Avoid These Mistakes On Your Data Warehouse And BI Projects
These days, organizational decision-making is highly dependent on efficient Data Warehousing and Business Intelligence. These technologies have helped industries make faster and more informed decisions, no matter how big or small. Widespread implementation of these analytics-based technologies has also opened up extensive career opportunities for specialists. Fortunately for database experts, data warehousing jobs and demand for business intelligence expertise are at an all-time high, and it is the right time to grab the right job.
However, the implementation of data warehousing and business intelligence projects can be quite complex and involve multiple risks. It is the responsibility of the data warehousing team managers to identify those data quality risks concerning the data warehouse implementations. This article outlines five such risks associated with BI and data warehousing implementations across multiple industries.
However, before we dive into those details, let's briefly recap what data warehousing and business intelligence are and how they are related.
What is Data Warehousing?
To be precise, Data warehousing can be defined as a data management system that stores and retrieves enormous data in prescribed formats. As an important part of the ETL (Extract-Transform-Load) system, data warehouses store the data extracted from multiple sources in an organized format. These data are then aggregated and organized by data warehousing technology for large-scale analysis and reporting.
What is Business Intelligence Architecture?
The Business Intelligence Architecture (BIA) is the technology infrastructure that enables businesses to perform their routine administrative tasks. The architecture includes Data analytics tools, data processing algorithms, ETL systems, and technology standards that facilitate an organization's business intelligence needs. In short, the BI architecture is the technology blueprint that makes business data available for analysis and data processing.
Data Warehousing and BI (DW/BI) system
Both data warehousing and business intelligence are interdependent and cannot function independently. Data warehousing begins with collecting data from flat files, RDBMS (Relational databases), CSV files, Excel files, and Web services (SOAP/REST). The collected data is then loaded into the repository. The process of extracting data from different sources, transforming them into suitable formats, and loading them into the repository is known as ETL(Extract-Transform-Load). The data is moved into a temporary data repository, also called staging, and then to the Data Warehouse.
In a nutshell, the Data Warehouse stores historical data that is used in data analysis. The data warehousing process can be considered as the process of moving this data to the source.
Business intelligence activities yield insights using information obtained from data warehouses. In other words, data warehousing and business intelligence are combined as one DW/ BI system that answers all business queries and contributes to decision making.
Even though the entire process might appear flawless and error-free, data warehousing jobs encounter several glitches during DW/BI implementations. The following section explores five common mistakes that happen during the DW/BI implementations.
5 Common mistakes to avoid while implementing DW/BI Projects
Failure to introduce QA testing early in the project
In the early stages of DW/BI projects, the focus is more on building an operational data store, creating enterprise-level data warehouses, and setting up the application reporting infrastructure. Hence, testing and data quality are often overlooked. Eventually, this affects data security, data quality, ETL processes, data consistency, business flow accuracy, end-user experience, and overall performance.
As the successful implementation of data warehouses depends on all these factors, it is imperative to plan effective tests early on in the project. Although there are still debates about when to introduce testing, it is more effective to begin testing at the requirement and design stages. It significantly increases the overall test efficiency.
Similarly, an early entry into the software development life cycle allows the testers to learn more about the product and the business rules. Consequently, they can design effective test plans and test cases at every stage of software development.
- Issues with data transformation
Even though ETL processes appear simple and flawless, they are often complex because they involve multiple sources. Integrating data from multiple sources add to unforeseen complexities. Common issues with data transformation include:
- Establishing key relations from multiple sources
- Searching terms and normalizing values
- Updating records without impacting performance
- Maintaining time zone consistency
Improper transformations can lead to inconsistencies and complicate the whole DW/BI system. Additionally, you will not be able to derive any valuable business intelligence, eventually making decision-makers halt their deployments.
- Implementing flawed management controls
Change is inevitable in every data warehouse project, regardless of the industry. New requirements and improvements often lead to changes in the overall project scope and deliverables.
Changes like these are especially difficult for testers who have to go through multiple technical specifications, ETL job flows, design and requirement documentation, data mapping tools, and more. A quality assurance process can only be effective if the testers link and identify these documents to change management processes. BI changes initiated by multiple stakeholders, business owners, or stakeholders often complicate data warehousing jobs. For all data warehousing and other skillset related I.T. jobs, visit techfetch.com.
Good change management is essential to facilitate communication and motivate more buy-in. This successfully encourages the adoption of new functionalities.
Source - Pixabay
- Ignoring the stage by stage testing
One of the biggest flaws observed in DW/BI implementation is ignoring the phase-by-phase testing and deployment. Many organizations choose to build a warehouse completely and begin testing and deployment at the end. This method may be viable for software development, but there are many factors to consider in DW projects. You may have to integrate information from multiple sources along with several metrics.
It is best to break down the entire data warehouse/business intelligence process into manageable short projects. The individual projects can be deployed separately and tested. This ensures that each phase of your project is successful and that the data will effectively be integrated into the system on completion.
- Failing to adopt automation
Unfortunately, several organizations still bank on manual ETL processes for customers or highly visible applications. With the increased adoption of DevOps for Data warehousing and faster application releases, the dependency on manual ETL test processes can be time-consuming and costly.
By automating tasks like regression testing and smoke testing, users can reduce their involvement substantially. Besides that, automated code testing with the implementation of a new database can save time and money. However, many companies avoid investing in automated tools owing to insufficient budget allocation. In that case, the in-house building of test tools is much recommended. Besides saving time and money, test automation is a great way to ensure quality BI deliverables.
Final thoughts:
There is no doubt that DW/BI projects are fundamentally risky, and industries often tend to implement systems overlooking the risks mentioned above. The time-tested solutions recommended here can save significant money, time, and human resources and help improve results.
