The Internet of Things (IOT) will soon produce a massive volume and variety of data at unprecedented velocity. If "Big Data" is the product of the IOT, "Data Science" is it's soul.

Let's define our terms:

Internet of Things (IOT): equipping all physical and organic things in the world with identifying intelligent devices allowing the near real-time collecting and sharing of data between machines and humans. The IOT era has already begun, albeit in it's first primitive stage.

Data Science: the analysis of data creation. May involve machine learning, algorithm design, computer science, modeling, statistics, analytics, math, artificial intelligence and business strategy.

Big Data: the collection, storage, analysis and distribution/access of large data sets. Usually includes data sets with sizes beyond the ability of standard software tools to capture, curate, manage, and process the data within a tolerable elapsed time. 

We are in the pre-industrial age of data technology and science used to process and understand data. Yet the early evidence provides hope that we can manage and extract knowledge and wisdom from this data to improve life, business and public services at many levels. 

To date, the internet has mostly connected people to information, people to people, and people to business. In the near future, the internet will provide organizations with unprecedented data. The IOT will create an open, global network that connects people, data and machines. 

Billions of machines, products and things from the physical and organic world will merge with the digital world allowing near real-time connectivity and analysis. Machines and products (and every physical and organic thing) embedded with sensors and software - connected to other machines, networked systems, and to humans - allows us to cheaply and automatically collect and share data, analyze it and find valuable meaning. Machines and products in the future will have the intelligence to deliver the right information to the right people (or other intelligent machines and networks), any time, to any device. When smart machines and products can communicate, they help us and other machines understand so we can make better decisions, act fast, save time and money, and improve products and services.

The IOT, Data Science and Big Data will combine to create a revolution in the way organizations use technology and processes to collect, store, analyze and distribute any and all data required to operate optimally, improve products and services, save money and increase revenues. Simply put, welcome to the new information age, where we have the potential to radically improve human life (or create a dystopia - a subject for another time).

The IOT will produce gigantic amounts of data. Yet data alone is useless - it needs to be interpreted and turned into information. However, most information has limited value - it needs to be analyzed and turned into knowledge. Knowledge may have varying degrees of value - but it needs specialized manipulation to transform into valuable, actionable insights. Valuable, actionable knowledge has great value for specific domains and actions - yet requires sophisticated, specialized expertise to be transformed into multi-domain, cross-functional wisdom for game changing strategies and durable competitive advantage.

Big data may provide the operating system and special tools to get actionable value out of data, but the soul of the data, the knowledge and wisdom, is the bailiwick of the data scientist.

See: http://bit.ly/10TgVHG

The hype of "big data" has created a mythical god called the data scientist: a lone-wolf, super-smart human with a solid foundation in computer science, modeling, statistics, analytics, math and strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.

The reality is that very few folks have mastered all those skill sets. As a result, data science is usually performed by teams, with people who have mastered one or some data science skills.

Here is a partial listing of the job titles and skills on Rose Data Science Teams:

Business architects: Team leaders. Strong business acumen and ability to communicate with senior business leaders and data scientists. Develops the architecture for information management and integrating data science and evidence based decision making. A change agent who has great persuasion skills to get the organization - at all levels - to use data science to make better decisions. They see the big picture, have business strategy talents and technology know-how. Business architects have a rare combination of business knowledge, process experience, transformation talents, methodology skills, and a winning personality that helps with communication and business change management.

Data scientists: The top dogs in big data. Many of these folks have backgrounds in math or traditional statistics. Some have experience or degrees in machine learning, artificial intelligence, natural language processing or data management. Others are strong in the computer sciences with experience in high performance computing architectures, data mining and designing algorithms. Some are innovative modelers with strong business acumen.

Data architects: Programmers who are good at working with messy data, disparate types ofdata, undefined data and lots of ambiguity. They may be people with traditional programming or business intelligence backgrounds, and they're often familiar with statistics. They need the creativity and persistence to be able to harness data in new ways to create new insights.

Data visualizers: Technologists who translate analytics into information a business can use. They harness the data and put it in context, in layman's language, exploring what the datameans and how it will impact the company. They need to be able to understand and communicate with all parts of the business, including C-level executives.

Data change agents: People who drive changes in internal operations and processes based ondata analytics. They may come from a Six Sigma background, but they also need the communication skills to translate jargon into terms others can understand.

Data engineers/operators: The designers, builders and managers of the big data infrastructure. They develop the architecture that helps analyze and process data in the way the business needs it. And they make sure those systems are performing smoothly.

In addition, data scientists are part of a bigger team in any organization. This includes business and IT leaders, middle management and front-line employees. The goal is to use data science to help the organization turn data into information - information into knowledge and insights - and valuable, actionable insights into better decision making and game changing strategies.

In my opinion, the best data scientists do not have the strongest technical skills. Rather, they are team players: professionals who love to play with data, spot trends and learn truths few others know. The very best data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Most importantly, they have strong communication skills to help business leaders - and all members of an organization - apply data science and analytic results to critical business issues.

I predict that data scientists who acquire business architecture skills - or business architects who master data science skills - will become the best business and political leaders of the future.  

See: http://bit.ly/10TgVHG

Cloud Infrastructure Providers - also known as Infrastructure as a Service (IaaS).

Big Data Survey of 257 business technology professionals at organizations with 50 or more employees, September 2012.

Fast Data applications are typically compute intensive and run on High Performance Computing Architectures. This video explores and unlocks the need for speed in Fast Data. It examines the properties that define Fast Data, analyzes the challenges, and then takes a closer look at the role Flash technologies play in delivering the performance required by applications using Fast Data.

Dimensional Research conducted a survey of business and IT leaders on the drivers of cloud adoption. Key findings include:

• Cloud adoption is driven by multiple factors

- CIOs report a wide range of reasons for adopting cloud applications, including compliance requirements 
(58%), better value (53%), and competitive advantage (51%)
- Business executives cite one key driver for choosing cloud applications, better value (80%)

• Employees like using cloud applications

- 79% of all participants report that employees believe experience with cloud applications is beneficial 
- 95% of CIOs say that IT employees want to gain expertise with cloud applications
- 83% of CIOs have no problem finding technical help for cloud applications 
  
• Outdated on-premise software is common

- 61% report critical applications that have not been updated recently 
- 14% have business critical software that has not been updated in over four years
- 28% of CIOs with current maintenance requirements for business-critical software lack confidence that they 
are in compliance

This talk shows you how to deploy and manage your Hadoop cluster on any Cloud, as well as manage the rest of your big data application stack using a new open source framework called Cloudify.