How Enterprise Data Warehouse Impacts Businesses?
Enterprise data warehouses (EDW) have existed for thirty years already. EDW is an essential business element since it allows for enhancing analytics, boosting business processes, creating more efficient promotion companies, personalizing the user experience, etc. This article will tell you what an enterprise data warehouse is and how it functions. Also, we will provide examples of why implementing EDW will help your business.
Enterprise Data Warehouse (EDW) is a centralized repository that consolidates and stores all enterprise business data from various sources. If necessary, one can extract the data from these warehouses to physical devices like HDD, SDD, CD, flash-cards, etc., or via instruments like client relationship management (CRM) and Enterprise resource planning (ERP). Therefore, companies can swiftly process massive data sets within one storage structure and single unification instead of searching for and combining them from various dissimilar databases.
An analogy is searching for information in books. Earlier, there were only paper books in various libraries; hence, searching, comparing, and analyzing data from books was time-consuming and expensive. Furthermore, the problem worsened if the books were from different epochs or countries. It causes difficulties with terms, proper names, and contextualization. Almost any reader can find a digital copy, and many (at least those in English) are on Google Books Ngram Viewer. Therefore, people can easily access, compare, and analyze them.
It is likewise with business data. They were formerly stored on paper in different storages; hence, they were harder to use. Then they became digital, yet held in other places, namely disks, servers, and databases. Currently, business data are easy to keep in one place; thus, all parties can access them if necessary, from the seller to the company director or an audit. These systems are enterprise data warehouses.
Numerous architecture approaches provide particular features, benefits, and pros and cons for enterprise data warehouses. Yet, let’s focus on the most significant ones.
One-tier architecture. One of the most straightforward architectures for EDW. Here, reporting means connecting to the data storage linked to various analytical instruments. This EDW architecture is easy and cost-efficient to implement and adjust, yet storing more than 100 gigabytes of information will cause some issues: low speed, a necessity of thorough inquiry, confusing results, and limited flexibility. Hence, one-tier architecture for enterprise data warehouses is suitable only for small data volumes.
This approach boosts inquiries' processing speed and makes them less demanding to input accuracy. Furthermore, data marts can limit access for end-users; thus, EDW becomes more secure. A two-tier enterprise data warehouse is better for real employment scenarios in business processes.
Hence, three-tier EDW perfectly functions for large companies, those with a branched business structure, and companies working in various directions.
Permanent access to business data. The prime benefit of an enterprise data warehouse for business is that the parties involved have constant access to business data. It is much better than separate data storage for each major affiliate or organization subdivision, leading to a complex data extraction process. Moreover, it causes more issues in output data and is not secure for client privacy and company protection.
Fast and easy access to business data. Being able to make the right decision rapidly is one of those factors that provide critical advantages for business. When business owners have instant access to structured and accurate data, even if they have enough time, employing EDW will benefit them since it allows for more time on data analysis rather than collecting it.
The reaction of shops to the Covid-19 pandemic is a shining example. The companies with EDWs reacted to changing the demand structure since they had data in real time. Thus, they ordered the resupply for high-demand products faster than competitors — for example, masks, sanitizers, tablets, and laptops.
Higher quality of business analytics. Enterprise data warehouses save CEOs and directors the trouble of making decisions on limited data or instincts. All critical decisions that impact the strategy and company rely on precise facts supported by information from data warehouses. Furthermore, these can be original solutions, like collecting data in one place and finding unexpected patterns after the analysis. For instance, before the hurricanes in the USA, the sales of flashlights grew, and what is less obvious, the strawberry PopTarts cookies and dry lunch sales, grew too.
More straightforward and significant integrations. Modern software solutions for data storage can integrate with numerous instruments for collecting and analyzing data. We shall consider not only Excel or CRM/ERP solutions but IBM Cognos Analytics, SAP Business Objects, Microsoft SQL Server, etc. Yet, all these instruments are efficient only if one can unite the data from alien systems in a single structured database. It will eliminate the chance of duplicated data and helps to extract them easily and fast. Furthermore, integrating these instruments into an enterprise warehouse is much faster and cost-efficient than incorporating them into several storages.
Data quality and consistency. Since enterprise data warehouses collect information from various sources and transfer it into a unified format, they offer more accurate data for decision-making company processes. Financial, marketing, and sales offices within a company can employ the data for their needs, and they will be sure that it is accurate and true. Hence, each office will provide consistent results which motivate teamwork.
High investment return. A business's gain in income after an investment is called return on investment (ROI). EDWs can reduce expenses and increase revenue while also boosting business analytics quality, significantly increasing the return on investments and preventing the business from going bankrupt.
Historical intelligence. Many enterprises employ EDWs for history reports that helps to conduct advanced business analytics involving trend analysis, deep pattern search, and forecasting business development in the long term.
Here is an example of a possible technology stack for EDW:
The following step is the logical modeling of data or organizing company data into logical relations called creatures (real-world objects) and attributes (the characteristics that define these objects). Then these logical data models become structures within the database; for instance, the creatures become spreadsheets, attributes become columns, relations transfer to limitations for the external key, etc.
This step typically begins with selecting and adjusting the technologies (DWH platforms, software for security, data conversion technologies, etc.). Then the development team will create the conveyors ETL (Extract, Transform, Load) and implements data protection means.
After integrating the prime components, it requires integrating them with the existing data infrastructure (data source, business analytics instruments, data lake, etc.) and with each other so that it will be possible to transfer data to the future EDW.
Then you will have to test the product thoroughly:
When all this is done, the EDW is deployed.
After the deployment, you will have to provide constant support and services for the enterprise data warehouse. With time, it will require estimating the EDW productivity and the end-user satisfaction rate. It will help you to identify the weak points in the warehouse's functioning and advance its abilities according to the current needs of your business.