Nowadays, most of enterprises are trying to keep up to date with the latest technology specially in information systems field, by implementing ERP systems and process automations systems. The increase use of those systems generated a huge amount of data. Accordingly, the new challenge for the enterprises is how to store, process, and analysis this data to help the enterprises to take the right decision on the right time. Many of new technologies are developed recently to address those needs, like Big Data, and Business Intelligence. On this article, we will focus on the Business Intelligence (BI).
What is the business intelligence?
According to (Bogdan NEDELCU, 2013) the business intelligence can be defined as “an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance” from the definition we can understand that the Business Intelligence is a continues process starting from the underlaying infrastructure that host the applications and solutions used by enterprises employees for data entry and orchestrating the internal process and the quality of the data. crossing where the data is stored and related technologies – like DBMS, or Data Warehouses – and the integrity of the data, and ending to the analytics, reporting, and visualization tools.
Business Intelligence project Lifecycle Management
(Steve Dine, 2016) “Business Intelligence (BI) projects are both time consuming and resource intensive, often suffer from poor communication between the business and IT”. Any BI project requires an incentive collaboration and cooperation between the business part and the technical part of the project. A good analysis for the business need and goals will lead the technical team to the right logical direction to select the right technology and right model development.
From the diagram we can find that the important step from the BI project lifecycle is Business Requirements Definition. Because it’s formed the base for many parallel processes will be fired after. Also, it’s continues process where the project should consider the new requirements / growth and include those new requirements within the project plan.
Business Intelligence use case
One of the interesting use cases is the one that introduced by (Fernando Iafrate, 2013) titled “Business Intelligence “New Generation” for a “Zero Latency” Organization (When Decisional and Operational BI are Fully Embedded)”. Where Disneyland Paris started by building underlying infrastructure from network, servers and storage with minimal latency rates and though this infra they started collecting the basic data from their restaurant, hotels, and shops and use their BI tools to generate on time reports on the Operation Control Center. Also, based on that basic data that flow in real time, they built predictive models that can guide the decision makers for the right strategies in the future. That setup, helped Disneyland Paris to achieve a higher rates of customer satisfaction.
References:
- Bogdan NEDELCU, 2014 , Business Intelligence Systems, Database Systems Journal. 2014;IV(4):12-20.
- Steve Dine, DEC 20, 2016, Business Intelligence Lifecycle Management, Datasource Consulting , http://ds.datasourceconsulting.com/blog/business-intelligence-lifecycle-management
- Naveen, September 30, 2019, Business Intelligence Lifecycle, intellipaat.com, https://intellipaat.com/blog/tutorial/data-warehouse-tutorial/business-intelligence-lifecycle/
- Iafrate F. (2013) Use Case: Business Intelligence “New Generation” for a “Zero Latency” Organization (When Decisional and Operational BI Are Fully Embedded). In: Benghozi PJ., Krob D., Rowe F. (eds) Digital Enterprise Design and Management 2013. Advances in Intelligent Systems and Computing, vol 205. Springer, Berlin, Heidelberg
