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How to Create a World of Self-Serve Business Intelligence

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Layout Of A Data Warehouse

Your data is very likely within databases and systems (CMS, CRM, catalogues, analytics etc…) so why would you need a data warehouse too?

What Is A Data Warehouse?

It’s a hub that creates a single version of the truth, comprising all your company data. Drawing data from all your sources and databases, it makes your data available to users in an easy to understand format. Which means, the data is non-conflicting and can be used for strategic and tactical decision-making.

Implementing a data warehouse typically requires setting up storage where data from other systems is automatically imported and streamed. The data is cleaned and processed according to your business logic.

At the end of the cycle, information from the warehouse is provided to the end user’s preferred platform, supporting reporting, analysis and other business intelligence tools. The end user does not need any specific skills for accessing and manipulating databases. The data can be embedded into any number of applications, such as web pages, web portals, web apps and many more applications.

Here’s an example of the data warehouse process:

Layout Of A Data Warehouse

You may still have reservations, such as, why store the data in additional storage?

There are several benefits to implementing a data warehouse…

Single Version of the Truth

All users will get the same results for a single question. A data warehouse removes the issue of different stakeholders applying their own rules or only using the data which supports their assumptions, which would result in getting different outputs when processing the data.

Self-Serve

A data warehouse allows for self-service. You no longer need to rely on the technical team or analysts to answer the simplest of questions. Insights can be delivered to users in their preferred environment. As the data has already been cleaned this reduces the time for conducting repetitive data tidying tasks.

Improved Performance

Production systems will have a reduced load when a data warehouse is used. As reporting often requires expensive memory operations, it’s imperative that those do not affect the availability of production systems’ collection of data.

Excellent Data Retention

Often, systems aren’t designed to store historical data. Instead, new data overwrites the old state of the data, which means historical data can be lost. This issue can be addressed with a data warehouse, by recording snapshots in the warehouse and using those for reporting changes that occur over time.

A data warehouse is not another tool in your toolbox, but rather a foundation for creating a data-driven culture in your organisation.

Coloured circles connected together

Data Integration Solutions

You may also be interested in data integration solutions such as Microsoft SQL Server Integration Services (SSIS) or Azure Data Factory (ADF).

SSIS is a platform for building high-performance data integration solutions, including extraction, transformation, and load packages, in short ETLs, for data warehousing.

Azure Data Factory, it is like SSIS in the cloud. It allows developers to transform disparate data from on-premises and cloud sources into trusted information for further analysis.

Which Is The Best Data Integration Solution?

This depends, if your ETLs (Extract,  Transform, Load) are simple and easy to convert/replace then Data Factory is the favoured option. By contrast, if you require complex logic, SSIS is the better choice.

Put another way, if the transform logic can be implemented by configuration, Data Factory is the best. If it requires code development and programming skills, SSIS is the right tool.

Get in contact if you want to know more about data warehouses and data integration tools.
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