Data Warehousing Stages

Data Warehousing Fundamentals

Data Warehouses have long been a part of company standard operating procedures. However, as time passes, companies need more complex systems that can handle increasing amounts of data.

The main differentiator between the various stages varies in terms of data accuracy, triggers, and interactions between the operational systems and storage mechanisms. Between industries, there are different levels of sophistication required. They each have varying fundamental warehousing needs that affect workflow and customer experience.

Depending on the size, age, and industry of the company, Data Warehouses will be within one of the four stages or in a point of transition. So, where does your company stand?

4 Stages of Data Warehouses

Stage 1: Offline Database

In their most early stages, many companies have Data Bases. The data is forwarded from the day-to-day operational systems to an external server for storage. Unless extrapolated and manually analyzed, this data sits where it is and does not impact ongoing business functions. Transactions such as loading or processing of data have no effects on an operational standpoint.

Stage 2: Offline Data Warehouse

While not entirely up-to-date, offline Data Warehouses regularly update their content from existing operational systems. By emphasizing reporting-oriented data structures, the organized data meets the particular objectives of the Data Warehouse.

Stage 3: Real-time Data Warehouse

Real-Time Data Warehouses gathers information through operational system events-based triggers. Often, these come in the form of transactions such as airline bookings or bank balances.

Stage 4: Integrated Data Warehouse

Daily activities to be passed back to the operating system continuously in the Integrated Data Warehouse. Integrated Data Warehouses are the ideal Data Warehouse stage with the data not just readily available but also updated and accurate.

It’s pertinent to understand what stage your company’s Data Warehousing facilities are before you can improve them. The journey to creating the best version of your Data Warehouse takes time to set up and even more to integrate into your company’s workflow.

Companies need to invest time to transition their Data Warehouses to more efficient stages as their organizations become larger and more complex. Often, there needs to be regular training towards different business units to maintain the accuracy of the data.

Data Warehousing is not an overnight process and requires a long term strategy for both integration, adoption, and maintenance. There must be procedures in place to avoid fraudulent, obsolete, and incorrect data.

Companies must also be on high alert for possible security breaches that may affect the trust that customers place on the brand concerning their personal data. As the data compiled becomes increasingly granular, it becomes important to protect it from malicious intent.

Despite its many challenges, developing a good Data Warehouse is still a necessary investment that should be in every company’s roadmap. Having a working Data Warehouse is the first step to creating efficient Data Mining workflows. There’s no doubt that having a good Data Warehouse will be the standard for companies that want to get ahead and stay ahead of the competition.