One of the ways to track what a customer sees is with product impression- keeping an account of the products displayed on the page that the customer visits. While this is an easy way of extracting data it has one major problem. A page usually contains multiple products and it is not necessary that just because a person lands on a particular page he is interested in all the displayed products. The more obvious outcome maybe that he might not be actually be viewing all the products displayed at one go- limited screen space- the reason we scroll.
The solution: Scroll based impression tracking.
Instead of automatically assuming all items to be desirable we segregate the page in accordance to the customer’s view- we only track the products visible to the customer. Once the customer starts scrolling we track only the products for which the customer stops scrolling and rank items according to the time spent viewing them. Double counting of products is avoided.
Scroll based impression tracking gives a much more accurate measure of a customer’s propensity of buying a particular product than the traditional product impression method.
For more information visit:
http://content.infotrustllc.com/infotrust-blog/how-to-setup-enhanced-ecommerce-impression-the-right-way-using-scroll-tracking

The process involving social media marketing is the amalgamation of both art and science. Let us have a look at what it takes to develop and maintain content for social media marketing.                                                                                        

• Creativity in content is crucial. Group brainstorming sessions are held including writers, marketers, sales managers to develop new and creative ideas.                                              
• Content prepared by writers is then edited for spelling, grammar, accuracy and creativity by team of experts.                
• A visually enriched content is created by inclusion of images without violating copyright laws.                                        
• Marketing copy is generated so as to ensure that clients’ goals are achieved be it selling a product or branding.          
• Targeting specific audience so that message reaches the right bunch of people.  
•  Lastly, social media sites are monitored and analytics tools are used to identify which campaigns perform well and which don’t. This learning can be put to use while developing content in future.

Read more at:http://www.business2community.com/social-media/behind-the-scenes-the-many-facets-of-social-strategy-01264443

Big Data may be relatively new but data has been a hot topic of discussion for over a 100 years. Interesting to note, the challenges in fetching and understanding it remain fundamentally the same. We are told that this is the era of Big Data but its roots go back to 1914. Willard Brinton wrote the first book on communicating data, Graphic Methods for Presenting Facts. He had stated that those who possessed cutting-edge data technologies would be the pioneers in their field. Processing and visual representation of data as information would allow us to give just the required information and facts to allow those who aren't proficient in data, actionable insights into their problems. In 100 years more, we would hopefully understand analytics and effective data visualization. Read more at: http://www.computerworld.com/article/2942728/big-data/big-data-the-100-year-old-buzzword.html

Although most modern enterprises run on data, it becomes useless if we cannot derive intelligence from it. Data includes all the information that is available including raw facts about companies and individuals. Intelligence is when we look at the data and try to find a relation to better interpret challenges and opportunities in the market. Insight with data and intelligence deals in adjusting strategies in response to the market. To develop predictive models about the market, we need intelligence and insight alongside data. Raw data is often mistaken for insights. For smarter decisions, internal and external data sets should be used as starting points and then create intelligence to draw sensible insights. To know more:  http://www.smartdatacollective.com/ray-renteria/327508/data-does-not-equal-intelligence-predictive-analytics-enterprise

There is an indirect effect on the rupee from the Greece crisis, even though India has less direct exposure. Dr. Raghuram Rajan (RBI Chief) in his article says that India is well guarded with good macro-policies and a healthy economy. Amidst the turmoil, the rupee has remained stable against the dollar and euro. Thanks to the foreign exchange reserves India is projected to fend off any impact from the crisis. Analysts have put the same claim that stronger economic fundamentals and comfortable foreign reserves are enough to see off any impact. Also amidst stimulus programme, the Greece crisis is said to be grounded. To know more:  http://profit.ndtv.com/news/greece-crisis/article-greece-crisis-may-impact-rupee-raghuram-rajan-777442

Internet of Things is set to digitize the world, with sensory technology in devices, feeding real time data to the internet. It is projected to have economic impact of $3.9 trillion to $11.1 trillion by 2025.
Such impact requires favorable groundwork, like interoperability between IoT systems and data generated must be used to its maximum. There is large potential in developing countries; customers will capture most benefits with users in businesses and other organization with remote monitoring in healthcare etc. The digitization of machines is a revolutionary idea, and even shows promise at this infant stage. Capturing full potential will require innovation and investment.To know more: http://www.mckinsey.com/insights/business_technology/the_internet_of_things_the_value_of_digitizing_the_physical_world

Internet of things is projected to take the world by storm. Ben Rossi (author) talks about how IoT will affect simple devices from a wristwatch to a tractor, and process usage data in real time. This will improve marketing efficiency and speedup campaigns. Large volumes of data will imply linkages and collaborations between marketers, data analyst and IoT enablers. Troubleshooting will be a new experience, with solutions coming up in real time. Customer profiling will be more complete, as devices can now integrate user activity full time. This will provide insight into customer behavior and hence provide instant solutions based on preference patterns.
 To know more: http://www.information-age.com/it-management/strategy-and-innovation/123459628/4-key-internet-things-analytics-trends-marketing-pros

