Why Should Business Intelligence And Data Warehousing Be Blended?
For a long time, the terms Business Intelligence and Data Warehousing were nearly interchangeable in the industry. To conduct timely analysis of huge historical data, you needed to organise, combine, and summarise it in a specified way inside a data warehouse, and you really cannot accomplish one without the other.
However, this reliance on data warehouse architecture for business intelligence has a significant drawback. Data warehouses have historically been and continue to be a costly and rare resource. They require months and millions of dollars to put up, and once they’re up and running, they can only be use for a very limited range of analyses. Whenever you need to ask interesting questions or processing new sorts of data, you will be require to undertake significant development efforts.
Using a data warehouse with business intelligence
The short answer is yes, if you have the financial means to do it efficiently. While some businesses practise business intelligence without the use of a data warehouse, there are drawbacks to this strategy, which are often related to time constraints or money constraints, respectively. Specifically, it is possible that processing the necessary data may place a burden on transactional databases, resulting in decreased performance and increased load time. This has the effect of slowing down the analysis-to-insight process.
Apart from that, data sources that are not combined are less efficient and may lack readily available historical information. To put it another way, transactional databases are incapable of performing the same functions as a data warehouse. When it comes to making the proper choices for your business in a timely manner, having a good connection with your data is essential. This is made feasible via the implementation of a comprehensive data warehouse in conjunction with business intelligence best practises.
Is it possible to have one without the other when it comes to Business Intelligence and Data Warehousing?
Most firms employed decision support software to make data-driven judgments two decades ago, and this hasn’t changed much. These applications accessed and reported directly on data stored in transactional databases, rather than via a data warehouse as a middleman. This is analogous to the current trend of storing large amounts of unstructured data in a data lake and querying it straight from the data lake.
Issues that were encounter back in the day of decision support programmes that did not include the usage of a data warehouse are list below:
- The data was not always in a format that was acceptable for reporting. The data was often of poor quality.
- The processing of decision support data placed a pressure on transactional databases, resulting in decreased performance.
- The information was scatter over a number of different platforms.
- Because transactional OLTP databases were not design for historical data storage, there was a scarcity of historical information.
Essentially, a Data Warehouse is a filesystem for historical performance data that allows firms to do research on it and take data-driven choices based on the information. OLTP systems, (CRM) methods, Enterprise Asset Management (ERP) systems, Supplier Management (SCM) systems, and a wide variety of other sources might be use to collect this business data.
Advantages of using a Data Warehouse:
- More Accurate and Reliable Data: Data from all of the data sources is subject to a number of transformations. To guarantee that the data kept in the Data Warehouse is of the highest possible accuracy and reliability. Thus, numerous discrepancies that may have exist in the Operational Data have been rectify. In order to guarantee that only consistent and high-quality data is store in the Data Warehouse.
- Quick Decision-Making: Because the data in the Data Warehouse has been uniform and of good quality. It may be deem to be in a format that is acceptable for analysis at this point. As a consequence. The company representative may complete the necessary analysis in less time while not having to worry about getting wrong data.
Many organisations have had business intelligence and ETL Data warehousing systems for many years, which were developed and built using conventional tools and methodologies. However, at this moment, they are not producing the outcomes that businesses need. They are unable to extend to new data sources or deliver more advanced analytics for the organisation. Because of the limitations of their current data warehousing system. Perhaps they are unable to integrate new cloud-based apps, unstructured data, or large amounts of data properly. They may also be unable to take use of cloud data warehouses and hybrid environments. Which would otherwise be more cost and resource-effective solutions..
To summarise, business personnel are not receiving all of the data they want for their analyses. And the data they do get is difficult to work with..
By upgrading their BI system with new ideas and best practises, businesses have the potential to maximise the return on their investment and guarantee that they are no longer falling behind the competition.
Business intelligence and data warehousing modernisation effort:
- The use of hybrid cloud environments or a combination of cloud platforms while deploying cloud data warehouses.
- Including self-service business intelligence and data preparation skills
- The development of a data integration or data engineering framework. That includes the capabilities that you need, including. Such ETL, ELT, data pipelines and streaming, Connection services, and data virtualization.
- Building analytical sandboxes or data science laboratories (hub) for business analysts, data scientists, data engineers. And data scientists into the business intelligence environment
- Extending the capabilities of relational databases to include data lakes, sophisticated analytical schemas. And a logical data warehouse (LDW), as well as leveraging polyglot (or multi-schema) databases. To manage unstructured, semi-structured, and structured data.