The extract, transform, load process extracts data from various source systems and transforms it by calculating or using another means, and loads the data into data warehouse systems. The process makes data meaningful by converting it into desired state required hence facilitating business intelligence that leads to excellence. It also allows verification of data transformation and calculation rules, which improves productivity of business entities. To implement ETL solutions in business entities, it is important to know the business needs, which is the information content for end-users to make informed decisions. The quality, scope and context of data source through a data profiling allows proper ETL to be built. Therefore, a grocery store, university or hairdressing salon requires an understanding of their business need and scope of data sources.
In modern enterprises, a lot of data is handled and it involves a lot of operations hence requires regular decision-making processes to keep the enterprises running. Business intelligence in the entities simplify information discovery and analysis enabling decision makers to access, understand, analyze and act on the information at their own convenience. However, there are challenges that that affect implementation of the system in the enterprises. For example, lack of quality data and analyzing data from different sources may affect implementation since appropriate and quality data leads to effective system. These manifests through failure to integrate the required data to make the right decisions for an enterprise. In order to maximize impact of business intelligence tools, it should not focus on roles of individuals but allow access of information to all employees of an enterprise. Through this act, the employees will be free to provide more suggestions to improve the enterprise through business intelligence. Also, to minimize challenges of implementing, running and maintaining BI tools, businesses enterprises should aim for fewer and more relevant data to incorporate in the system and build it into daily processes.
Terms definition:
Enterprise reporting- Is the provision of information to the decision makers of an entity to generate reports based on data, create intuitive visualizations and make data driven decisions.
Online Analytical Processing (OLAP)- It is a software that allows users to discover data, review reports and analyze information from multiple database systems at the same time.
Mobile BI- It is the ability to access both technical and organizational elements of business intelligence data such as KPI and dashboards on mobile phones.
Real-time BI- It is the process of delivering business intelligence information and business operations information at the exact time they occur.
Operational BI- Is the process of reviewing business operations, data and processes I order to make effective and strategic enterprise decisions.
Software as a service BI- Is a cloud-based application accessed by an end user with secure internet connection and is hosted outside a company’s premises.
Open Source BI- It is an application that allows a business to install major policies and systems in their business premises for free.
Collaborative BI and Location Intelligence- It is the merging of business software’s with tools that ae collaborative in order to achieve data driven decision making.
Definitions:
Data Mining- It is the process of finding data and correlations within a large set of data to get useful information and predict outcome.
Predictive Analytics- Is the use of data and machine learning techniques to identify future outcomes based on current and historical data.
Text Mining- Is the process of converting unstructured text data into useful and actionable information in a business enterprise.
Statistical Analysis- Involves collecting, organizing and scrutinizing each data sample through interpretation and presentation.
Big data analytics- Is the use of advanced analytical techniques to examine large sets of data from various sources and sizes in order to help in decision-making process.
BI architects develop maintainable BI applications to meet the objectives of a company. They also recommend strategies to improve capacity of BI tools and provide expert guidance on Bi skills and technologies.
BI developers develop, deploys and maintains Business intelligence solutions, executes queries upon request of data, conduct unit testing and trouble shooting and also creates tools to store data. They also maintain and support data analytics methods.
Business Analysts create a detailed business analysis, showing problems, solutions and opportunities for improvement in the future. They also monitor progress and plan for the future success of the business entity.
Data management professionals support development, enhancement and maintenance of datasets for consistency, completeness and accuracy. They also interact with consumers and employees to find data requirements for new and existing applications and audit data to ensure integrity in a business.
The key performance indicators for the personnel include the return on investment made, costs and revenues, budget compliance, mean time to recover and effective business intelligence systems to serve requires. The professionals may be motivated through employee motivation strategies such as bonuses on an achievement of particular performances, instilling trust and encouraging transparency. This encourages individual people to perform their responsibilities to achieve success.
