Cloud Analytics is one application that is growing very fast and will accelerate in the coming decade. According to Ashutosh Kulkarni (Sr. Vice President and General Manager, Informatica Cloud), scalability, affordable price, simplicity to offer domain specific and user-specific capabilities make cloud analytics unbeatable. The scalability of Hadoop, and the emergence of highly elastic cloud analytic services like AWS, EMR and Redshift, Microsoft Azure, and Salesforce Wave Analytics, has helped in moving IT infrastructure to the cloud and cloud integration Platform-as-a-service (iPaaS) is designed to support the scalability, reliability and security of cloud analytics. Read more at: http://www.computerworld.com/article/2924846/data-analytics/the-future-of-cloud-analytics-is-now.html

Healthcare analytics, estimated to be over 20 billion, is growing very fast. The main reasons for the growth are: improved population health, reduced healthcare cost and patient safety. Thus, a vast majority of healthcare decision-makers have made analytics their priority. However, there are four main factors that could impact the adoption rate of analytics in healthcare. Data integration: It is the most important challenge because of the absence of unified datasets and inoperability between technologies.

Data breaches and security: This needs to be taken care off when data needs to be transferred to cloud in view of the recent healthcare data hacks.

Data sciences talent: There is likely to be stiff competition for talented data scientists with experience in healthcare and analytics.

Low current levels of analytics investment: The order of magnitude of investment in analytics may be the biggest challenge for the growth of analytics in healthcare.

Read more at:http://www.cio.com/article/2904270/healthcare/healthcare-analytics-4-things-impacting-the-adoption-rate.html

Instincts, intuition and creativity are invaluable when it comes to customers, but they cannot paint the whole picture. A solid grasp of data and analytics is required along with them to make a mark in the market. For example, logic does not come out as a winner when deciding loyalty but data and applied analytics tells us a different story. Results though counter intuition sometimes provide important insight which helps develop the marketing strategies. Advanced analytics ventures into the field of correlation and converts guesswork into evidence-based decisions. Advanced analytics also improves customer experience.  Sometimes it does not completely rely on intuitive results, deviates from it but helps to validate targeted campaigns.  To know more please follow the link:http://www.cmswire.com/cms/digital-marketing/they-love-me-they-love-me-not-data-analytics-can-tell-you-029247.php

Web-Connected devices that can talk to each other, transfer data, and carry out tasks without human interaction, are what make up the Internet of Things (IoT). These devices/ things are becoming popular among any firm looking for an edge. Generally IoT refers to devices in Consumer products such as Smart Phones, Smart cars etc. In Industrial sector these devices are used to build smart factories by integrating sensors and software to complex machinery. Growth of these devices will be very high as suggested by a report from Deloitte and there are only few firms working in IoT/Industrial Internet implying that there will be enormous opportunities for entrepreneurs in this sector.

As is the case with any budding technology, this area also faces challenges. Some of which are data security, addressing potential customers’ concerns about privacy, the lack of common standards between companies developing software for these devices; and the need to build physical infrastructure to support the collection and analysis of the data.Read more at: 

http://www.wamda.com/memakersge/2015/05/5-minute-guide-internet-of-things

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/

•  Big Data enables various capabilities like forensics and the analysis of long-term historical trends. By collecting data and analyzing historical trends, you would be able identify when an attack started, and what were the steps that the attacker took to get a hold of your systems. These techniques could play a key role to detect threats at an early stage. Big Data provides  opportunity to consolidate and analyze logs automatically from multiple sources rather than in isolation. This enhances intrusion detection systems (IDS) and intrusion prevention systems (IPS). Integrating information from physical security systems, such as building access controls and even CCTV, could also enhance IDS and IPS to a point where insider attacks and social engineering are factored in to the detection process. This presents the possibility of significantly more advanced detection of fraud and criminal activities. Big Data could result in far more practical and successful SIEM, IDS and IPS implementations. Read more at-         http://www.techrepublic.com/blog/big-data-analytics/how-big-data-is-changing-the-security-analytics-landscape/

• Apache Hadoop- as software vendors are targeting the distributed storage and processing architecture, need for Hadoop is increasing 
• Apache Spark-The rapid rise of the in-memory stack is being extended as a faster and simpler alternative to MapReduce-style analytics.
• NoSQL-Databases like MongoDB and Couchbase are taking over jobs previously handled by monolithic SQL databases like Oracle and IBMDB2.
• Machine Learning and Data Mining- Big data pros who can harness machine learning technology to build and train predictive analytic apps are in high demand Statistical and Quantitative Analysis-Add in expertise with a statistical tool like R, SAS, Matlab, SPSS, or Stata is of high demand in today’s world. 
• SQL- SQL is still in demand for the next-generation of Hadoop-scale data warehouses. 
• Data Visualization-It has become most important in the job market. 
• Progamming Languages-Knowing programming languages like Java, C, Python, or Scala could give you the edge over other candidates. Read more at: 

http://www.datanami.com/2015/01/07/9-must-skills-land-top-big-data-jobs-2015/

Companies want to have an edge using analytics but struggle due to unavailable technologies, outdated software, cumbersome systems, complex integrations coupled with huge costs in infrastructure and personnel. The cloud has transformed the way organizations look at big data and analytics solutions. It has been predicted that investment in cloud over the next five years will grow threefold. • Open source platforms like Apache Spark provide simple and fast data processing capabilities. These though powerful, due to expensive hardware, long lead times are difficult to deploy. The cloud allows immediate usage of open source platforms without initial investments.
• Cloud -based software are user friendly, simple to grasp and sort in comparison to on premise counterparts and release cycles are shorter.
• Cloud-based analytics provide a platform where hard business problems can be effectively tackled.
• Processing and extracting data are easy from a synced centralized location.
• Better connectivity leads to better productivity, deployment of data pipeline is easy. Read more at:

http://insidebigdata.com/2015/05/08/5-reasons-data-analytics-in-the-cloud-will-take-center-stage-in-2015/

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