Data Analytics

Analytics is the scientific process of discovering and communicating the meaningful patterns hich can be found in data. It is concerned with turning raw data into insight for making better decisions. Analytics relies on the application of statistics computer programming and operations research in order to quantify and gain insight to the meanings of data. It is especially useful in areas which record a lot of data or information.


Analytics is an encompassing and multidimensional field that uses mathematics, statistics, predictive modeling and machine learning techniques to find meaningful patterns and knowledge in recorded data.


Why is analytics important?

This can change how we look at the world and usually for the better. Some times we think that a process is already working at its best but sometimes data tells us otherwise so analytics helps us to improve our world. In the world organizations would usually apply nalytics in order to describe, predict and then improve the performance of the organizations. Specifically it would help in the following areas: Web analytics, Fraud analysis, Risk analysis, Advertisement and marketing, Enterprise Decision management, Market optimization, Market modelling.


Life cycle of analyticals

Advanced Analytics

DC research shows SAS with a commanding 30.8 % market share in advanced analytics more than twice that of our nearest competitor.We dominate the market because we know it's not just how advanced the technology is that matters.it's how far it can advance our organization.


1. Data Mining:

Want to know what will happen in the future? Find the most lucrative opportunities? Get insights into impending outcomes.


a)SAS Econometrics

Analyze complex business and economic scenarios, providing a scientific basis for better decision making.


b)SAS Forecast Server

Produce large numbers of forecasts quickly and automatically to improve planning and decision making.


c)SAS Visual Forecasting

Generate large numbers of reliable forecasts quickly and automatically in an open environment.

2.Statistical Analysis:

Whether you're analysing customer data, runching sales numbers, monitoring supply chain operations or trying to detect fraud, apply powerful statistical analysis to all your data to get the most accurate answers.

a) SAS Visual Statistics

Create and modify predictive models faster than ever using a visual interface and in memory processing.

b)SAS® In-Memory Statistics

Find insights in big data with a single environment that moves you quickly through each phase of the analytical life cycle.

c) SAS® Analytics Pro

Access, manipulate, analyse and present information with a comprehensive analytical toolset that combines statistical analysis, reporting and highimpact visuals.

3.Forecasting:

Generate large quantities of high quality forecasts quickly and automatically no need for human intervention. And streamline your forecasting processes so you can focus efforts on the most important, high value decisions.

a)SAS Econometrics

Analyze complex business and economic scenarios, providing a scientific basis for better decision making.

b)SAS Visual Forecasting

Generate large numbers of reliable forecasts quickly and automatically in an open environment.

4.Optimization & Simulation:

Identify the scenarios that will produce the best outcomes.

a)SAS/OR

Optimize business processes and address challenges with enhanced operations research methods.

b)SAS® Optimization

Find optimal solutions to complex business and planning problems faster than ever.

c)SAS Simulation Studio

Build models that mimic complex, real-life systems so you can better understand and optimize them using discrete event simulation.