The motive of capital good policy is noble, but not its implementation. The policy fails to address the basic problem of lack of research to improve manufacturing units. Thus the policy fails to expand the share of manufacturing in GDP. As India is rapidly globalizing, it needs to improve its manufacturing process using better machinery, newer intelligence and better quality raw materials. Automated assembly lines and lean production techniques must be put in use. Similarly, artificial intelligence and bid-data analytics will help the Indian companies improve their productivity and increase profits. India should gear up for advanced manufacturing in areas such as aircraft manufacture, materials, microelectronics and telecommunications. Most importantly, India needs to develop a knowledge ecosystem that includes a good intellectual property regime to foster innovation and creativity. Read more at: http://blogs.economictimes.indiatimes.com/et-editorials/capital-goods-policy-contains-little-meat/

Today companies are gathering more and more data but are not certain of what to do with this data. For the last five years, companies are getting in to the big data practices in order to get competitive advantage. Big data is basically using analytical software to visualize unstructured data (data available to the public) and structured data (companies propriety information) .This visualization does wonders, it gives predictive sales figures, future prospective of the company and sales target .Gathering information is getting simpler with the emerging software & IoT; but sorting and filtering the huge pile of data is a complicated process.

According to a new report, an analyst spends about 50-90% of their time in just cleaning and filtering the raw data. Companies can solve this problem by improving the data software where data can have lower level of risk for data scientist and algorithms can help in data cleaning.

To know more: http://www.zdnet.com/article/big-datas-biggest-problem-its-too-hard-to-get-the-data-in/

Big data have existed since the time of the cave man existed in physical paper and other analog forms (stone, papyrus, tape) and was largely inaccessible. With the inception of Commercial Internet & new forms of commerce and social media, we can access information of "open source". Now technology can connect streaming and near real-time data to generate information very fast. Dynamic machine-learning algorithms can also help to discover hidden patterns and associations and graph databases has the ability to query and explore entities and their relationships in order to understand how opponent operates. It was also found that tools for geospatial analysis can help analysts to understand how events and groupings are related in space and time. To know more, please read the article by Mark Testoni (Writer at Homeland security today.us) -: http://www.hstoday.us/columns/critical-issues-in-national-cybersecurity/blog/big-data-analytics-from-forensics-to-foresight/d0c045fe32568eedefda4195d46472e6.html

This paper gives us a holistic understanding of two main topics – big data analytic & supply chain management. In this highly competitive world, every organization is looking forward to a competitive advantage in the form either cost effectiveness or better utilization of resources. To achieve this goal organizations are using a data driven supply chain to integrate the entire processes. This enables organization to stream line supply chain while mitigating the risk in an uncertain business environment. Big data analytic also helps an organization to extract important trends & information that can be used to make the systems more efficient & robust.

To read the full article click:- http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2755828

Retailers and most of the brick and mortar shops store their customer's data to understand their buying and transaction behavior. Some obscure data point that may help them become more knowledgeable about their customers. However, hidden from these in-store shopper analytics are all of the transactions their customers make outside their stores with competitors. In recent past, there are many switching of preference to online one. But this up growing online marketing basically picking the flaws of customers' real time analysis in case of brick and mortar shops. So it's high time to either integrate or dig into flawless marketing analytics.

To read, follow: http://www.forbes.com/sites/forbesinsights/2016/04/27/the-hidden-danger-in-brick-and-mortar-customer-files-amazon-prime-members/#69beabd83681

Big data analytics is becoming self-explanatory day by day to even a lame person who has lesser knowledge in statistics, analytics and data science. It is a positive sign with respect to current business purpose because we need real time analysis and that can be only possible if data interpretation and visualization becomes more feasible. There are different software like R, SAS and many other online service providers like google analytics, Piwik helps to create better data visualization and interpretation of customer and business data. Even our very own Microsoft excel has some in built data interpretation and visualization programs. But one program, called Quill, takes the trend a step further, producing text-based reports that explain the data clearly and concisely.  Think of it as an executive summary created by a computer to explain a set of data at the click of a button. So it’s time for personalize big data analytics.

to read, follow: http://www.forbes.com/sites/bernardmarr/2016/04/27/will-we-soon-no-longer-need-data-scientists/#540a99bf55f3

NASA is working on big data analytics to facilitate their mission mars more productive and informative. They are using big data analytics to understand the environment of the mars. That is they are using modern technology to collect and analyze environmental data and based on that it will give a predefined signal to the Curiosity rover currently operating in Mars. These data points help to generate a more specific road map for upcoming 2020 mars mission for NASA. One of The expert in NASA JPL laboratory says these huge analytic modelling defines an easier way to resolve the problem. 

