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

Entify the problem:

Business units specify the need, scope market conditions and goal related to the business question they want to solve, which will lead to the selection of one or more modeling techniques.

Prepare data for analysis:

Depending on the business question and proposed analysis methods, this step involves using specialized techniques to locate, access, clean and prepare the data for optimal results. In our multifaceted data world, that could mean data from transactional systems, unstructured text files and data warehouses.

Explore data:

Now its time to explore the data in an interactive and visual fashion to quickly identify relevant variables, trends and relationships.(The shape of the data when variables are plotted out is called distribution of data. You can use shapes to identify the patterns).

Transform data and create models:

SA skilled analyst or modeler builds the model using statistical, data mining or text mining software, including the critical capability of transforming and selecting key variables. Models need to be built rapidly so modelers can use trial and error to find the model that produces the best results.

Test and validate models:

Once built, he model is registered, tested(or validated), approved and declared ready for use against your data. With a centralized model repository, you can store extensive documentation about the model, scoring code and associated metadata (data about the data) for collaborative sharing and version control necessary for auditing purposes.

Deploy models:

When approved for production use the model is applied to new data to generate predictive insights.

Monitor and assess model:

The predictive performance of the model is monitored to ensure it is up to date and delivering valid results. If the model performance degrades, its time to make changes. When it no longer works or serves a business need, it is retired.

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.