As we continue discussing the business intelligence perspectives topic, let us review some of the common methodologies and approaches which are used in this perspective.
Methodologies: There are no standardized business intelligence methodologies that influence the responsibilities and activities of the business analyst.
But the business intelligence initiative can work with the methodologies used in other perspectives which themselves might affect the business analysis role.
2 Approaches: Within the business intelligence structure there are a number of less formal and intersecting approaches that are linked to a specific business and technical contexts.
Types of analytics
There are three types of data analytics that constitute solutions, with increasing levels of systems complexity, cost, and value and they are:
• Descriptive analytics: these types of analytics use historical data to understand and analyze past business performance.
Business information can be classified and combined to best suit the stakeholder’s view and reported using strategic dashboards, tactical key performance indicator (KPI) scorecards, and operational level management charts.
Assumptions should not be made as to what interests the stakeholders, what decisions need to be made, or what actions might be executed.
The business analysis is focused on the information and communication requirements used in standard reporting, dashboards, ad hoc reporting, and query functionality.
• Predictive analytics: this uses statistical analysis methods to analyse historical data and identify patterns, and then uses that analysis to make predictions about future events.
The specific situations that are of interest to the stakeholders are stated, and the business rules are described.
The business analysis centers on the information requirements for pattern recognition through data mining, predictive modelling, forecasting, and condition-driven alerts.
• Prescriptive analytics: this elaborates on predictive analytics to identify decisions that are to be made and to start suitable action to enhance business performance.
Statistical optimization and simulation techniques can be used to decide on the best outcome among different choices.
For situations of interest to stakeholders, a detailed description of the associated decisions and potential actions are needed.
The business analysis is focused on the business objectives, limiting criteria, and business rules that support the decision-making process.
Supply and Demand Driven: The goals and priorities of a business intelligence initiative can be based on the goals of improving existing information delivery systems i.e. supply driven; or on the business goals of providing suitable information to enhance decision-making processes i.e. demand-driven.
• Supply-driven: this is based on the supply needs of the organization. This approach maps existing systems data to describe what data is available.
The implementation strategy would include the following steps:
- Time the addition of existing databases into the business intelligence
solution architecture. - Gradually replace or repair existing outputs.
- Investigate new insights that might be gained from the combined
data.
• Demand-driven: this is based on the customer needs. This approach begins with spotting the information output required to support business decisions, and then tracing that information back to the original data sources to decide on its feasibility and cost.
It allows for gradual implementation strategies that are not decided by existing database structures, and provides for early investigative use of business intelligence beyond existing reporting requirements.
Data Structures:
There are two types of data that business intelligence approaches consider and they are structured and unstructured data :
1. Structured data: these are recorded data that have been numbered and categorized.
In conventional data warehouse, the solutions are based on combining the structured data and a rules driven template. This is used to ensure data integrity.
The business analysis is also focused on data models, data dictionaries, and business rules to define information requirements and capabilities.
2. Unstructured data: in unstructured data, the structure and relationships are not predetermined and no particular organization rules have been applied to ensure data integrity.
Data business intelligence solutions can include semi structured or unstructured data such as text, images, audio, and video and this data frequently comes from external sources.
Information sets are obtained from the raw data. The business analysis focuses on metadata definitions and data matching algorithms to describe information requirements and capabilities.