Let’s say you work for a publishing company deciding upon what New Release Newsletter to send to a particular customer. One simple way would be to just ask your customers as to what genre of books they would prefer to read- Preference Modeling-and the decide accordingly what newsletter to send to whom. Alternatively you can try to predict an individual’s buying propensity for a particular genre and send the newsletters accordingly – Cluster Modeling.
Which of the above should you chose to yield more accurate results?
A survey was conducted with a New York based publisher revealed that deciding which genre newsletter to send to whom based on Clusters had a 2-time better open rate, a 4-time better and 7-time higher click-through-rate, than those based on the consumer’s self-stated preference.
The conclusion: It is equally, if not more, important to observe what people do as compared to what they say.
Read more at: http://www.agilone.com/clustering-beats-preferences

Big data analytics in healthcare industry, encompasses the whole transformation of data, beginning from the recording of the fundamental data to predicting diseases and alerting patients. Any health concern’s big data analytics capabilities fall into three major categories, descriptive, predictive and prescriptive. Descriptive analytics quantifies past events, like for example, the number of patients, who visited the hospital in the last month. It provides considerable insights into the data which can be later used to make predictions, regarding disease trends. Predictive analytics uses these descriptive data to predict the future, which often reduces unnecessary costs, related to controllable diseases, by making early detections. Finally prescriptive analytics provides suggestions on ways to mitigate the imminent threats, suggested by predictive analytics. Hence, an efficiently functioning data analytics infrastructure, incorporates all the three processes and is invaluable in the healthcare industry. Read more at:

http://healthitanalytics.com/news/healthcare-big-data-analytics-from-description-to-prescription

Sigmaway consultants have worked with clients in benefits solutions workspace providing analytics on healthcare plans. For more details visit http://www.sigmaway.us/

# Technologies like self-service and social media are important, if insurers need to communicate, face-to-face, with customers, since, it is the best form of transferring information, till date. People working on data insights need good communication skills to convey their findings to customers, who may not understand the technical language.
# Organizational collaboration is always important between two institutions if they are supposed to work together, so that they can share their technologies and focus on other business priorities, in harmony.
# As customers are generating more and more data, insurers are turning to analytics in order to interpret the data.
# With increasing data security breaches, insurers need skills to address the vulnerabilities and manage the situation in case of a security breach.
# Insurance technology professionals must be ready to embrace the evolving technology in order to survive the competition.
# Insurers need to think creatively of utilizing the multitude of technologies, available, in order to improve customer relations.
# Social media skills are required to build customer relations, as well as, create a brand image for the concern. 
Read more at:

http://www.insurancetech.com/management-strategies/7-tech-skills-for-insurance-pros-/d/d-id/1319170?image_number=2

Organizations are now facing a change in the way they get their business intelligence services. Initially it was the IT department that provided business intelligence services, with all powers vested upon them. With business intelligence tools becoming increasingly accessible and users having knowledge of data and processes, IT departments are no more the only source of business intelligence services making self-service business intelligence the next step. The market for self-service business intelligence analytics tools is growing pretty fast. With these tools, business users can access pooled data from various sources, leverage those and produce business insights. Self-service analytics comes with several advantages. With self-service business intelligence, data has lower turnaround times leading to increase in decision-making processes and productivity. Read more at: https://channels.theinnovationenterprise.com/articles/the-drive-towards-self-service-business-intelligence

 The potential to go public and make millions with an Initial Public offer (IPO) is appealing to most entrepreneurs and sometimes seems the statement of success.  But IPOs are not as pretty as they seem. Planning and executing the IPO is a hectic task, it requires huge research and strategy, which is mostly outsourced to investment bankers who gives a rosy picture hiding some important information you should know.  After the IPO is floated you end up creating a lock up since those shares can’t be sold for a while. Also, not every company is suited for IPO, except those who have a simple business model and a clear cash flow strategy. There is a difference between great valuation and accurate valuation, overvalued stocks collapse as the markets gets smart unless they grow into their valuation. Read more at: http://blogs.wsj.com/accelerators/2015/06/23/richie-hecker-understand-the-risks-of-going-public-before-you-ring-the-bell/

Healthcare is being revolutionized by the advent of big data. New gadgets and technologies are aiding this transition. A continuous stream of health related data promises us the concept of continuous healthcare. This data will enable us to study behavioral changes among patients and diagnose problems better and faster. Real time disease management is fast becoming a reality and it will be even possible to tend to patients in the early stages of their disorders and take steps accordingly. Real time monitoring of treatment efficacy will immensely help in post-operative care, drug therapy. It also holds the power to point out which patients merit additional intervention. Read more at:

http://www.dataversity.net/how-big-data-will-enable-continuous-healthcare/

The combination of new software technology, industrial data science and visualized information is acting as a game changer in the aviation industry. Analysts monitor flight data every second on the ground, for variations and immediately alert the pilot of any anomalies. Real-time data monitoring also helps in reducing fuel consumption apart from providing safety and security. Tim Leonard of southwest Airlines while talking to business insider said that the predictive analytics tools help prevent disasters they make pilots, mechanics, and employees more aware. It also helps provide frequent and precise data on current weather conditions leading to better scheduling and minimising delays. Read more at: http://www.businessinsider.in/Why-every-flight-you-take-is-obsessively-monitored/articleshow/47694902.cms

