All about Big Data

Big Data is massive. This article will explore the issues of big data: what it is, how it will improve decision - making, and how to use it correctly.
1) What's the big deal about big data? Big data is all about three "V's" Velocity, Volume and Variety.
2) How it will improve decision?
# Deliver customer insights.
# Deliver targeted communication.
# optimize performance.
3) How to use it correctly?
# Collect data.
# Clean Data.
# Merge Datasets.
# Analyze Data.
To learn more about big data follow the article by Angela Hausman (PhD) at: http://www.business2community.com/big-data/big-data-like-teenage-sex-01458960

Importance of Design Thinking for Data and Analytics

Design thinking is at the top of mind for business teams of big giants as well as startups. The traditional "If you build it, they will come," mentality has been taken from techniques like customer journey mapping and empathy-driven prototyping. Many companies are unsure how to implement it to improve their business - especially in areas like data analytics and decision sciences. The first step is to ask:  for whom are we designing and what is the problem they are experiencing? The second: to what end are we modeling the design - to boost consumption and engagement, improve performance, or to achieve scale? These same needs to be asked at the outset of any analytics effort. Here are five simple steps that are key to infusing analytics with a designer mindset.
1) Create a design framework that allows you to fail fast.
2) Empathize with your customer to impart emotion into your product.
3) Focus on problem-solving that allows for rapid experimentation.
4) Employ methods to inspire creative brainstorming across teams.
5) To design the killer solution, let nature be your guide.
To read more visit at: http://www.datanami.com/2016/02/16/what-design-thinking-means-for-data-analytics/

Is Big Data Changing Disruptive Innovation

Despite the many differences in application, most people agree on that the disruptive innovation are: 1. Cheaper 

2. More accessible 

3. And use a business model with structural cost advantages. 

Due to the presence of these characteristics in disruption, it's difficult for an existing business to respond to competition. To read more above disruptive innovation follow: https://hbr.org/2016/01/how-big-data-is-changing-disruptive-innovation

Difference between Hadoop and Apache Spark

Hadoop and Apache Spark are seen as the competitors in the world of big data, but now the growing consensus is that they are better convention in together. Here is a brief look at what they do and how they are compared.  1. They do different things: Both are the big-data frameworks, but they do not serve the same purposes. Hadoop is a distributed data infrastructure. It also Indexes and keep track of that data, enabling big-data processing and analytics. On the other hand, Spark is a data processing tool. Secondly, both can be used individually, without the other. 3. Spark is faster 4. You may not need Spark's speed: Spark is fit for real-time marketing campaigns, online product recommendations, cybersecurity analytics and machine log monitoring. 5. Failure recovery: differently, but still good. Read more at: http://www.computerworld.com/article/3014516/big-data/5-things-to-know-about-hadoop-v-apache-spark.html