unification
Employees in every department - and sometimes even off-site - increasingly need to access company data for shared and individual projects. The IT department needs a secure solution to distribute data through self-service access to all lines of business.
In addition, the staff of almost all departments generate and optimize the information that the rest of the company needs. Data Integration must be collaborative and unified in order to improve collaboration and unification within the organization.
2. Data Integration saves time
If a company intervenes to integrate the data correctly, the time to prepare and analyze such data is drastically reduced. The automation of unified views eliminates the need to acquire data manually and staff are no longer forced to create connections from scratch every time they need to run a report or develop an application.
In addition, by using the right tools, rather than manually coding the integration, you can save even more time (and resources) for the development team.
All the time saved in these activities can be used more profitably, by dedicating a greater number of hours to the analysis and execution of processes aimed at improving the productivity and competitiveness of the organization.
3. Data integration reduces errors (and rework)
There are many things to watch out for when dealing with a company's data resources. To manually capture complete and accurate datasets, staff need to know each site and account they need to access - and have the necessary software installed before they begin. If a data repository is added without the employee being informed, the information collected will necessarily be incomplete.
Furthermore, without a Data Integration solution capable of synchronizing information, reports must be periodically reworked to include any changes. Thanks to automatic updates, however, reports can be easily run in real time, whenever necessary.
4. Data Integration allows you to produce more useful data
Data Integration activities actually improve the quality of a company's data over time. As data is integrated into a centralized system, quality problems are identified and necessary improvements implemented, which ultimately leads to the availability of more accurate data - a fundamental requirement for quality analysis.
ETL and Data Integration
The Extract / Transform / Load process, commonly known as ETL, is a Data Integration process in which information is taken from the source system and stored in a Data Warehouse. This is the permanent process used in Data Warehouses to transform multiple data sources into coherent and useful information for analysis and business intelligence purposes.
SIINFO implements Data Integration solutions with the most modern commercial and Open Source tools such as Talend, Knime, NiFi, Oracle Data Integrator but also frameworks such as Spark.