Natural disasters unlike other man-made disasters are the most terrifying events in the world since they cannot be controlled. However, by using the power of big data, it is possible to help in disaster management. For this purpose big data can be used through crowdsourcing which can be achieved by the increasing use of social- networking in the present days. In case of earthquakes, instead of using dedicated sensors which are highly expensive one can use the almost similar sensors in smartphones through Wi-Fi-hotspot and GPS to collect data to create an overall picture. For this, infrastructure needs to be set up so that information can be uploaded from the affected areas so that the affected people can be tracked down. Moreover the maps created through crowdsourced collaboration helps to optimize the recovery process. Read more at :http://channels.theinnovationenterprise.com/articles/big-data-in-a-crisis

Vendors often focus on showcasing their front-end capabilities, i.e. dashboard reporting and data visualizations, while ignoring the vital aspect of analytics, namely data preparation: cleansing, structuring and integrating data to make it ready for analysis. The typical scenarios include using more than one type of data source, working with large datasets, working with messy, unorganized data. This is where your business intelligence tools come in. These tools are meant to automate or simplify the bulk of the data preparation process by using pre-programmed adapters that connect into different types of data sources, and restructuring the data into a single centralized repository. Here are 3 crucial aspects of data preparation one should be aware of when evaluating business intelligence software: Access to the original data, joining multiple data sources and data management. Choosing the wrong software could skew your initial price estimate when you are forced to allocate technical resources or purchase additional programs to handle data preparation.

Read more at:

http://www.sisense.com/blog/data-preparation-checking-hood-analytics-software/

78% of the companies have dedicated social media teams and only 26% of the companies have social media as the part of their marketing strategy. This shows that, they don't recognize the value of social media marketing and they don't trust social media data for decision making. Companies using social media for marketing or promotion mainly use two strategies for monitoring:

• Restrict to hash tag mentions: A strategy that leads to high precision at the cost of many missed mentions.
• Unrestricted keyword search: An approach that could generate numerous false positives.

But these strategies lead to false results. Now the question arises how to monitor social media accurately?

Rohini Srihari (Chief Scientist at SmartFocus and a contributor to Econsultancy) in her article “how reliable are social analytics?” talked about several ways for monitoring social media accurately. Some of them are:

• For comparison across brands and different content sources, you should consider the various features like share of voice, sentiments, sudden spikes etc.
• Sentimental analysis is best for analyzing trends like change in public perception.
• For location based analytics, a researcher should ensure that a sufficient number of samples have been obtained.

To know more follow this link: https://econsultancy.com/blog/66466-how-reliable-are-social-analytics/

The Retail industry is very competitive. Retailers need to apply analytics to analyze consumer behavior and retain them. Predictive Analytics help retailers to predict the response of customers regarding new offer, discount or product. But barrier of culture and stage fright, stop them to apply big data analytics.

Leslie Dinham (Teredata) in her article "two ways retailers are overcoming barriers to analytics adoption," talks about solutions to these barriers or adoption blockers. They are:

Barrier 1# Culture is the culprit: Employees get rigid due to working in the same culture, performing same job or duties. They don’t want to change their decision making process and roles. It becomes difficult to apply data analytics in this culture. The solution to this problem could be informing employee about the benefits of using data analytics and provide necessary training.

Barrier 2# Stage Fright: Many times, retailers won’t get success while applying analytics in their organization because they won’t able to choose the right team, tool or technology, won’t able to integrate new analytical capabilities into operations or the culture of the organization is not innovative. Paying attention while applying analytics in these things can help organizations to successfully apply analytics.

To know more about these barriers and solution to them, read an article at: http://www.forbes.com/sites/teradata/2015/05/13/two-ways-retailers-are-overcoming-barriers-to-analytics-adoption/

“Data is the new oil”

This comparison of big data with oil has always been there, ever since big data came into limelight. It is considered that like oil, the more you extract from big data, the more you benefit.

Now look at this new statement:

“Data is the new soil”

This statement reflects the growth in the field of big data. From being used only for extracting information, it is now being used to explore new avenues. Big Data is now being used as a raw material from which new ideas can be generated and further processed into new products and services. Many examples of this were given at Sapphire Now, SAP’s annual user conference, where innovators demonstrated various fields in which they have started using big data sets to create unique products. Some of them are:

• Handle the short and medium term challenges that climate change creates
• Help “local spaces” understand what mobile customers want
• Provide shoppers with a contextual in-store experience
• Help companies create solutions and discover things like energy and profit leaks, make predictable promotions based on clustered buyer preferences

Thus, big data is now providing a new range of solutions to make our lives easier as well as better. To know more, read the following article by Virginia Backaitis, Senior Partner at Brilliant Leap, at cmswire.com:

http://www.cmswire.com/cms/big-data/is-data-the-new-soil-sapphirenow-029124.php

In today's present scenario, retailers are facing threat from online stores. There is a fall in profit percentage. But to deal with this, retailers are increasing their customer's database as they can apply analytics on the data, predict and track customer behavior.

