Importance of Business Intelligence:
Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end users make informed business decisions.
Gone are the days when Businesses saw Data Science and Analysis as Rocket science. Solutions often include Software development Components but may also include an organizational change in strategy and planning.
Data is the New Oil. A Firm that has data about its clients readily knows of their habits and what they want. B. I don’t mean only writing computer codes, they are different roles definition depending on the organization. The responsibilities appear to be:
- To investigate business systems, taking a holistic view of the situation. This may include examining elements of the organizational structures and staff development issues as well as current processes and IT systems.
- To evaluate actions to improve the operation of a business system. Again, this may require an examination of the organizational structure and staff development needs, to ensure that they are in line with any proposed process redesign and IT system development.
- To document the business requirements for the IT system support using appropriate documentation standards.
In line with this, the core business analyst role could be defined as an internal consultancy role that has the responsibility for investigating business situations, identifying and evaluating options for improving business systems, defining requirements and ensuring the effective use of information systems in meeting the needs of the business.
It should be noted that there is a difference between Business Intelligence and Data Analytics.
Business Intelligence is about figuring:
happened ,When it happened, Who did it, and How many are they in Number.
- B.O reports KPIs, maintaining Dashboards, Scoreboards.
Data Analytics is about :
- Why it happened again, Will it happen again? What will happen if we change
- Involves Statistic techniques, Data Mining, Predictive Analysis, Big Data, Machine Learning.