A data warehouse can be defined as an informational environment that assists in extracting strategic information that is useful in making the strategic decision for the betterment of the enterprise.
In this context, we will define the data warehouse in brief along with the features that explain how data warehouse provides an integrated view of an entire enterprise. We will also discuss the advantages of the data warehouse.
Content: Data Warehouse and its Features
Data Warehouse Definition
In our earlier content, we have discussed the need of data warehouse where we have studied about how the use of operational system failed to retrieve strategic information and how the need of data warehouse arose.
Now, the question is what kind of data does data warehouse provide? Data warehouse let the user know about his company’s business, its performance over years, what kind of operations company has been doing, what are the recent trends and what measures could be taken for improvement of the company’s business.
Data warehouse provides all this information in an integrated form which can be easily analysed. The retrieved information includes data from the very past of the company. The data warehouse also provides an interactive environment to retrieve the strategic information.
Data warehouse is software?
Don’t consider data warehouse as a software, it is a computing environment. It gathers data from all the operational systems if required it also collects data from outside of the enterprise. The collected data is integrated and the inconsistencies in the data are removed. The data is now transformed into the format easy for analysing and decision making.
Features of Data Warehouse
Feature of data warehouse defines the nature of data it contains; it also explains how the data from the operational system is different from the data in the data warehouse. It also explains the use of a data warehouse. Below are the features of data in a data warehouse.
1. Subject-Oriented Data
Data in an operational system is from independent applications. These applications have data sets that are organized around the application and are required for the operations of the application.
Let’s overview an example to understand it better. A bank has to process several tasks for which it has distinct applications such as to process loans of the customer it has a different application, to process savings of the customers it has a separate application which contains the data sets that are essentials for executing the operations of this application.
Again, consider an insurance company, it would be dealing with several distinct application such as life insurance, car insurance, property insurance. Each application would contain the data set that is essential for its operation.
So, the data in the operational system is application-oriented.
The data warehouse is different from the operational system in the sense that it does not contain data that is organized around an individual application. It stores data by business subject or we can say the data in data warehouse is subject-oriented.
Well, the term business subject means the subject that evaluating from an enterprise point of view. The business subject keeps on changing from enterprise to enterprise.
Let’s overview an example to understand this as we have seen that the operational system of an insurance company would store data sets from individual applications such as life insurance application, car insurance application, property insurance application where the data is application-oriented.
But, the data warehouse of an insurance company would store the data around the subject claim, it won’t store application-oriented information. It stores subject-oriented information.
2. Integrated Data
Related data from the various application is integrated and stored in the data warehouse. A data warehouse can contain data from several operational systems. Apart from the internal source the data from outer sources is also mix in the sources of data in a data warehouse.
Now, when there are several sources of data in data warehouse there could be issues like different naming conventions, a data item would have different attributes for different applications. The data from different sources would be in a different format.
All these issues must be resolved before storing data from different sources in the data warehouse. Before storing data into data warehouse all the inconsistencies present should be removed. The data should be transformed into an acceptable format. The data should be standardized in the context of naming conventions, attributes, codes and measurements.
3. Time-Variant Data
As we know that data warehouse is used to retrieve strategic information that helps the executive in making a strategic decision. To make the decision for the betterment of the company the executive must analyze the past few years’ performances of the company.
The operational system has the information that is essential for day to day work, this is the current information. Suppose, if the executive has to analyze the buying pattern of the customer to analyze the customer’s choice. He needs the data regarding the buying pattern of the customer over the past few years.
This is why data house contains the historical data of the enterprise along with the current data.
4. Non-Volatile Data
The data in the data warehouse is non-volatile which means it is not liable to get update rapidly. A data warehouse does not have to support the day-to-day operations as we have an operational system for that purpose.
In a data warehouse, we do not keep information about every single transaction or processing. In fact, the data in the data warehouse has the snapshots of the data over a period of time. The data is updated to the data warehouse at a specific interval.
Also, the different data sets are updated at different frequencies. Like products attribute may be updated once in a week, product sale might be updated once a day and so on.
5. Data Granularity
We do not search in a data warehouse for a single piece of information. A data warehouse is always searched for the summary data as an operational system has information at the lowest level.
In a data warehouse, the data is kept summarized at different levels. Like you can search for the sale units of the product once for the entire region, further, you want to know the sale units of a product in different states or above that on an individual store. That’s why data warehouse contains summarized data at different levels.
Advantages of Data Warehouse
- The first important benefit of a data warehouse is that it provides strategic information for making business decisions.
- The data warehouse has data from the very past of the company.
- Data warehouse retains the quality and consistency of the data.
- Data warehouse saves time and improves the efficiency of taking decisions.
- Data warehouse ease executives to make strategic business decisions.
- Data warehouse provides strategic information.
- It stores data around a subject, store data from the very past of the company, stores data from all the possible source, and the data is non-volatile.
So, this is all about the data warehouse and its features which helps the enterprise using a data warehouse to stay competitive in the market.