Reflective Essay on Presentations

First Speaker: Andre De Waal/Senor Analytical Consultant, SAS

1- Social Media Analysis

Data analysis from tweeter posts

First Approach:

Download tweets and inserts them into an excel sheet that happened over 13 munities on the topic (real Donald Trump) using SAS website. The results were not what we wanted to get out of it. Our objective was to analyze what Donald Trump tweets, but we got a result of 577 tweets that happened over the last 13 munities of people comments on Donald trumps tweets.

Second Approach:

We used CNN’s website to download Donald trumps tweets over the last three months and inserted them into an Excel sheet. This time we got the results we’re looking for. The tweets were all tweeted by Donald trump. We used sentiment analysis to find out if the responses were positive or negative.

2- SAS Viya “(SAS Viya is a single, cloud-ready environment that will serve everyone – from data scientists to business analysts, application developers to executives – with the reliable, scalable, secure analytics management and governance essential for agile IT.)”

“In the new world, it's not the big fish which eats the small fish, it’s the fast fish which eats the slow fish.”

He used the previous quote to explain that “Firms who are quick to develop and execute an effective marketing technology strategy, regardless of their size, have the opportunity to stand out from competitors who are slower to adapt.”

Second Speaker: Naveen Agarwal: Manager, Business Analytics at J&J Vision Care, Inc.

Navigating the business of big data industry

Opportunities and challenges for professionals in business analytics.

· How to best utilize big data

Big data is noisy: a lot of it is useless

It needs work to be useful

Three Things about big data

1- Is called big data, why?

2- Big data has potential

3- How do we find out where an organization is with big data?

Big data= Volume, variety and velocity

Structured data ex. Sales data, search data

Unstructured data ex. Social media (Facebook, Instagram), YouTube

Big data has big potential, but mixed records of success

The age of analytics

Competing in data-driven world

Data Analytics Maturity

Operation: effective

Reporting and data wherehouse: decision-making

Self service analytics: democratizes of data

Questions to answer Business Analytics

· R&D clinical testing

· Quality control

· Global supply chain

· Sales and Marketing

Statistical Analysis and tools

Descriptive: What happened ex. Summarize past data

Inferential: Why? Ex. Relating sample data

Predictive: What could happen? Ex. Trend analysis

Prescriptive: What should we do? Ex. Data modeling

Traditional Rules in big data industry

Increasing education, experience, and responsibilities.

1- Business Analysts

2- Senior Business Analysts

3- Data Scientists: very advanced

4- Software Engineers