Transport for London (TfL) data, collected through ticketing systems, censored vehicles, traffic signals, survey groups etc. is provided through open API's for 3rd party app developers. This data is then used to produce maps showing when and where people are traveling, and allowing analysis at the level of individual journeys by using Big Data. The key priority to initiate this data was to provide travel information which gives the routes customers use and to send travel updates to them. Thus Bernard Marr from Forbes in his article showed how big data played a big part in re-energizing London's transport network. Read more about this article at: http://www.content-loop.com/big-data-internet-things-improve-public-transport-london/

Paul Mandeville, Chief Product Officer, QuickPivot, in his article at cmswire.com has given 3 C’s of modern retail marketing. They are:

• Context
• Content
• Customer Experience

Combining these three with big data, can do wonders for retailers. All they need to do is to clean up their organization’s database and find a common thread between these three C’s of retail marketing.

The retailers need to realize the power of these C’s for capturing their customers’ hearts. To know more, please visit the following link:

http://cmswire.com/digital-marketing/the-3-cs-of-modern-retail-marketing/

Sustainable development is the development of the society, keeping in mind the needs of the present as well as future generations. It is this development that is the ultimate goal of every nation. For this, UN will launch a new set of Sustainable Development Goals (SDGs) in 2016. These goals will give rise to various important questions such as:

• How will we achieve these goals?
• Who will finance these goals?
• Which countries will need most resources and of what type etc.

For answer to these questions, the decision makers refer to the data provided by various international institutions like UN, IMF, World Bank, etc. But while doing this, they face a big hurdle i.e. Incompatibility of Data in terms of definitions, methodologies and sources.

Now, with the data revolution going on and big data and analytics taking the centre stage, we now have an opportunity to collect and produce high quality data that will provide the right information on the right things at the right time. The need is – its correct implementation.

To know more, please read the following article by Gail Hurley and Jos Verbeek at brookings.edu:

http://www.brookings.edu/blogs/future-development/posts/2015/05/26-financing-for-development-hurley%E2%80%8F

Big Data- Knowing what it is and how it can be used is not sufficient. To be successful, the need is to develop a strategy on how to optimize its use for your own advantage.

David A. Kelly, in his article in Q1 2015 issue of TeraData Magazine has compiled the views of three eminent researchers in this field - Vince Dell’Anno, David Stodder and Dan Vesset. They have suggested the following 3 best practices for developing big data strategies:

• View Big Data As A Valued Corporate Asset
• Foster A Culture Of Embracing Data
• Collect Diverse Data, Then Follow Up With Action

To understand them in detail, please visit the following link on forbes.com:

http://www.forbes.com/sites/teradata/2015/05/20/three-best-practices-for-executing-on-big-data-strategy/

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

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

Lots of experiments are going on in using predictive analytics in health care. But, only few succeed. The need is to learn from what has been done and work further.

Jennifer Bresnick, in her article on HealthITAnalytics.com, has summarized some of the ways healthcare organizations have already found success by turning big data into a strategic asset that can help providers react quickly and effectively to the ongoing challenges of quality care delivery. They are:

• Hospital quality and patient safety in the ICU
• Precision medicine, personalized care, and genomics
• Population health management, risk stratification and prevention
• Reducing preventable hospital readmissions

To know more, please visit the following link:

http://healthitanalytics.com/news/four-use-cases-for-healthcare-predictive-analytics-big-data

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/

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

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/

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 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/

According to Mary Shacklett (president of Transworld Data), organizations need big data and analytics capabilities for immediately transferring big data into actionable decisions. According to a Gartner September 2014 report, there is an increase in investment in big data by 64% from 2013. Jeff Kelley (Big data analytics analyst from Wikibon), says that “customers expect personalization when they visit websites, so companies need to develop analytical capabilities and in the long term apply real-time will grow as Internet of Things.” Preventive maintenance analytics can be developed, if data on the Internet to thing can be analyzed. To know more about real time analytics and Internet of Things read on:  http://www.techrepublic.com/article/surge-in-real-time-big-data-and-iot-analytics-is-changing-corporate-thinking/

Punk in Punk Analytics means working fearless and having an approach to ‘do-it-yourself way’. In the last few years with the rise of new technologies, we entered into the era of punk-style analytics. Some characteristics of punk analytics are:

• No barriers for information search, as you can download easily and free of cost.
• Mistakes are part of the process, but we can overcome by using analytics software and by having familiarity with the data.
• Fast and to the point is good.
• Idea should be transparent and it should have less processed time.
• Punk Analytics are basically concentrating on the issues of the moment, as it is the starting point for active exploration.
•   Working collaboratively is important to achieve a common goal.
To know more about punk analytics follow the article link of James Richardson (Business Analytics Strategist at Qlik): http://www.itproportal.com/2015/04/19/rise-punk-analytics/

According to a 2014 survey of more than 2,000 business professionals by MIT Sloan and SAS Institute, 87% respondents share a common restlessness to elevate their organizations to the next level of analytics.

Being data driven, has now become a necessity for all businesses, especially financial institutions. Using predictive data analytics to interpret a wide range of internal and external data on customers helps financial institutions to identify best targets for a particular product and earn more by making timely pitches.

Russ Bunham, in his Forbes article, gives four tips on how financial institutions can use big data analytics to their advantage:

1. Creating a customized, consistent customer experience
2. Dissolving internal silos to have one view of the customer
3. Ensuring data insight flows to the right person to make the pitch
4. Using big data knowledge to enhance customer relationships

To understand them in detail, visit the following link:

http://www.forbes.com/sites/centurylink/2015/04/14/4-ways-financial-institutions-can-bank-on-big-data-in-2015/

Over the last two years, Big Data and technological advancement have transformed the way industries operate and compete. Financial Services industry, in particular, has adopted big data analytics to inform better investment decisions with consistent returns. It is widely used by investment banks, asset management firms, insurance firms and stock exchanges, to name a few.

The Investopedia article by Trevir Nath, talks about 3 V’s of Big Data i.e. volume, variety and velocity, how they are applied by financial services industry, algorithmic trading and the challenges faced by this industry in the increasing embrace of big data. In the nutshell, this article explains that despite the challenges, the financial services industry is trending towards Big Data and Automation.

Read more at:

http://www.investopedia.com/articles/active-trading/040915/how-big-data-has-changed-finance.asp