1. Companies are aggressively moving to computerized support of their organizations. Can you list at least 2 of the factors driving this move?
• Speed and efficiency.
• Legibility and accuracy.
• Cheaper research and development.
2. The definition of Business Intelligence (BI) is:
BI is an umbrella term that combines architecture, tools, databases, analytical tools, applications and methodologies.
What does “umbrella” term mean?
The definition of Business Intelligence (BI) encompasses various software applications used to analyze an organization’s raw data. The discipline entails many related activities, including data mining, online analytical processing, querying and reporting
3. Sometime we say that the term Business Intelligence (BI) is “context free”. What does this mean?
The term business intelligence is “context free” in the sense that the expression means different things to different people. For this reason, we have seen researchers advancing different definitions for business intelligence.
4. Describe what a data warehouse is and how it might differ from a traditional database used for transaction processing.
A data warehouse is a central repository for corporate data and information that an organization derives transaction data, operational systems and external data sources. Although these two may look like they are similar, they exhibit several differences with regard to usage pattern, architecture as well as technology. A traditional database is based on operational processing while a data warehouse is based on informational processing.
A data warehouse focuses on storage, filtering, retrieval and analysis of voluminous information.
A traditional database is used for day to day operations while a data warehouse is used for long-term informational requirements.
5. What is the difference between a data warehouse and a data mart?
A data mart is a subset of a data warehouse that relates to specific business line. Data marts are managed by a specific department within an organization. On the other hand, a data warehouse involves multiple subject areas and assembles detailed information from multiple source systems.
6. What is meant by “Big Data”?
Big data refers to a huge volume of structured, semi-structured and unstructured data from which viable information can be extracted. This kind of data is so voluminous that it cannot be processed using outmoded database and software techniques. Big data helps organizations to improve their operations and be in a position to make quick and smart decisions.
7. Data mining methods are divided into supervised and unsupervised methods. What are these and how are they different?
Supervised data mining method has to do with the presentation of fully labeled data to a machine learning algorithm. On the other hand, unsupervised data mining methods conduct clustering. Data instances are divided into a number of groups.
Unsupervised data mining methods do not put emphasis on predetermined attributes. Moreover, it does not predict a target value. Instead, unsupervised data mining finds hidden structure and relation among data.
Supervised data mining methods are appropriate when there is a specific target value that I to be used to predict about data. The targets can have two or more possible outcomes, or even be a continuous numeric value.
Supervised data mining methods the classes are known in advance while in the other the groups or classes are not known in advance. In supervised data mining methods, data is assigned to be known before computation but in unsupervised learning Datasets are assigned to segments, without the clusters being known.
8. When we consider KPI’s (key performance indicators) we distinguish between driver KPI’s and outcome KPI’s. What is the difference between the two (give a couple of examples of each)
Key performance indicators provide a framework on which organizations can value their progress. Outcome KPIs which are also referred to as lagging indicators measure the output of previous activities. On the other hand, driver KPIs/leading indicators measure the activities that have a significant on outcome KPIs. Driver KPIs have a significant effect on outcome KPIs, but the reverse is not necessarily true.
9. A BSC (balanced scorecard) approach for BPM (business process management) is well-know and widely-used. Describe the strengths of a BSC approach.
BPM entails activities
BPM involves activities like automation, remodeling, monitoring, and analyzing and improving business processes.
This is one of the most palpable benefits of BPM approach. It cuts down on costs and increases revenue. BPM adds crucial value in the long run by allowing businesses to compete globally. BPM technology equips a business to switch gears and respond to changing business environment appropriately.
Change is inevitable in business and a business must be ready to undergo sudden changes at any time. BPM accords a business the flexibility of making changes at minimal costs.
BPM automates several elements within regular workflows. Process improvements such as eliminations of drawbacks, elimination of redundant steps, and introduction of parallel processing are achieved through BPM. These process improvements allow employees to focus on other important activities of their business since the core support functions would have been handled.
Basically, BPM uses advanced software programs to facilitate the automation process. These programs enable process owners to keep abreast of their performance. Apart from guaranteeing transparency, BPM keep track of how processes work without the need of monitoring techniques and extensive labor.
10. A closed-loop process is often used to optimize business performance. Briefly describe what a closed-loop process means.
A closed-loop process, also referred to as feedback control system is a management system that promotes a well-organized base of preferred outcomes and system feedback. This process is designed to achieve and maintain the desired output in comparison with the actual condition.