Data integration is crucial for innovative companies that want to improve decision-making processes and increase competitiveness. That’s how. Data integration offers a 360-degree view of data deriving from multiple and different sources—a real need in the era of multi-cloud, Internet of Things and big data management. This is what data integration means, a must for innovative companies, being a prerequisite for developing advanced analyses to extract new knowledge.
What Is Data Integration?
Data integration is bringing together data from multiple different sources to offer users a unified view. Data integration refers to the data sources, the type of data and the architectural approaches. The data integration process ranges from collection to data cleaning, from data mapping to transformation, making its use more usable – in an integrated view – to those who access it.
Types Of Data
The types of data available to organizations fall into five categories:
- Machine To Machine (M2M) data: data produced by the interaction between electronic devices (such as sensors, RFID, Wifi connections);
- People To A Machine: the data that arise from the interaction between people and devices, for example, during eCommerce transactions;
- People To People: data that comes from the interaction between people (generated on social media by the activities of members, on forums and blogs);
- Public Admin Data: data found in public databases (Open data or data freely available to everyone to be analyzed and reworked according to one’s interest, without limits on copyright, patents or other control mechanisms);
- Enterprise Data: they are retrieved within the corporate data warehouses (ERP data or corporate CRM data
Examples Of Sources:
- web traffic data;
- Corporate ERP or CRM;
- marketing software to be customized;
- apps dedicated to customers;
- support systems for sales, customers, and partners;
- IoT devices for predictive maintenance;
- wearable devices ( wearable tech ) to customize health plans;
- Road map sensors for self-driving cars.
Approaches: Data Lake And Data Warehouse
It was once customary to create data silos, but the advent of big data now overtakes the traditional approach to storing separate data for each business function. This architectural configuration (in which data storage is organized according to separate repositories by the company department, the environments are isolated from each other, without communication and integration) is opposed by other approaches such as data warehouses and data lakes. In the data warehouse, the computer archive that collects data from the company’s internal operational systems integrates them with data from external sources.
The data then must be structured or require representation by relationships that can be described with rigid tables and diagrams. Data lakes constitute a data storage environment in their native format until they need to be given structure. This management model allows the integration of large amounts of data of any format and deriving from any source. The integrated model allows an organization to have both a data lake and a data warehouse that collaborate in an integrated way to respond in synergy to the different storage, management and analysis of each type of data.
Furthermore, data integration allows information to be taken from the source system to deliver to the warehouse of a data warehouse through the ETL process (Extract / Transform / Load or extraction/transformation/loading). It allows you to provide consistency to multiple data sources to be transformed into information that supports data analysis and business intelligence.
Developers need to unify multiple sources to analyze the data or even offer a unified view. Finally, without data integration, even compiling a report becomes complex. You need to access multiple accounts on different sites, access data in native apps, copy them, reformatting and data cleaning, and then move on, for example, to big data analytics.
What Data Integration Means
Companies adopt data integration to analyze and exploit information more effectively, especially in the cloud and big gata. Data integration is, in fact, crucial for the innovative company that wants and must improve decision-making processes and increase competitiveness. While there is no lack of a universal data integration strategy, all integration solutions have a common denominator: a controller server, a network of data sources, and clients accessing data from the controller server. In fact, in a data integration process, the client sends a request for data to the controller server, assimilating the necessary data from internal and external sources.
The data extraction from the sources leads them to combine them in a logical, unified and usable form to send them to the client. Data integration initiatives – in large companies that generate big data flows -as we have seen, make it possible to create data lakes and data warehouses. Data warehouses integrate multiple data sources into a relational database, enabling users to enter queries, process reports, produce analyses, and find information in a consistent format. Data integration allows the data warehouse to make high-level summary information accessible in a format where alignment fits together perfectly.
What Is Data Integration For
Planning the data unification serves to:
- improve collaboration and integration in the company, helping employees to work on shared and individual projects;
- avoid wasting time: in fact, the automation eliminates the need for manual data acquisition and eliminates the need to start from scratch for each report or application development;
- drastically cut errors and subsequent processing: a data integration that synchronizes information and does not have to periodically reprocess the reports to modify them (instead, automatic updates allow you to carry out reports in real-time);
- improve the quality and, therefore, the accuracy of the data ;
- Simplify the analysis processes.
Data Integration Theory
Data integration theory is part of database theory. Use first-order logic to formalize the concepts of a problem. It serves to assess the feasibility and difficulty of integration. They are abstract theories but general enough to fit all integration systems, including those that:
- they include a nested relational system;
- XML databases;
- They treat databases like programs.
Implementation-level technologies, such as JDBC, offer Oracle or DB2 DBMSs connections.
Common Data Integration Tools Are
- Analytics Canvas;
- Ultrawrap platform by Capsenta;
- Cloud Elements API Integration;
- Denodo platform;
- elastic.io Integration Platform;
- Microsoft PowerQuery;
- IT Platform;
- Oracle Data Integration Services;
- Red Hat JBoss Data Virtualization;
- RapidMinder Studio;
- Azure Data Factory;
- SQL Server Integration Services;
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