 In this context, the Future group's plan is to increase the database of customers so that they can fight ecommerce companies.

According to Punit Soni (CPO at online marketplace Flipkart), “Capturing a huge swath of pricing and things of the largest economies of the world, and becoming the default marketplace is not easily doable for offline players”.

To know more about Future Group strategy and analytics in retail, read an article link “Future Group banking on analytics to battle e-commerce companies” by Jayadevan PK (ET Bureau): http://articles.economictimes.indiatimes.com/2015-05-08/news/61947503_1_rakesh-biyani-future-group-data-analytics

The retail industry is B2C industry. In B2C industry, forecasting and planning future demand and supply is a very important function to improve operation's efficiency. But, consumer behavior is very unpredictable. To analyze this unpredictable behavior, retail stores need to analyze big data. In Consumer Goods Analytics Summit in Chicago, suggestions on applying Big Data Analytics in Retail Industry were discussed. Let’s have a look on some of them:

·        By using big data analytics try to find out actual problem and their solution.

·        Apply analytics in every possible way from making sales report to multi-structured data to understand and improve customer service.

·        Always Interpret big data.

·        Recruit persons who understand the value of data analytics. 

To know more about Big Data Analytics in Retail, read the article link “Are retailers organized for Analytics” by Gib Basset, (Consumer Goods and Retail Industry Principal with Oracle Corp) at: http://www.retailwire.com/news-article/18266/are-retailers-organized-for-analytics

The Global Food System comprises of a number of stakeholders as well as data - consumers, producers, economics, trade agreements, financial transactions, demand data, supply data, forecasting models, climatology, large and small-scale farms, politics, distribution systems etc. How do all of these correlate in a useful manner and show results? This is not possible with traditional scientific methodologies and technologies as there is a robust volume of complex data available. Rather, there’s a need for Big Data Analytics that will help in following areas:

• Measurement of poverty and hunger levels
• Improve aspects of how we feed and eat
• Food policy actions, etc.

Therefore, we need to invest in larger data warehouses which will provide the backbone for big data analysis of local, regional, national and ultimately, the global food system.

To know more, please read the following article by Hari Pulapaka, Executive Chef and Co-Owner, Cress Restaurant, at The Hufffington Post:

http://www.huffingtonpost.com/hari-pulapaka-phd-cec/big-data-analytics-the-gl_b_7216378.html?ir=India&adsSiteOverride=in

“Predictive analytics have the potential power to produce remarkable services and longer lives”

-James Heskett

All of us must have heard of uses of predictive analytics in marketing i.e. it helps to understand the needs of the customer. But, have you ever wondered that the scope of predictive analytics can be much more than that.

“Predictive analysis applied to humans is now one of the hottest concepts to come along.” It can now be used in the following situations:

• Development of concepts such as 30-minute package delivery
• Big Data analysis of target customer's and others' purchases, combined with related information to identify their needs even before they arise
• Personalized logistics

These situations seem to be amazing but turning them into reality is what that needs to be done. To know more, please read the following article by James Heskett at HBS Working Knowledge:

http://hbswk.hbs.edu/item/7527.html

The hype around the word “big data” is ever increasing. It promises to bring a big revolution in marketing. But, in all this hype, myths also arise, which need to be cleared.

Joerg Niessing,INSEAD Affiliate Professor of Marketing, and James Walker, Partner Demand Analytics, Strategy&, in their article at knowledge.insead.edu, talk about eight commonly heard myths on big data. Some of them are:

• It’s big
• The more granular the data, the better
• Big Data is good data
• Big Data is a magic 8-ball

These myths need to be discarded before putting into use “the real Big Data”. To know more, please visit the following link:

http://knowledge.insead.edu/blog/insead-blog/the-eight-most-common-big-data-myths-3878

Managing large volumes of heterogeneous data and using it effectively has always been a problem question in the legal domain. The solution to this big question has now been obtained with the advent of big data. Legal Informatics, a field which has emerged from big data, ties together work in the representation of legal knowledge with the performance gains derived through distributed processing.

Many questions arise in the minds of lawyers such as:

• How does the Judge rule on certain types of cases can be studied by date and time?
• Does the judge dismiss cases for a consistent pattern of reasoning?
• How do holidays affect decisions?
• Do they sentence harder at different times of the day?

These questions can now be easily answered with the help of Legal Informatics.

But, like all things have two sides, use of big data analytics in legal domain also has its repercussions like routine tasks will now be easily undertaken by analytics, judges will come under increased pressure, etc.

