Data Consolidation

Data is the primary driver of an efficient AI model. It is the precursor of automation, adaptation, and prediction, so with data unification, the models can be fabricated up to the desired precision. Data consolidation refers to the collection, extraction, combination, transformation, and storage of data in a centralized manner.

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Different techniques like ETL (Extract, transform & load), data visualization, data warehousing, and data integration can be amalgamated as per the customer’s necessity. The primary goal is to retrieve data from heterogeneous information sources and transform it into valuable insights with data cleansing, aggregation, interpolation, extrapolation, textual analysis, etc.

Challenges Faced by the Customers

Poor Data Quality and Analytics
All industries are data-oriented nowadays. It is thus impossible to maintain the quality level of data while sourcing it from different sources of varying capacities. In this scenario, poor data quality and insufficient analytics can hamper the strategic approach, object-oriented growth, and many reformative initiatives of data-intense industries. Unless all workflow is properly digitized and integrated on a single platform, data quality management will be intractable. This, however, rectifying this once data is streamlined and unified, which obviously can be achieved swiftly with AI modeling for data consolidation.
Incompatibility
Data from multiple sources is always in a different format. A significant time is therefore dedicated to the ETL phase. This manual process can take over months, distorting organizations' operational structure. To make sure this time-taking and cost-ineffective approach, AI-modeled data consolidation shall be taken into place.
Latency
Effective data management on time is a significant challenge when data is isolated or it is to be outsourced from different sources. The data migration process, therefore, takes too much time with integration afterward. At this moment, automatic sourcing and integration are only achievable with AI.
Security
Multiple layers of protection are always needed to secure data from external or internal threats. Data consolidation using automation is the only key to achieving this. This is the basic need of Business Intelligence.
Poor Data Quality and Analytics
All industries are data-oriented nowadays. It is thus impossible to maintain the quality level of data while sourcing it from different sources of varying capacities. In this scenario, poor data quality and insufficient analytics can hamper the strategic approach, object-oriented growth, and many reformative initiatives of data-intense industries. Unless all workflow is properly digitized and integrated on a single platform, data quality management will be intractable. This, however, rectifying this once data is streamlined and unified, which obviously can be achieved swiftly with AI modeling for data consolidation.
Incompatibility
Data from multiple sources is always in a different format. A significant time is therefore dedicated to the ETL phase. This manual process can take over months, distorting organizations' operational structure. To make sure this time-taking and cost-ineffective approach, AI-modeled data consolidation shall be taken into place.
Latency
All industries are data-oriented nowadays. It is thus impossible to maintain the quality level of data while sourcing it from different sources of varying capacities. In this scenario, poor data quality and insufficient analytics can hamper the strategic approach, object-oriented growth, and many reformative initiatives of data-intense industries. Unless all workflow is properly digitized and integrated on a single platform, data quality management will be intractable. This, however, rectifying this once data is streamlined and unified, which obviously can be achieved swiftly with AI modeling for data consolidation.
Security
All industries are data-oriented nowadays. It is thus impossible to maintain the quality level of data while sourcing it from different sources of varying capacities. In this scenario, poor data quality and insufficient analytics can hamper the strategic approach, object-oriented growth, and many reformative initiatives of data-intense industries. Unless all workflow is properly digitized and integrated on a single platform, data quality management will be intractable. This, however, rectifying this once data is streamlined and unified, which obviously can be achieved swiftly with AI modeling for data consolidation.

In a nutshell:

Establishing a solution-providing entity with scalability, integration, minimal footprint, maximum security, centralized infrastructure, etc., is only achievable with data consolidation in the first place.
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