Data science techniques are becoming increasingly popular these days to improve business outcomes. Hadoop, already showcased the broader use of big data technologies and their impact on businesses. In this new age, the limits of machine learning are constantly being tested as innovators are trying new techniques that decrease human intervention as much as possible. Companies are ready to work with the data they have in Hadoop. Penetration of SQL on Hadoop has been a great help as they have created an environment that has made data accessible to downstream apps and learning algorithms. Machine intelligence is catching up in all spheres, data science is becoming a new trend with data scientists coming in demand. To know more, please follow:

http://www.dataversity.net/predictions-for-data-science-over-the-coming-years/

Corporates today provide many pro women services like day-care centres, six months-maternity leave, transportation services etc., in fact, sometimes companies hire same number of men and women at entry levels, but still the proportion of women workforce is on a decline. According to Economic Times, a recent report shows that women “invest” in their careers differently. After obtaining a job, the effort to take it further reduces. Post marriage, her career becomes home oriented as she has both work and home responsibilities. Apparently, women who work with same passion as men are tagged “non-family type”. They are not able to develop a rapport with their bosses as strong as men’s. So, retaining female employees depends entirely on herself, the family and the supervisor. Read more at: http://blogs.economictimes.indiatimes.com/et-commentary/why-indian-companies-are-losing-their-women-workforce/

In the face of global competition, the business world needs to adapt new technologies and forgo past financial traditions. Following Derek Klobucher (Moderator of SAP Business Trends)'s article, most software were designed in the 70s, and 80s so they are slowly losing relevance in the modern era as the technology landscape is vastly changing. The way forward is adapting the next generation business platform. SAP is working on solutions to integrate instant real time insight, offer updated user interface and user experience which saves on transaction times by 30 to 40 percent, and be non-disruptive. Month end closing is a traditional; however modern times dictate fast, accurate and secure data analytics. To know more http://www.news-sap.com/how-real-time-analytics-will-kill-a-financial-tradition-sapinsider-2015/

Text data is the best answer in understanding customer feedback. It is best for answering the why and what questions. Text Analytics is becoming mainstream as interest is pouring in. Sources include call center notes, email messages, social media etc. Following Fern Halper's TDWI article, 5 fundamentals include: 1. Text analytics involves analyzing unstructured data to extract relevant information.
2. Extracting various information in the form of keywords, entities, concepts and sentiments
3. Considering taxonomy, while dealing with specific vocabularies.
4. Integrating text data with traditional data, depends on the kind of data
5. Text data is not as accurate as statistical techniques
To know more http://tdwi.org/Articles/2015/06/23/5-Text-Analytics-Fundamentals.aspx?Page=2

Just as sourcing ingredients is essential to the quality of a great dish, the quality of labels determines the accuracy of a supervised or semi-supervised machine learning (ML) solution. What is a label?
Data labeling involves taking unclassified data and augmenting each piece of that data with some sort of meaningful ‘tag’, ‘label’, or ‘class’ that is somehow informative or desirable to know. Assigning a label is a judgment task to be performed by an analyst depending upon the kind of variables he/she wants to work with and is an essential part of the ML process.
How to ask Questions?
All tasks involving labels involves, at some point, asking a question to a human being for collecting the data. The questions should be relevant, clear and precise in addition to being plain and understandable and one for which answering does not involve much effort for the individual.
How to debug tasks?
The collected data may sometimes show discrepancies due to:
• Data: Certain factors which can cause bias in the workers have to be eliminated.
• Workers: We have to detect the expected errors arising due to human involvements, say, an error arising due to spammers rather than people making genuine mistakes.
• Tasks: If the problem still persists then there might be a problem with our initial assumptions. Time to rethink from the start.
How to assess Work Quality?
Specific domain based algorithms exist for every point during the project that determine the ongoing quality of work.
Labels are essential and cutting on effort in this regards is most likely to lead you to erroneous results.

For more information visit:
http://blogs.technet.com/b/machinelearning/archive/2015/06/23/label-quality-for-machine-learning.aspx

As the amount of data being generated daily is increasing, sales teams are now using analytics to boost sales. Several factors should be kept in mind while doing this like choosing the right set of data. The sales team may be lead astray when wrong data sets are chosen leading to drawing wrong conclusions. Choosing the right metric and making correct assumption is also crucial. Data on compensation can also provide insights into how sales can affect individuals or a group. Using this data and applying predictive analytics, one can easily forecast future performance and accordingly plan sales process. Read more at:http://www.cmswire.com/digital-marketing/dont-let-data-blind-your-sales-team/