Although "data analytics" and "business intelligence" are frequently used together, they have several key distinctions. Data analytics focuses on forecasting what will happen in the future, whereas business intelligence focuses on looking to the past to describe what happened using historical data. It's not easy to tell the two apart, though. Both phrases are crucial in creating a data-driven organisation since they both include the use of data to support business decisions.
Business intelligence is the term used to describe the systems, programmes, and procedures used to gather, integrate, analyse, and present business data. Its goal is to facilitate wiser business decisions. BI reports are made to be run and evaluated frequently to provide intelligence on historical performance and provide answers to hypothetical inquiries. Descriptive analytics is a subset of business intelligence that includes reporting on customers, operations, and sales.
On the other side, data analytics entails applying data science methods to make future predictions. Analytics that are descriptive, predictive, and prescriptive fall into these three types. Data is transformed into something business managers can visualise, comprehend, and analyse using descriptive analytics. Predictive analytics offers insights into possible future outcomes based on descriptive data and additional predictions made using data science and algorithms. Prescriptive analytics recommends what activities to take and which will result in the best results.
Data scientists use massive data sets and algorithms to organise and model them. Data analytics for business requires a higher level of mathematical expertise. It uses algorithms, simulations, and quantitative analysis to identify links between facts that don't immediately seem to be related. Data analytics aims to discover why events occurred rather than providing answers to inquiries about what occurred. To answer a basic business question or solve a problem, we must ask questions repeatedly, using the responses to guide the next query.
Business intelligence and data analytics have several things in common despite their differences. Both include some data preparation, and data analytics typically involves data modelling, which involves gathering, cleaning, categorising, converting, aggregating, validating, and transforming raw data. Additionally useful for business intelligence is clean data. After being cleaned, the data is saved in a structure and format that makes reporting easy. The data is frequently kept in a data warehouse, which serves as a single source of truth for all organisational reporting for both BI and data analytics. Both require a data warehouse as the foundation of the analytics stack and an ETL tool to import data.
In conclusion, business intelligence and data analytics are crucial for creating a data-driven organisation. Data analytics looks towards the future, while business intelligence is more concerned with historical data. Predictive and prescriptive analytics is considered data analytics, while descriptive analytics is considered business intelligence. Both call for data cleaning and a data warehouse that acts as a single source of truth. Data analytics, however, relies on simulations, quantitative analysis, and algorithms to identify links between data that aren't immediately apparent and calls for a higher level of mathematical ability. Iterative questioning, using the results to guide the next inquiry, and resolving core business issues are the cornerstones of data analytics for business.
4/14/2023, 13:49:59 by Tech Tip 24
