Integrating means combining data found in different sources to allow the end user to have a unified view the same for a accessibility Ideal, at the service of business needs.
At the moment, the emergence of new technologies and the explosion of data pose a great challenge in this aspect, and this translates into greater technical complexity when implementing a data integration plan, even though the goal remains the same: avoid fragmentation by developing solutions to this.
A response to ever-changing needs
They are solutions for a range of technologies, incluido Data Warehouse, MDM applications, BI applications, SOA architectures, etc. and for all of them data integration represents a fundamental element.
Without taking into account the casuistry, data integration policies are focused on achieving optimal accessibility to make the most of the potential of information business thanks to its multifunctionality, that can be used for different uses:
These various data integration needs, Besides, have relegated traditional ETLs to analytical environments and data integration needs appear for other types of projects, as mentioned, from outsourcing, integration of applications or migrations to the database. , synchronization and a long etcetera.
Data integration technologies or solutions, therefore, they must allow their move and update through fast and reliable Good, at the end of the day, only when an integration is agile, you get a business that is also agile.
On a practical level, integration is a response to business needs that vary over time to obtain reliable data that responds to business requirements. To that end, integrate means so much scalability how to have more maneuverability when integrating services into a service-oriented architecture or, as an example, address projects that involve data transfer for latency or batch processing. real time.
Platforms that meet data integration requirements also help manage more efficiently by reducing development times and maintenance costs.. Despite this, its implementation is slow and complex.
In reality, even though data integration projects may cover sectoral areas during a progressive implementation, your end goal is holistic. In summary, it's about covering all the data, not to focus on specific projects.
The reliability of the data
In summary, integration means data reliability. A company that does not have integrated data or, as an example, you have performed a faulty integration, will not have reliable data, and this will result in problems of all kinds. In these cases, do not integrate media has difficulties to comply with regulations, customer dissatisfaction, loss of company status, lack of confidence in information, inability to make critical decisions based on data because it is unreliable, not being able to access the data when it is needed from anywhere or, as an example, lack real-time information.
If by passive the lack of data integration means inoperability, per asset, data integration means reliability for decision making and unification of data from different information sources to respond to any of the needs associated with data integration and, in general, all those initiatives that require obtaining reliable data in an achievable way.
An adequate data integration strategy must pursue the unification of the company's data to allow the fulfillment of all these objectives., even though the implementation of a plan may be more or less complex, according to the cases. Moving from heterogeneity and fragmentation to integration implies, in summary, the reward long-awaited competitive advantage.
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