Proper application of Business Intelligence Services (BI) and Data Warehouse implementation allows you to drill down into the organization’s data. It allows you to draw conclusions from information in order to gain a competitive advantage on the market. To implement an effective BI tool, a company needs a well-designed data warehouse first. Data Warehouse design is the process of building a solution for data integration from many sources that support analytical reporting and data analysis. However, a badly designed data warehouse exposes you to the risk of making strategic decisions based on erroneous conclusions.
In this article, we will take a look at the data warehouse design process on a high level – starting from the collection of requirements up to the implementation itself. The article will also help you not to make key mistakes related to the implementation of the data warehouse.
What is a Data Warehouse?
Before we get to the step-by-step guide to implementing a data warehouse, let’s get started from what exactly is a data warehouse. The data warehouse is defined differently, which complicates the formulation of a strict definition. In general, a data warehouse is a centralized data management system that combines company information from multiple sources in a single repository. This data is then used to provide analytical insights about the company’s performance over time and to make better decisions. Moreover, data Warehouse occupies a leading position in data science for business and advanced artificial intelligence or big data analytics.
Gathering Requirements for BI and Enterprise Data Warehouse implementation and design
Collecting requirements is the first stage of the data warehouse design process. The purpose of the phase is to define the criteria for the successful implementation of the data warehouse. The requirements for analysis and reporting, as well as hardware, software, testing, implementation, and training of users, should be specified. The organization’s long-term business strategy should be as important as current business and technical requirements.
After outlining the business and technical strategy, the next step is to determine how an organization will backup the data from the warehouse and how to recover the system in the event of a failure. The development of an emergency recovery plan while collecting requirements ensures that the organization is ready to react quickly to both direct and indirect threats of data loss.
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Determination of the physical environment for ETL, OLAP, and database
The next step is to determine the physical environment of a data warehouse. At least there should be separate physical application servers and databases, as well as separate ETL / ELT, OLAP processes, and reports configured for development, testing and production. By building separate physical environments, we must ensure that all changes can be tested before transferring them to production. Development and testing should be done without stopping the production environment, and if data integrity becomes suspicious, IT staff can investigate the problem without affecting the production environment.
Data modeling using Star Schema or Snowflake approach for data warehouse implementation
After defining requirements and physical environment, the next step is to determine how data structures will be available, combined, processed, and stored in the data warehouse. This process is known as data modeling. During this phase of data warehouse design, data sources are defined. Identifying the source of the data and, what is equally important, the availability of this data is crucial to the success of the project. After identifying data sources, the data warehouse team can start building logical and physical structures based on set requirements.
Extract, Transform, Load (ETL)
The purpose of ETL (Extract, Transform and Load) is to provide optimized data loading processes without losing data quality. The ETL process takes the most time during development and consumes the most time during implementation. Identifying data sources during the data modeling phase can help reduce ETL development time. Therefire, failure at this stage of the process may lead to the poor performance of the ETL process and the entire data warehouse system.
OLAP or Tabular cubes designing
On-Line Analytical Processing (OLAP) is an engine that provides an infrastructure for ad-hoc queries and multidimensional analyzes. Requirements for dimensions and measures of OLAP cubes must be specified at the beginning of the data warehouse design process. So, below are three key elements of OLAP design:
- Grouping measures – numerical values that we want to analyze (such as revenues, number of customers, the number of products purchased by customers, or the average purchase amount).
- Dimensions – they complement and extend the analysis of measures to areas such as the geographical region, month or quarter.
- Data granulation – the lowest level of detail that we want to include in the OLAP data set.
You need to make sure that OLAP cube processing is optimized during the development stage. After data warehouse updating, OLAP cubes should be updated quickly. Failure to update any of them in a timely manner can result in poor system performance. Taking time to explore the most efficient OLAP cube generation path can reduce or eliminate performance-related problems after the data warehouse is deployed.
Business Intelligence Front-End development
After defining business requirements, placing physical environments, modeling data and designing ETL processes, the next step is related to the choice of the method and form of sharing data contained in the enterprise data warehouse (EDW). Choosing the right front end tool (Power BI, Tableau, Looker) is to ensure the way in which users will access data for ad-hoc analysis, pre-defined reports, and dashboards. Various options are available, including also the construction of a front-end in-house part in your own strength or the purchase of an off-shelf product.
Either way, keep in mind the requirements of business users to ensure the best quality of shared data. The most important element of the entire process is secure access to data from any device – desktop computer, laptop, tablet, or phone. The tool should enable the development team to modify the back end structure when changing the reporting requirements at the enterprise level. It should also provide a graphical user interface (GUI) that allows users to customize reports. The OLAP engine and data warehouse may be the best in its class. However, if users are not able to use data effectively, the data warehouse becomes an expensive and useless data repository.
Report and dashboard development
Most end-users typically use data warehouses only to generate reports or dashboards. As we mentioned in the front-end development section, the ability to quickly and efficiently select report criteria is an important feature of generating them from a data warehouse. Distribution options are another important factor. In addition to receiving reports via a secure web interface, users may need reports sent as an e-mail attachment or as a spreadsheet. Controlling the flow and visibility of data is another aspect of the development of the reporting system. Developing user groups with access to specific data segments should ensure security and data control. A well-designed data warehouse should be able to handle new requests related to ad hoc reporting.
