Advantages of Data Warehouse

Being a subject-oriented, integrated, time-variant and volatile, data warehousing caters several advantages to enterprises and users when implemented for business purposes. The successful application of DWH delivers great results and improves the overall functioning of every organization.

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Delivers Enhanced Business Intelligence
With data warehousing techniques and processes, data can be accessed and analyzed from multiple sources. Thus, the data is not limited to any specific section, which benefits business people to make improved and intelligent business decisions. The data warehouse and related BI processes can also be directly implemented in inventory management, financial management, sales and marketing.
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Ensures Data Quality and Consistency
Data Warehousing supports data conversion into a common and standard format. The standardization of data and output lets multiple departments of an organization to produce commensurate and well-formed results with no discrepancy from any end. Hence, businesses can run with higher accuracy and consistency, generating persistent and dependable employment decisions.
Saves Time and Money
Keeping all the data in one place certainly saves user’s time to access a specific set of data. They can make rapid decisions on key enterprise actions as enterprises do not spend extra time in analyzing the unordered data from multiple sources.
A data warehouse execution does not require much of IT support and does not even involve a higher number of channels , thereby ensuring cost-effectiveness. Similarly, the business executives interested in querying data won’t wait for the other IT processes to work before any data retrieval. The business continue to run every time and anytime, without any time lag or reliance on external sources.

Tracks Historically Intelligent Data
Since DWH is known for storing historical data, it keeps users and enterprises updated about the conventional customs and trends changing with time. So businesses can track data in different time periods and proceed likewise in the future. This also allows organizations to gain a competitive edge over others.
Generates high ROI
 As per reports by IDC, companies that have invested in data warehousing implementation  and related BI systems generated a higher revenue and saved incredibly on each of the business models and processes.
Limitations of using DWH
Extra Report Work
Unquestionably, the bigger the organization, the more data it holds and the extra time and load the data warehouse runs. The data generated by DWH requires the involvement of each department in the organization and thus, bothers with extra report work. Many times, it also involves data from consumers and clients, which again causes annoyance and trouble of entering excessive data.
Inflexibility and homogenization of data
As discussed above in the benefits section, sometime the similarity and standardization in the data formats lead to inflexibility and homogenization of data. This further limits the data in terms of establishing relations during aggregation and difficult to tune for query speed. Meanwhile, the homogenization also causes loss of data.
Ownership Concerns
While warehousing is all about centralizing data at one place for the ease of analysis and access. It sometimes causes issues to different departments as they hesitate to share their personal data within a central repository. This also raises security and ownership concerns for few departments. In this case, organizations must ensure that the analysis of data is given to trusted individuals within the enterprise.

Demands for large amounts of resources
If not IT support, but data warehousing implementation certainly requires large amounts of data resources to manage and handle data from multiple sources. This, in turn, raises cost concerns and cost/benefit ratio for the companies. Nonetheless, businesses can choose to execute it wisely by optimizing their costs yet generating best results.
Hidden issues consume time
Sometimes, internal sources fueling the data warehouses keep bundling issues that are undetected for years. For instance, while entering customer data, some values field accept null values, which may result in incomplete customer data in the future, even if the data is available.

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