As a result of increased e-commerce in B2B and the general availability of data on the Internet, B2B pricing and product information is easier to find. Buyers have more information than before to have relevant and convenient product and pricing. To meet these assumptions, B2B companies are leveraging advanced analytics. Analytics can help companies to customize buying experiences through commerce channels. It also provides sales reps about what customers are likely to purchase and what prices make sense to quote in the context of the deal. Read more at : http://data-informed.com/how-advanced-analytics-is-changing-b2b-buyer-expectations/

Precision analytics is enabling e-commerce dealers to deliver goods fast after getting an order. They study buying patterns and prompt the buyer even before they decide to click. India's largest mobile wallet, Paytm is also trying to strengthen its presence in e-commerce. E-commerce company Amazon also use predictive analysis to study customer buying trends. Read more at: http://www.business-standard.com/article/companies/precision-tool-to-get-speed-on-e-commerce-profit-road-116051600057_1.html#

Big data & analytics bring together open source technologies. That works together to accelerate the data pipeline. They are : Spark - for large-scale data processing

Mesos -  for cluster resource management

Akka - for data-heavy applications

Cassandra - for storage engine and 

Kafka - for event processing

To gain the full value of data, there is a need to analyze it in real time. Read more at:  http://www.cio.com/article/3068672/analytics/igniting-faster-analytics-with-the-smack-stack.html

Visual content is more effective and interesting than text article. In a research, it was found that colored and eye-catching visuals increase people's willingness to read a piece of content by 80% and 40% of people respond better to visual information than plain text. Infographics and videos are also powerful, and interesting platform to represent a message. Research shows that infographics are liked and shared 3 times more than other any other content on social media. Read more at: http://www.business2community.com/content-marketing/20-infographic-video-facts-feel-power-visual-content-01544708#Cr0SLgpyZOmoqY49.97

With regards to big data analytics in the healthcare industry, there's a noteworthy contrast between beginning an activity and succeeding with it.  We have to collect data systematically. Organizations should also conduct regular reviews of their data integrity and health information management programs to help clinicians to collect important data in a standardized, and accurate manner. Big data analytics is costly and hiring an outsider with more experience might be interesting for suppliers who are feeling lost. Outsourcing can also be risky when it comes to data security. Read more at: http://healthitanalytics.com/news/how-to-build-a-successful-big-data-analytics-program-in-healthcare

The most common hurdle for healthcare organizations with data analytics programs is consistent information from unique sources. An enterprise resource planning (ERP) can manage operational staff data, but cannot store patient-care related data, and therefore conducting a financial analysis of a patient care requires some imagination. If the analysis involves a nursing unit, the study may include clinical, operational staff data captured from the ERP such as shift worked, number of shifts worked for the current pay period, temporary or permanent resource status, educational background and professional credential levels. Linking this data to patient care data may require the shift date, patient bed assignment and other data elements. This type of analysis would only be possible on second- and third-generation data systems that can process different data languages for analysis and research. Read more at : http://healthitanalytics.com/news/goals-and-guidance-are-key-to-healthcare-big-data-analytics

More than half of all projects aren't completed within budget or on time. An analytics initiative can fail for a variety of factors: First, there are misconceptions about analytics. It's also not about data. Rather, it's a way to predict future strategies and support decision-making. Second, analytic projects fail when the data quality is inferior. Third, states and localities suffer from a talent shortage when it comes to finding people who can successfully run an analytics project in the public sector. Last, measuring the impact of analytics in government is  more complex than in the private sector. For more read: http://www.governing.com/columns/tech-talk/gov-data-analytics-failure.html

Data and analytics capabilities have become a top priority for businesses. Many organizations use the discipline to gain a competitive advantage and improve the customer experience. Nowadays, organizations place a high value on data and analytics. Read more at: http://www.emarketer.com/Article/Marketers-Analytics-Strategies-Extend-C-Suite/1013901

A business must know their audience. The risk starts when a business does not have customer insight, disorganized measurement systems. According to a research, more than half of direct traffic comes from organic search, but due to browser issues, Google may not be able to detect this. It's important to accurately pinpoint the traffic's source and medium (organic search (organic), cost-per-click paid search (CPC), and web referral (referral)). To know about referral traffic, read : http://www.skyword.com/contentstandard/enterprise-marketing/how-to-deal-with-direct-traffic-source-data-in-your-content-analytics/

As client information volumes grow, more advertisers depend on predictive analytics programming to focus on their clients. With predictive analytics, advertisers use information science-based strategies to segment databases. Then they figure out which practices and properties of an organization are measurable, and after that identify a pool of eminent clients that match those qualities. Predictive analytics are truly encouraging on the grounds that can begin taking the advanced non-verbal communication and particular practices and utilize that to manage  advertising venture. Read more at: http://searchcrm.techtarget.com/feature/Predictive-marketing-tools-trump-old-fashioned-gut-feel

