Implementing Data Warehouse Has Now Become a Prime Concern for Many Organizations – How It Has Paved the Way for Better Productivity

By Swamini Kulkarni

With the top-end advancement of technology, the pressure to improve productivity has increased yet more. And, this is where data warehousing peeps in. It is interpreted as a practice of accumulating and handling data from a number of different sources to offer exclusive business insights. A perfect blend of required components and technologies, it not only aids in lowering production cost, but also paves the way for the strategic use of data. An electronic repository of a huge amount of information, data warehousing is intended for scrutiny, analysis and inquiry. The advanced process of converting data into information and making it accessible to the users in a timely manner is where its exclusivity lies in. 

Now, in case, you consider data warehouse to be a product, it’s time for you to get over the fallacy! Data warehouse is retained and propped up separately from the venture’s operational database and should be considered as nothing but an environment. As an architectural paradigm of an information system, it delivers past as well as current important business data which would otherwise have been difficult to access through the traditional data store. Incorporating data integration, data cleaning and data amalgamation, data warehousing can pleat data, examine it, and take decisions based on the information available in the warehouse. In a nutshell, data gets flowed into a data warehouse from the transactional system as well as other relational databases.

And, it doesn’t end here. There are also several high-end decision support technologies that aid in utilizing the data within a data warehouse in the best possible manner. When these techniques help the officials use the warehouse quickly and efficiently, they also impart greater executive insights into corporate performance. Then, the information accumulated in a warehouse can be perfectly cast-off in domains like customer analysis, operation analysis and tuning production strategies. Customer evaluation is done by analyzing the customer’s economical rounds, purchasing time and purchasing bents. At the same time, data warehousing also plays an active role in managing customer relationship and making required amendments for the environment, which, in turn, proves to be quite beneficial for business operations. Finally, the product policies can also be well modified by relocating the products and organizing the product collections while tallying the sales quarterly or yearly.

However, to assimilate heterogeneous databases, there are two approaches namely query-driven approach and update-driven approach. A traditional approach to consolidate heterogeneous databases, the quick-driven approach is generally used to keep integrators on the very top of multiple heterogeneous databases. These assimilators are also acknowledged as mediators. Now, coming to the process how this approach exactly works, when an enquiry is delivered to a client, a metadata dictionary interprets as well as decodes the inquiry into an ideal form for individual heterogeneous plots. Then, the inquiries are recorded and sent to the local enquiry processor. At last, the results from diverse sites are combined into a global answer set.

On the other hand, the update-driven approach is mostly an alternative to the traditional slant. And, today’s data warehouses in use are basically in the practice of following update-driven approach over the conventional approach mentioned earlier. In this line, the information from multiple heterogeneous sources are incorporated in advance and are stowed in a warehouse. This information, then, becomes accessible for direct enquiring and examination. Providing first-rate performance, this outlook also makes sure that the data is copied, managed, desegregated, interpreted, abridged and reorganized in semantic data store in advance. Simultaneously, meting out enquiries doesn’t really mandate an annexation to process data at local sources.

Furthermore, the importance of a data warehouse becomes even more pertinent when the logical requirements go against the constant performance of operational databases. Running a complicated inquiry on a database demands the table to pass in a provisional fixed state. And, this is often indefensible and quite shaky for transactional databases. Here, a data warehouse is enrolled to do the systematic as well as analytic work, making room for the transactional database free to pay special attention on transactions. 

According to Allied Market Research, the global data warehousing market is expected to register a CAGR of 8.2% during 2018–2025. Growing demand for dedicated storage system for increasing volume of data, rise in need for column-oriented data warehouse solutions to perform advanced analytics, and necessity for low-latency, real-time view & analytics on operational data fuel the growth of the global data warehousing market. On the other hand, several complexities and high implementation cost check the growth to some extent. However, emerging trend of implementing virtual data warehousing and significant surge in the application of AI in data warehouse have almost downplayed the factors and are expected to create multiple opportunities for the key players in the market.

To conclude, we can state that the global data warehousing market is expanding at a rapid pace and it’s going to proliferate yet more in the next few years to come.

Swamini Kulkarni holds a bachelor’s degree in engineering and works as a content writer. She is deeply fascinated by technological advancements and the trending topic in the world. When she is not glued to the computer, she loves to read, travel, and spend time thinking about how she could read and travel more often.