When we face Business Intelligence,we must carefully study its main discipline, which over time has been integrated into another discipline that has appeared with the arrival of Big Data; we talk about Business Analytics and Data Science (Data Scientist).
Table of Contents
Business Analytics is the most growing activity and profession in Business Intelligence since the arrival of Big Data. But first, you have to know what is being dealt with here. The definition of Business Analytics provided by Thomas H. Davenport of Competing on Analytics is:
“We understand by business analytics the intensive use of data, statistics and quantitative analysis, predictive and explanatory models, and decision-making based on facts and evidence. BA can be an input for decision-making by people or it can be an engine for automated decision-making.”
1. Business Analytics Strategy / Business Analytics
To carry out a good Business Analytics strategy, it is important to carry out the following steps:
• Design of a data architecture for reporting, analysis, predictive modeling and self-service BI.
• Implement a BI Architecture Portfolio.
• Solution architecture for data discovery, data visualization, and in-memory BI.
• Enable Operational and Analytical BI.
• Create master data maintenance program and analytical data governance.
• Create shared metadata environments.
2. Activities of a Business Analyst / Data Scientist
3. Data, information and knowledge
In Business Analytics, knowledge must constantly iterate with the information that in turn is repeatedly executed with the data, generating a cycle that results in an enrichment of the first.
3.1. Data
By definition, data is the minimum semantic unit and corresponds to primary elements of information that alone are irrelevant as support for decision-making. They can also be seen as a discrete set of values, which say nothing about the why of things and are not indicative for action.
The data can come from internal or external sources of the organization, of an objective or subjective nature, or of a qualitative or quantitative nature, etc. For example, these can be a person’s phone number, their job title, etc.
3.2. Information
Information can be defined as a set of data processed and that have a meaning (relevance, purpose and context), and that, therefore, are useful for those who should make decisions, by reducing their uncertainty. Data can be transformed into information by adding value:
• Contextualizing: it is known in what context and for what purpose they were generated.
• Categorizing: the units of measurement that help interpret them are known.
• Calculating: the data may have been processed mathematically or statistically.
• Correcting: errors and inconsistencies have been eliminated from the data.
• Condensing: the data has been summarized more concisely (aggregation).
The information could be defined with this formula:
Information = Data + Context (adding value) + Utility (reducing uncertainty).
3.3. Knowledge
Knowledge is a mixture of experience, values, information and know-how that serves as a framework for the incorporation of new experiences and information, and is useful for action.
This originates and applies in the minds of connoisseurs. In organizations, it is often not only found within documents or data warehouses, but also in organizational routines, processes, practices, and standards.
Knowledge is derived from information, just as information is derived from data. For information to become knowledge, it is necessary to perform actions such as:
• Comparison with other elements.
• Prediction of consequences.
• Search for connections.
• Conversation with other knowledge bearers.
4. Conclusions
Business Analytics or Business Analytics, is the functional discipline that every business analyst must incorporate as their own skill when working on Business Intelligence and Big Data projects.
Business Intelligence and Big Data is the technological substrate that supports the activities of the Data Scientist. However, the line between BI IT expert and Business Analyst is getting thinner, as both have to be increasingly transversal disciplines.
