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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.

What is a Data Warehouse?

 

A combination of different types of strategic data aids, Data Warehousing (DWH) is a process that collects and manages data within a system. More often than not, Data Warehouses gather data from multiple sources while acting as a core designed for analysis and generating reports. Data Warehousing helps business leaders make better decisions, faster.

Data Warehouses are not particular products but an entire ecosystem. They allow users to find past and present information in a more organized manner compared to traditional operational databases. A well-designed data warehouse can access information faster and create better reporting processes.

Data Warehouse Data Structures

Data Warehouses take information from various sources and acts as a repository. There are three types of data in Data Warehouses: structured, semi-structured, and unstructured.

Before users can make use of the data through Business Intelligence tools, it first has to be processed through Data Mining. Data Mining looks for meaningful patterns from the Data Warehouse to create a holistic view of the business and give better recommendations to relevant business units.

Types of Data Warehouses

There are three main types of Data Warehouses: Enterprise Data Warehouse, Operational Data Store, and Data Mart.

The Enterprise Data Warehouse (EDW) is a business’ central data warehouse that classifies data while giving access to the right users. While Operational Data Stores (ODS) are real-time data often used for routine activities, and Data Mart is Data Warehouses made for specific business units.

What are Data Warehouses used for?

Data Warehouses are used by businesses to sort through data from various sources and organize them by context to become usable. Data Miners use them as a preliminary step to find a meaningful pattern.

Many industries regularly use Data Warehousing to improve their operations and data mining practices. From retail chains looking to track items and manage inventory to airlines that need to keep track of repeat customers and route profits, there are infinite ways that Data Warehousing aids businesses from all over the world.

The Struggles of Implementing Data Warehouses

Data Warehousing is not without issues. Data Mining becomes increasingly common, so are the restrictions that come with the amount of data that companies are allowed to store.

The beginning stages of building a Data Warehouse can also be an overwhelming process for companies that did have a good data management foundation. It can be time-consuming and require additional training to adjust people to their usage.

Many large companies also struggle with organizing a large amount of complex data that may not find in the existing cloud storage solutions and may need physical storage that entails additional maintenance, hiring of personnel, and use of space.

Conclusion

While Data Warehousing may seem like a resource-intensive process, it’s an investment that will be useful for years to come. It can help users efficiently access critical data, and integrate systems that would otherwise be a pain to consolidate.

Overall, Data Warehousing should be standard practice and creates a good foundation for Data Mining.