To read, follow: http://www.forbes.com/sites/bernardmarr/2016/04/14/amazing-big-data-at-nasa-real-time-analytics-150-million-miles-from-earth/#1cbda1b371dd

In order to make management decisions, all companies in various industries work on large transactional data. For example: healthcare, retail, pharmaceuticals and so on. Some of the benefits big date provides:-

1. Big data analytic makes operations more efficient and frequent.

2. It appreciates the quality of services by analyzing the whole data and not just sample subset. As a result, the companies make more precise decisions.

3. Marketers target the right customers using big data. Through focusing on data based strategies

4. It gives an opportunity to new companies because they can analyze the existing industry data.

5. It also gives a competitive edge to those companies who use it decision making.

To read more: http://timesofindia.indiatimes.com/tech/it-services/How-Big-Data-is-changing-the-way-we-do-business/articleshow/50417589.cms

A recent study by MIT University shows that there is a 78% increase over the past three years in the job posting related to analytics and this led universities to launch specialized program in Master in Business Analytics. Nowadays, data analytics are used in marketing, finance, health care, logistics, insurance ecommerce, manufacturing and government services. To know more, please read the article by Michael Norton (Writer at lowellsun.com)-: http://www.lowellsun.com/business/ci_29635724/mit-reports-spike-analytics-job-postings

In the aviation industry, aircraft maintenance and safety is always on high priority. With the increase in air traffic and air passengers, aircraft maintenance and repair firms are utilizing predictive analytics integrated with the Internet of Things (IoT) to predict component failures and pilot stress level. Firms are developing a solution that can predict the likelihood of a component failure which can help to replace the part before it fails. In future, by analyzing breathing patterns of pilots before they board the flight, will help to monitor the stress level. To know more, please read the article by Jonathan Ananda (Writer at The New Indian Express) -: http://www.newindianexpress.com/business/news/Data-Analytics-to-Make-Flying-Safe-for-Pilots-and-Passengers/2016/03/16/article3329532.ece

Image Source - : https://pixabay.com/en/oldtimer-aircraft-take-off-aviation-1010805/

Big data analytics are helping organizations connect their data and use it to identify new opportunities. Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. Here are some sectors where big data are making an impressive impact-: 

# 1. Healthcare 

Big Data Analytics in the healthcare sector helps patients and the healthcare ecosystem to be aware of the appropriate treatments and measures to be taken.

#2. Retail

In the retail sector, analytic solution offers insights to understand and respond to changing customer experience.

#3. Banking

The financial sector has to deal with vast volumes of data. This sector poses higher vulnerabilities to fraud and financial crimes.

#4. Manufacturing

In manufacturing, operations managers can use advanced analytics to dig historical data to study and improve production.

To know more, please read the article by Arya MM (Writer at infotechlead) -: http://www.infotechlead.com/big-data-2/biz-booms-investment-big-data-analytics-38700

Chemical industry deals with lots of data sets. Some of these data are relevant to product development and the rest of them mostly helps to identify external parameters governing the production process. Generally, big data help to facilitate cost cutting issues. It brings a new product to the market and improves industry environment. Intellectual sensors are integrated with the research and material related informatics. This process minimizes the time required for new innovation. Sensitive sensors can also help to predict the upcoming situation by measuring current situations. Big data works in the back end. This ensures worker's safety by analyzing the toxic level, heart rate, etc. Intelligent sensors diagnose the past chemical data and all possible combinations to predict the new combination. It smoothens the R&D process. Therefore, automation, safety guide and intelligent forecasting are the key mantra for success in the chemical industry.

 To read, follow:  https://www.environmentalleader.com/2016/02/23/how-big-data-is-changing-chemical-manufacturing/ 

Big data analytics can analyze the past behavioral data of humans. It is highly correlated to future outcomes.  The most important task is to understand how much emotional extent can be predicted accurately. By analyzing critical reasoning and judgmental decision, there will be higher chances to predict more efficient human behavior. Social media plays an important part by providing more sensitive data having higher emotional quotient. Search dynamics also help to predict future trends. Therefore, combining big data, transaction data and social data along with shopping behavioral data opens a new horizon which enables marketers to influence human behaviors and perceptions. To read, follow: : http://www.forbes.com/sites/forbesinsights/2016/02/02/has-big-data-taken-the-human-out-of-human-behavior/#2280493b170c 