To know more, read the following article by Robert Plant, Associate Professor at the School of Business Administration, University of Miami, at The Wall Street Journal.

http://blogs.wsj.com/experts/2015/04/24/what-big-data-means-for-the-legal-system/

According to Burton White (Vice President of the Industry Supply Chains at Chainalytics), for retailers and e-commerce firms, developing an effective and efficient supply chain strategy is challenging. To make last-mile delivery as their core supply chain strategy, they have to provide right inventory at the right time at the right place in the right form.
Some tips to be considered while developing a supply chain strategy are:

• Never lose sight of what actually matters to the customer
• Explore innovative approaches, like to bundle product shipments.
• Explore non-traditional distribution capabilities.
• Optimize transportation solutions to meet last-mile demands.
• Consider the inventory’s form, function and placement within your supply chain.
• Focus on returns management efficiency.

Read more at: http://www.industryweek.com/last-mile

A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. According to Gartner, the advantage of Data lakes is: helps in addressing the old and new problem by providing the relevant set of data for analyzing the situation. Disadvantages are:

• Lack of data quality.
• Security and access control.
• Data Lake requires proper infrastructure.

But using purpose built cloud systems security, access control and scalability problem can be solved, but data quality is not good.
 To know more about Data Lake and its advantage and disadvantages, read an article Data Lakes: Emerging Pros and Cons by Joe Panettieri. Link: http://www.information-management.com/news/Big-Data-Lakes-Cloud-Computing-Analytics-10026889-1.html

For any business, demand forecasting is an important function. Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase in near future. It helps you to order inventory and arrange staff for fulfilling customers need.

According to Peter Daisyme (Co-founder of Hostt), some ways to make demand forecasting more accurate are:

• Use the right set of data for making decisions.
• Consider the variables like the seasonal trend, random trend, economic conditions, etc.
• Know your customers and business.
• Each year, you should refine your demand forecasting technique.
Read more at: http://www.entrepreneur.com/article/244823

According to Sujan Patel (Contributor), companies before analyzing big data, must understand the company's goals and mission. In a survey by Price Waterhouse Cooper, only 44% of companies feel that they have the right talent to capitalize big data. When any company chooses tool for data analytics, focus should be on team needs and solution and the team must know how to use that tool. Read more at: http://www.forbes.com/sites/sujanpatel/2015/04/22/how-fortune-500-companies-are-building-big-data-teams-and-how-startups-can-too/

According to Rick Frascona (senior content manager with MadValorem), digital marketing is one of the parts of real estate marketing strategy. Event-driven marketing and predictive analytics can lower costs of direct mail marketing. A Real estate agent can find customers who have a maximum probability of buying or selling houses with the help of predictive analytics.  According to the National Association of Realtors, 92% of homebuyers in 2014 used the internet to search for homes. Read more at: http://www.inman.com/2015/04/21/can-predictive-analytics-breathe-new-life-into-direct-mail-marketing/

In today’s world, data is limitless. But, this Big Data, will help you achieve your targets, if we use it efficiently. Otherwise, you’ll be trapped in a web of data. For this, you need to do a gap analysis i.e. what data you have today and what data would you like to have.

To answer these questions, this article by Mary C. Long, Chief Ghost at Digital Media Ghost, suggests three tracks on which your business can move forward on:

1. Get a handle on your current data
2. Vet potential data sources and set a realistic budget
3. Add ONE new data source to your client insight capabilities

To know more, visit the following link on cmswire.com:

http://www.cmswire.com/cms/customer-experience/dont-let-big-data-keep-you-up-at-night-028895.php

Big data represents a major change in the way businesses and other organizations operate and will require a new mind-set and capabilities. Extracting business value from big data seems to be a complicated task. But, it can be easily accomplished, if done in a planned manner. Following article reports nine steps, given by HCL Technologies CEO, Anant Gupta, in the World Economic Forum’s Information Technology Report, to help organizations overcome the barriers in transition to Big Data and minimize the difficulties that may occur along the way:

http://www.information-age.com/it-management/strategy-and-innovation/123458030/9-steps-realising-benefits-big-datas-promise

According to a survey, the Indian fashion industry is likely to touch $77 billion in a next five year. To understand the changing customer behavior and have a competitive advantage, e-commerce firms and retailers can use analytics. In a research from Technopak, the share of apparels and lifestyle in e-commerce is likely to increase by 30% and e-commerce will contribute about 6% of apparel sales. Fashion industry faces a challenge of understanding customers want and require quick access to market dynamics.  E-Commerce in retail is increasing, and all channels, firms are looking for advanced analytics to understand consumer behavior. Read more at: http://www.business-standard.com/article/pti-stories/data-analytics-can-help-retailers-know-consumer-behaviour-115042000358_1.html

According to Collen Jones (CEO of Content Science and co-founder of ContentWRX), big data help to identify new market opportunities and customers. But, most companies are facing problem with analyzing big data. Therefore, before analyzing big data, an organization also needs to analyze small data. • Understand your situation by collecting and analyzing the data.
• Interpreting the data you collected in a clear and compelling way
• Searching what to do next?
Collected Data should be focused on content and customers and should be accurate and reliable. And while searching what to do next try to find opportunities and threats.
To know more read at: http://www.cmo.com.au/article/573077/thinking-big-data-marketing-get-small-data-right-first/