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The data integration process must be exact to develop visualizations, dashboards, and reports with correct and trustworthy data.
Fremont, CA: For analytics reasons, organizations worldwide regularly acquire massive amounts of digital data from several sources. These data get stored in various data formats and seldom saved in a single database in a single format. However, a competent online analytics solution has the difficult challenge of combining all of these disparate data sources stored in various forms for fundamental business analysis.
If the analytics tool performs a good job of integrating and analyzing data, it should provide users with a comprehensive perspective of the organization's operations and how effective they are! As a result, data integration is critical in web analytics.
Data integration is the process of integrating data saved in various forms across many databases to be used by a single software application. So defined, data integration is the joining of unconnected components with no shared features.
As a result, every web analytics system must have a faultless integration procedure to draw relevant insights about the organization's health or growth. In addition, the data integration process must be exact to develop visualizations, dashboards, and reports with correct and trustworthy data.
What Is Data Integration?
Data integration that is inconsistent can only result in useless data that is hard to compare. Because of the sheer volume and diversity of data acquired by digital analytics technologies, accuracy in data integration is becoming increasingly difficult. Without a faultless data integration procedure in place, web analytics will be ineffective and unreliable.
Customized programs for integrating heterogeneous data from several storage sources into a standard format can get built in-house. However, if the data size expands rapidly, such systems may not condense the data efficiently.To keep such customized apps working perfectly with rapidly growing, diverse data, significant hardware expenditures and complex code must get undertaken. As a result, it may not always be possible for all businesses, particularly start-ups.
As a result, the data warehouse is the only answer to this challenging data integration problem. A data warehouse helps you overcome this challenge by acting as a database for disparate databases. It mainly serves as a repository for unrelated data that other programs may access and use for analysis. The data warehouse receives this ready-to-use data via a process that extracts, cleanses, transforms, and distributes data regularly.
Various applications that require ready-to-use aggregated data, such as the online analytics tool, will access a data warehouse and extract the relevant data for analysis.Every data integration solution, on the other hand, has flaws. Data warehouses are no exception. However, to build a successful data integration solution for business, the user will need enough resources and a clear understanding of your requirements.