ETL performance tuning
We recommend creating separate programming and test environments. As a result, organizations can provide improved system performance using ETL, query processing, and delivery of reports without interrupting the current production environment. You need to ensure that the development and test environments, hardware, and applications mimic the production environment. As a result, the productivity improvements developed during the development phase will work more efficiently in the production environment.
After developing a data warehouse system in accordance with business requirements, next is time to test it. Testing or quality assurance is a step that should not be omitted. As a result, it will allow the data warehouse team to reveal and resolve problems before the first deployment. Failure to complete the testing phase may lead to delays in the completion or completion of the data warehouse project.
Enterprise Data Warehouse implementation & deployment
A decision whether the system will be available to all will depend on the number of end-users. As a result, it additionally depends on how they will access the data warehouse system. Another important aspect of system implementation, which is often overlooked, is the training of end-users. The training should be carried out regardless of how intuitive the GUI is, from the point of view of the DWH team and programmers. If the tool for end users is difficult to use and “incomprehensible”, then they will stop using it, leaving out all the advantages of the system.
Understanding the best practices in designing a data warehouse and its implementation
Designing a data warehouse is a time-consuming and demanding undertaking. Good and bad aspects appear at every step. However, if the organization devotes more time to develop robust requirements at the very beginning – the next steps of the process will be more smooth and lead to the successful implementation of the data warehouse.
Benefits of data warehouse implementation for your business
Firstrly, with a data warehouse implementation, you can convert data from multiple sources into a common structure. This way, you can guarantee the reliability and quality of your corporate data. It allows your company to identify and delete replica data, incorrectly recorded data, and disinformation.
Improved Business Intelligence (BI)
Secondly, you can use the data warehouse to collect, assimilate, and extract data from any source at all stages of your business. As a result, your business intelligence will improve quickly due to the ability to easily consolidate data from various sources.
Smarter Decision Processing
Furthermore, data warehouses support business intelligence features such as data mining, artificial intelligence, and machine learning. Data warehouse implementation allows your company to provide solutions in practically every area, including business processes, financial management, operations, and inventory management, sales, and marketing.
Data Warehouse Implementation in different industries
Here are some examples of the everywhere use of data warehouses. Let’s take a look at the different industries where data warehouses are an important part of their daily business.
Data warehousing is being actively implemented in the healthcare industry for predicting results, reporting on treatment, and sharing data with insurance companies, research institutions, and other healthcare providers. Data warehouse implementation is the base of healthcare systems. In other words, having the most recent and up-to-date treatment information is vital to saving lives. 
Bankers can effectively manage all available resources thanks to the implementation of a data warehouse. In addition, they can improve their analysis of customer data, government rules, and market trends to make better decisions.
In the retail sector, data warehouses are mainly used for distribution and marketing to provide product tracking, price policy research, advertising transaction tracking and customer demand trend analysis. Retailers typically include corporate data warehouses for business analysis and forecasting.
You may also find it interesting – How to maximize profits in retail using Business Intelligence.
In the investment sector, data warehouse implementation is used to analyze market trends and customers, as well as other data models. For example, forex and stock markets are the two main sub-sectors where data warehouses play a key role, as a one-point difference can lead to huge losses around the world. 
Data Warehouse Implementation Trends
Cloud Data Warehouse
The cloud is quickly becoming the preferred option for users who want to buy data warehouse capabilities. Cloud data warehouses not only support all of the features of traditional data warehouses, but also optimize operations like data centers, business intelligence backends, analytics sites, and more.  Above all, cloud data warehouses automatically perform serial backups, which ensures data availability and fault tolerance at the level of 99.99%. 
Data Warehouse as a Service
DWaaS reduces the cost of purchasing, setting, and maintaining hardware and software. Because the DWaaS provider manages the data storage, there is no need to recruit an in-house team to manage the storage infrastructure.  Moreover, the data warehouse as a service market is predicted to reach $ 23.8 billion in 2030. Also, North America is projected to be the major data warehouse as a service market by 2030. This is explained by the increasing emphasis of the retail and e-commerce, healthcare, BFSI, and public sector sectors on data analysis for valuable information. 
Data Warehouse implementation and Big Data integration
The combination of historical business data with less structured data from big data sources provides hidden data patterns and insights that can drive business improvement efforts. 
Every day, many professionals and business leaders use data warehouses to make key business decisions that affect the lives of people all around the world. In short, data warehouses may be profitable and convenient, not to mention an affordable and attractive return on investment that can make your company even more competitive.
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See how we implemented business intelligence for manufacturing companies to enhance management efficiency by implementing an automated reporting system. Let us know if you have any questions regarding Data Engineering or Business Intelligence implementation.
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- Scnsoft.com. Data Warehouse Implementation in 2021. URL: https://www.scnsoft.com/analytics/data-warehouse/implementation. Accessed June 11, 2021.
- Globenewswire.com. Data Warehouse as a Service (DWaaS) Market to Reach $23.8 Billion by 2030, with Growing Focus on Real-Time Data Analysis: P&S Intelligence. https://www.globenewswire.com/news-release/2020/03/16/2000805/0/en/Data-Warehouse-as-a-Service-DWaaS-Market-to-Reach-23-8-Billion-by-2030-with-Growing-Focus-on-Real-Time-Data-Analysis-P-S-Intelligence.html. Accessed June 11, 2021.