3D Printing for Dummies

What is 3D printing: As per Wikipedia, "3D printing or Additive manufacturing is a process of making a three-dimensional solid object of virtually any shape from a digital model." It uses an additive process, where successive layers of material are laid down by a 3D printer in different shapes, based on a computer-generated 3D model of an object.

Why is it so useful: Wikipedia points out that 3D printing is useful right "from pre-production (i.e. rapid prototyping) to full-scale production (i.e. rapid manufacturing), in addition to tooling applications and post-production customization." Unlike traditional subtractive methods of manufacturing, there is less material wastage in case of 3D printing.

How is it important: Mark Fleming claims that "Medicine will forever be changed as new bioprinters actually print human tissue for both pharmaceutical testing and eventually entire organs and bones." Also, he says, "experimental, massive 3D printers are printing concrete structures, with the goal of someday creating entire buildings with a 3D printer."

Who uses 3D printing: TechRepublic reports that "A growing number of innovative companies are experimenting with 3D printers, propelling the technology closer to the mainstream market." These include major companies like General Electric, Boeing, Ford, Nike, Hasbro and Hershey's. Many hobbyists also use personal 3D printers in their homes.

Where to from here: As 3D printers become less expensive, more companies and even individuals will start adopting it. As Fleming puts it, 3D printing "is a disruptive technology of mammoth proportions, with effects on energy use, waste, customization, product availability, art, medicine, construction, the sciences, and of course manufacturing."

Big Data for Dummies

What is Big Data: As per Wikipedia, "Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization". In 2001, industry analyst Doug Laney defined Big Data in terms of 3 Vs: high Volume, high Velocity and high Variety. Business Analytics firm SAS considers two additional factors: high Variability and high Complexity.

Where does Big Data Come From: Wikipedia says, "Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks." According to McKinsey, "The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future."

Who works with Big Data: Again from Wikipedia, "Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research. The limitations also affect Internet search, finance and business informatics." Another category of Big Data users are law enforcement agencies across the world who have to work with multiple local databases containing different categories of information about various criminals and terrorists.

Why Big Data Matters: McKinsey maintains that "From the standpoint of competitiveness and the potential capture of value, all companies need to take big data seriously." SAS believes that organizations can analyze Big Data "to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smarter business decision making". They have given the example of UPS who started analyzing Big Data in the 1980s, long before the term was coined, and used it to achieve major savings.

How to work with Big Data: Since Big Data can't be processed by traditional database management tools or data processing applications, a new set of applications have evolved to specifically deal with Big Data. Some examples are Hadoop (by the Apache Foundation), MongoDB and Splunk. From a hardware point of view, practitioners of Big Data analytics prefer the faster and cheaper Direct-attached storage (DAS) over the traditional shared storage architectures such as Storage area network (SAN) and Network-attached storage (NAS).

Whether Big Data matters to You: Some companies claim that Big Data analysis is the hottest new practice in the field of BI today. McKinsey predicts that "By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions." Clearly, if you an IT employee at a technical or lower-to-middle management level, knowledge of Big Data analytics could help you in the future.