Foundational concepts of business intelligence
Business Intelligence is a term for data and software tools for organizing, analyzing, and providing access to data to help managers and other enterprise users make informed decisions. It is a roadmap to measure a company’s or business’s performance to achieve a competitive advantage and listen to their customers using data mining and statistics.
Different organizations will invest in a Business Intelligence (BI) solution for different reasons depending on their specific circumstances, needs and industry. Due to this reason, they need to find the relevant modules for supporting them. A Business has to work on fulfilling certain essential functions such as Finance, Human Resources, and Logistics. Different solutions are available for both data warehouse and data mart such as OLAP, OLTP, SAP and data mining.
Ad hoc business intelligence inquiries benefit greatly from the higher performance offered by online analytical processing (OLAP), a solution. The common dimensional model used in data warehouses is the organization method that OLAP is made to work with effectively. OLAP is not intended to enable high volume update operations or to hold massive amounts of text or binary data. OLAP excels at quickly summarizing data for analytical queries because historical data stored in a data warehouse is inherently stable and consistent.
The OLAP database is made for quick data retrieval and searching. Businesses must choose which provider to purchase their OLAP from. Additionally, they must weigh the pros and cons of each OLAP type and OLAP server before deciding which to utilize for their organizations. It could be quite important for corporate productivity. For instance, not all operating systems can run every type of OLAP server. In order to acquire the finest OLAP for its purposes, a business must decide which operating system to choose. Additionally, not all OLAP servers offer different security types like SSL and SSPI. Some are multi-secured, while others are particular. The OLAP database and server that a company or firm picks will also influence how it hires its IT staff and what tools and apps it needs to deploy.
OLTP is typical of online stores like Amazon. Online transaction processing, or OLTP, handles online transactions quickly. However, this procedure merely modifies and updates a single or a number of small to medium-sized databases without combining them analytically as OLAP would. The database’s actual design would be necessary during this stage. It is determined what the business requires, what kinds of databases are appropriate for storing the required information, and what information needs to be saved.
By leveraging a variety of functions to optimize supply chain activities, SAP supports logistics and other planning. Additionally, it aids in planning, documenting, and keeping track of business processes. It aids in cost minimization, planning, tracking, and performance. Occupational and production safety procedures, regulatory compliance, and corporate accountability are just a few examples of additional environmental functions it might have. For environmental impact assessment and safety, SAP EHS combines corporate policies, compliance, and environmental, health, and safety capabilities with global business processes for human resources, logistics, production, and finance.
Data mining is a technology that uses complicated and sophisticated algorithms to examine data and uncover relevant information for decision-makers to consider. Data mining analyzes data and presents the findings to decision-makers, whereas OLAP organizes data in a model suitable for analyst investigation. As a result, data mining and OLAP both facilitate analysis that is driven by data.
Data validity, or being consistent and accurate, is referred to as data integrity. The phrase “Garbage In, Garbage Out” is commonly used in the data warehousing industry. The abbreviation ETL stands for extract, transform, and load. The data is fed into central databases, sometimes known as data warehouses, from specialized databases. Data deduplication and verification are carried out during this stage.
We must make sure that neither the ETL procedure nor the data warehouse contains any unapproved methods of data alteration. To achieve this, security measures against unauthorized access to data must be in place, including physical access controls to the servers and the tracking of all data access activity. Only if there is no unauthorized access to the data can data integrity be guaranteed.
Any subsequent report and analysis won’t be helpful if the data warehouse doesn’t have data integrity. For example, by analyzing accurate data from customer credit card purchases Louis’s Trattoria a US restaurant chain, learned that quality was more important than price for most of its customers. Based on this info the restaurant introduced vegetarian dishes, and seafood selections and the sales rose by ten percent. Data integrity is the pivot in decision-making.
(Data Integrity, n.d.; Lamani et al., 2019; Laudon & Laudon, 2011; Microsoft Support, n.d.)
References
Data Integrity. (n.d.). Www.1keydata.com. Retrieved July 30, 2022, from https://www.1keydata.com/datawarehousing/data-integrity.html
Lamani, A., Erraha, B., Elkyal, M., & Sair, A. (2019). Data mining techniques application for prediction in OLAP cube. International Journal of Electrical and Computer Engineering (IJECE), 9(3), 2094. https://doi.org/10.11591/ijece.v9i3.pp2094-2102
Laudon, K., & Laudon, J. (2011). Management information systems. : global edition. Addison Wesley Longman Ltd.
Microsoft Support. (n.d.). Support.microsoft.com. Retrieved July 30, 2022, from http://office.microsoft.com/en-001/excel-help/overview-of-online-analytical-processing-olap-HP010177437.aspx
