Data silos and their disadvantages for big data

Storing information in data silos is common in many companies. For example, it occurs when data is stored on devices or on a non-shared platform. It is a way of working that helps to protect information by easily controlling access to it. However, in recent years the applications of Artificial Intelligence and Big Data have become key factors in the strategy of many companies. Data offers companies the possibility to analyze their actions, learn from them and even predict and plan for the future.

To apply all these functionalities, information should not be stored in isolated data silos. Therefore, it is necessary to break down data silos and unify information to take full advantage of its analytical and predictive potential. To break down these structures, Microsoft has launched two cloud tools: Azure Synapse Analytics and Azure Purview . In addition, the company has restructured operational data warehouses to take advantage of a new architecture in the cloud environment .

Breaking Data Silos with Azure Synapse Analytics

Azure Synapse Analytics is an unlimited analytics service that allows you to integrate diverse data sources, enterprise Data Warehousing and Big Data analytics. Companies can work with their data quickly and securely, achieving maximum information productivity and generating insights from different data sources. The tool is able to combine data engineering, machine learning and business intelligence capabilities without creating data silos.

Complete data compression with Azure Purview

Azure Purview is an end-to-end data governance solution that makes it easy for businesses to gain a complete understanding of information. It does this by reaching all business data, tracking information and creating a glossary to designate where that data is stored.

This tool, integrated into Microsoft Information Protection, uses more than 100 Artificial Intelligence classifiers that search for personally identifiable information (PII), sensitive data and flag non-compliant data.

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