DataScience BI Vs Statistical Models

With the worlds of data science, business intelligence and statistical models continually developing and evolving, it is more necessary than ever before to recognize the differences and distinctions these terms possess.

The Definitions

Data Science

- In simplest terms, data science is the gleaming of knowledge and insight from data through the use of a wide variety of techniques and theories from many various fields.

It is best described by DATAVERSITY, ""Data Science combines the allure of Big Data, the fascination of Unstructured Data, the precision of advanced mathematics and statistics, the innovation of social media, the creativity of storytelling, the investigation and inquiry of forensics, and the ability to use all of those skills together while still being able to demonstrate the results to non-technical audiences."

Business Intelligence (BI)

- Business Intelligence encompasses the software, systems and tools that are essential for transforming raw data into a meaningful analysis that businesses can use in the strategic planning process of their organization.

Business Intelligence as described by Forrester is, "A set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery. Research coverage includes executive dashboards as well as query and reporting tools."

Statistical Models

- A statistical model containing a set of assumptions in relation to observed data and additional data.

According to WhatIs, "Statistical analysis is a component of data analytics. In the context of business intelligence (BI), statistical analysis involves collecting and scrutinizing every single data sample in a set of items from which samples can be drawn."

At the core of BI, data science and statistical models is one basic thing, analytics. In addition to this, as analytics is applied, there are also hybrid terms and definitions of these technologies.

From testing unstructured data to building specific applications for such data, analytics plays an essential role. For those who are looking to utilize the myriad of unstructured and semi structured data that can be found throughout the web and the IoT, any variety of business intelligence tools can be used to glean simple analytic insight (even on some Big Data).

The unique science of Business Intelligence that has come to encompass both data science and statistical models (in addition to other things) is continually evolving to provide the best utilization possibilities and the most secure formulations for following a sound strategy, regardless of the organization. As Big Data continues to evolve, the possibilities are essentially unlimited in the uses of these functions.