{"id":23129,"date":"2025-01-15T10:24:35","date_gmt":"2025-01-15T09:24:35","guid":{"rendered":"https:\/\/www.regestaitalia.eu\/integrated-data-for-more-efficient-business-management-when-is-it-possible\/"},"modified":"2025-06-03T15:35:49","modified_gmt":"2025-06-03T13:35:49","slug":"integrated-data-for-more-efficient-business-management-when-is-it-possible","status":"publish","type":"post","link":"https:\/\/www.regestaitalia.eu\/en\/integrated-data-for-more-efficient-business-management-when-is-it-possible\/","title":{"rendered":"Integrated data for more efficient business management: when is it possible?"},"content":{"rendered":"

The use of data in business management has well-known and widely recognized benefits. However, companies today face a new challenge: the multiplication of data sources<\/b> due to an increasingly pervasive digital presence within business processes. <\/p>\n

In this context, data integration-that is, the ability to aggregate and harmonize information from disparate systems-becomes<\/b> crucial to ensure efficiency, both from a strictly operational and decision-making perspective. But what exactly is meant by data integration and in what situations can it be applied? And most importantly, why is it so important? <\/p>\n

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To fully understand this, let us take a step back: it is now clear that<\/span> the effectiveness of a data-driven approach is closely linked to the quality of the data itself.<\/b><\/span> This concept is no longer just theoretical or academic, but has tangible business implications.<\/span><\/span><\/p>\n

A research related to the Italian market<\/span>\u00b9<\/span>, in collaboration with the Observatories of the Politecnico di Milano, shows that currently <\/span>only 20 percent of companies are aware of the costs of poor data quality.<\/b><\/span><\/span><\/p>\n<\/div><\/div><\/div>

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The advance of artificial intelligence is helping to raise awareness in this area: <\/span>among the entities that are approaching AI<\/span>, in fact, the percentage of those aware of the costs associated with poor quality of <\/span>data rises to 41 percent.<\/span><\/span><\/p>\n

Data integration is undoubtedly an effective strategy to guard against this problem: a proper approach, in fact, requires a series of preliminary steps aimed precisely at eliminating redundancies and dissimilarities<\/b>, creating a uniform and normalized corpus, typically through a data lake.<\/span><\/p>\n

Current definition of data integration<\/span><\/h2>\n

Data integration is the process of combining information from different sources<\/b> to create a unified and consistent view. This approach involves the aggregation, transformation and standardization of data<\/b>, making it accessible and usable across the enterprise. Integration tools help break down information silos and create a Single Source of Truth, a single, trusted source for the entire organization. <\/span><\/p>\n

There are several possible techniques for achieving data integration, among which we mention the four most popular:<\/span><\/p>\n