Nowadays, we hear about data science technologies like machine learning and data modelling. But, now new cloud services for machine learning is gaining importance and more do-it-yourself (DIY) tools will emerge for sectors like financial services, healthcare or retail, predictive sales or marketing. Read more at: http://www.business2community.com/b2b-marketing/diy-predictive-modeling-pitfalls-opportunities-01527315#PHERr4OuiDd4QyeJ.97

Predictive marketing uses machine learning to deliver more accurate insights to encourage sales. The primary objectives are measuring customer behavior and audience insights, campaign effectiveness, calculating and improving customer lifetime value and customer retention. Predictive analysis can achieve these goals by learning from patterns within the data that are derived from customer touch points. Read more at: http://www.emarketer.com/Article/Using-Data-Predictive-Analytics-Helps-B2Bs-Throughout-Funnel/1013868

Supply chain management is a field where Big Data and analytics have many applications. Organizations apply big data analytics in supply chain management. Supply chains have been driven by statistics and quantifiable performance indicators. But the sort of analytics, which are really revolutionizing industry today are real time analytics. Traditional data monitoring involves sales and order tracking and point of sales data. It is now being supplemented with weather, events and news, with the aim being to create insights in the short term. For more read the article written by Bernard Marr (contributor) : http://www.forbes.com/sites/bernardmarr/2016/04/22/how-big-data-and-analytics-are-transforming-supply-chain-management/#5c0319b44c2d

Big data analytics is a useful instrument for empowering organizations to become more resilient in the face of increasing cyber-attacks, according to a software market analyst and IT consultant. Attackers inject malcode into genuine processes by process hollowing and install deception techniques. They send data outside an organization. Analysis of security log data and other security system data provides useful insight for improving organizations' cyber defense capability. For more read the article written by Warwick Ashford (security editor) : http://www.computerweekly.com/news/450288411/Big-data-analytics-a-useful-security-tool-says-analyst

Social media is important for any business. It can help be to connect with customers and drive conversion rates higher. Most customer relationship management tools are incorporating social selling into their platform. But, many companies are not aware of the importance of incorporating social media into their platform. This article will help you to know about the factors why CRMs need to take social leads seriously. Read more at: http://it.toolbox.com/blogs/insidecrm/4-reasons-crms-need-to-track-social-media-73172

Nowadays, cloud and mobile technologies are providing enterprises with chances to utilize big data and analytics to take better decisions. Cloud computing helps companies to combine information from all sources. The advantages that cloud technology provides over on premise data analytics implementations are- 1.  Robust Data Foundation, 2. Fast Time to Value, 3. Improved Collaboration, 4. Quicker Adoption, 5. Scalability and Elasticity, and 6. Lower Total Cost of Ownership. For more read the article written by Ajay Khanna (Vice President, Reltio) : http://data-informed.com/why-cloud-analytics-is-better-analytics/

Organizations need databases which are needed to store data safely. Through databases one can solve problems from NoSQL and RDBMS framework to in-memory databases. This help banks to give quicker reaction times and viable examination, prompting better client experience and maintenance. Utilizing a center layer on top of various databases, banks can rapidly assemble information. For more read the article written by Nanda Kumar(CEO, SunTec Business Solution) : https://www.finextra.com/blogposting/12478/making-data-work-for-banks

Intelligent automation can make a crossover IT environment. It is enhanced continuously. It decreases bottlenecks and kills the requirement for manual mediation. It can also screen SLAs through changing occupation needs and cordoning off servers so that vital work processes can be executed. Intelligent automation additionally makes it conceivable to rearrange IT administration by building connections between apparatuses. Read more at: http://www.networkworld.com/article/3060616/cloud-computing/how-to-use-intelligent-automation-to-drive-better-cloud-resource-management.html

One of the major difficulties for organizations is the accumulation of data.  According to a research, it was found that only 1% of all apps use prescriptive analytics. This number is set to rise by 2018 to 50%. Organizations need to have high quality, rich insights into their data usage and is important for forecasting, tracking physical and performance capacity. This is where predictive analytics plays an important part; allow real time feedback, provide advantage of tools for capacity and performance planning, lower the cost of ownership, and improve the quality of support services. For more read the article written by Tim Jones (Technical Specialist) : http://www.mis-asia.com/blogs/blogs/predicting-your-storage-capacity-with-analytics/

At the point when we plan the memory subsystem, we consider the design of human brains. So in some ways people aren't an incredible model for an analytic framework, and that is why current-day information researchers overlook the way our memory framework works. Designing the memory subsystem for an advanced analytics solution, we have to consider the engineering of our brains. The layer that we put on our operational data can be simple. We could put in an all-out operational systematic layer for transient choices taking into account current information. It needs time to see how our brains store and recover information and utilize that data as a model for creating the next logical framework. For more read the article written by John Weathington (author) : http://www.techrepublic.com/article/get-smarter-about-analytics-by-studying-memory-design/