Big data analytics plays an important role in supply chain management. 97% of supply chain executives have reported how big data analytics helped them to grow their business and only 17% of any particular industry have implemented this process. This process generates higher visibility and deeper insight to the customer behaviour and demand supply scenario. It also helps to discover and manage supplier relationships more effectively. Big data help to understand customer needs and make a 360 degree analysis regarding marketing channel, segmentation and acceptability. Predictability helps to create more efficient supply chain progress (increased ~10%). It identifies supply chain risk by considering the previous demand, supply scenario almost accurately. Supply Chain Traceability and recalls are data-intensive and highly correlated to supply chain risk. The ability to quickly meet customer fulfilment is an important driver. It helps in competitive advantage across all industries which can be achieved by big data analysis.

To read, follow: http://www.computerworld.com/article/3035144/data-center/overcoming-5-major-supply-chain-challenges-with-big-data-analytics.html

Investment in shares requires analysis of huge historical data. Analysis is the primary phase and it forecasts the future investment process. Broking house plays a clinical role in this context and charges a percentage of hike in shares. But these days such information is frequently available in different websites and are updated on a regular basis. The usage of technology for predictive analysis is hugely correlated with big data. These are limited to institutional buyers. The automation in predictive analysis requires a huge precision which is cost effective, but its implementation could be a game changer.

To read, follow: http://www.thehindubusinessline.com/markets/stock-markets/big-data-robo-analytics-to-drive-next-phase-of-growth/article8249330.ece

Any decision pertaining to designing products, environments and brand experience is closely connected to the use of colour because it depends on the buying behaviour of customers, brand perception, strategic differentiation, and user experience. But availability of information related to colour perception is limited. Organizations are using big data to analyse colour preference and perception which contains over a 100 years of data. They divided their study into four categories regarding colour data, analytic and insight:

1. Colour competitive intelligence is relevant for aggressive in brand competition.

2. Colour legal intelligence helps to identify the colour norms available to different countries.

3. Colour Research Intelligence collates colour studies that have been conducted over the past century, world-wide, about the industry and product segments.

4. Colour listening intelligence signifies how much buzz about a particular colour is popular in the market.

These all studies benefit other companies by creating their own colour mix to attract new customers, branding and dictate consumer preference trends.

To read, follow: http://www.forbes.com/sites/michellegreenwald/2016/02/17/a-new-comprehensive-4-in-1-big-data-resource-to-aid-color-innovation/#6e729df04012

Big Data analytics is coming up in a big way. According to Forrester Research, companies estimate that they are analyzing only 12% of the data at their disposal. The demand for predictive analysis combined with the small number of data scientists explains why 88% of the data captured by businesses fail to produce actionable insights. So, automation in Big-Data Analysis comes to the rescue. Benefits of Big Data Analysis are - Automation supports existing staff, Automation reduces the need for additional staff and extensive training, Automation saves time and money. Read more at:  http://it.toolbox.com/blogs/byod-cloud/the-benefits-of-automating-big-data-analysis-70335

Internet of Things is evolving. Effective IoT requires software modifications, network communication support changes, platform shifts. The missing key is the data infrastructure to support IoT. Big data analytics is an advantage in today’s world, but it is essential for the coming IoT revolution. Big data can help IoT initiatives in many ways. To know more, follow:  http://it.toolbox.com/blogs/itmanagement/five-ways-big-data-can-improve-your-iot-initiative-69958

By making use of excess of data available and information and communication technologies (ICT), smart cities can now address the various economic, spatial, social and ecological problems. Although data analytics forms the core of business strategies, yet very little is known about new business models within smart cities and urban living. Recent advances in Data Analytics have shown promise than city data analysis and its use can generate value for municipalities, companies and individuals. From relatively simpler analytics techniques to more sophisticated one, the available tools can adopted to obtain the desired results. For example, when the enormous volume of the data poses serious challenge, clustering technique can make the dataset manageable. Read more:- https://icrunchdatanews.com/smart-data-analytics-smart-cities/

Big Data Analytics has started to make significant strides in less explored areas like improving manufacturing performance. No wonder the theme of ARC European Forum, 2015 was intended to help develop some clarity over emerging related concepts such as Industrie 4.0 and the Industrial Internet of Things (IIoT). Several use cases about how "applying these concepts can deliver tangible benefits in real-world industrial production across a broad cross-section of industrial sectors" were presented. Read More at: http://www.automationworld.com/industrial-internet-things/big-data-analytics-improve-manufacturing-performance