There are many ways in which a first-time customer will feel enticed to be a full-time customer and maintain a long relationship with the company. One should understand the profile of the typical customer of the company and segment the group both geographically and demographically. Giving customer's a positive experience by accurately replying to their queries goes a long way. Companies should understand that customer retention is much more than the cost incurred in the advertising campaigns. While offering subscription information, companies should keep in mind not to cost their customers anything. Rather, moving subscriptions to social media can reach thousands customers within minutes. Hence social media posts should be updated according to the customers interests. Read more at :

https://www.smartdatacollective.com/customer-feedback-data-analysis-keys-good-customer-retention-rate/

A huge amount of digital data is getting piled up every day and to deal with that the technology recruiters are valuing the skills in data visualization, data science, machine learning and data analysis the most. These skills in data analysis help the companies to give more insight about the data and help to predict a better future. With the courses on data science people are now showing immense interests in machine learning and data visualization tools. Professionals are willing to upskill to keep pace with the automation. Read more at: http://economictimes.indiatimes.com/jobs/techies-reskill-to-log-on-to-big-data-deluge/articleshow/58103804.cms

Artificial Intelligence is one of the most important part of our daily life from simple features like automatic image tagging to prediction and recommendation in business. AI will begin to learn emotion, better understand human sentiment and solve more complex problems. Soon, companies will be able to automate a large chunk of data analysis, decision making and customer service, allowing employees to tackle the most complex challenges rather than get bogged down in the details. Read more at:https://www.entrepreneur.com/article/290444

Predictive analysis is now coming to the mainstream. Companies are trying to recruit people with the knowledge of maths and economics together with the business. Evolution of analytics is changing its pace. Organizations are treating the data as their key assets and trying to analyze those to gain more from their business. Initially company didn't realize the importance of data analytics. But now it has become a common trend of trusting their data to the cloud as it seems more secure. Read more at: https://www.cio.com.au/article/620089/slow-evolution-predictive-data-analytics/?fp=16&fpid=1

With the increasing availability of data in the present digital age, a new science of opinion mining is emerging. It is based upon the use of Artificial Intelligence (AI) to mine public opinion for sentiments as well as the topics driving that sentiment. This can be carried out in two ways: the first one involves the exclusive use of AI to structure the data, while the second one involves the use of AI along with processing of data through a team of people to verify the data for sentiment and the topics driving the sentiment, since AI could struggle to understand the nuances of human emotions. So, the field of data mining can be used by governments, global organisations, media and businesses to shape their strategies efficiently and, measure the public’s/consumer’s satisfaction of their policies, products, services and brands.Read more at: http://www.business2community.com/big-data/opinion-mining-future-data-analytics-01849821#SoExOgmWJWYF7RIM.97

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. Visualization of data helps in finding specific information, like tracing data correlations by presenting the data in graphic form, and noticing how one set of data influences another. Also, by live interaction with data one can spot the changes in the data as it happens and get a predictive analysis. Data visualization enables one to not only see the information, but also to know the reasons behind it. With predictive analysis, the behavior of the trends in the future can be predicted. Thus, data visualization tools have become a necessity in modern data analysis. Read more at: http://www.datavizualization.com/blog/the-top-5-benefits-of-using-data-visualization

To make the data presentable, data visualization is of utmost importance. According to columnist Paul Shapiro data visualization plays a crucial role for marketers because by just looking at large datasets, we can’t get an idea about the pattern of the data. This can be easily done with a help of a scatter plots, bar charts, pie charts, etc. This data visualization makes our data analysis more effective. Here comes in the concept of preattentive attributes. These attributes are those aspects of a visual that our iconic memory picks up, like color, size, orientation, and placement in a few milliseconds. To read more, follow: -http://marketingland.com/brief-introduction-data-visualization-theory-marketers-184112

Effective data analysis is learned overtime. It takes time, patience and effort. Here are few tips which can help to make the journey of learning smoother.

·         Use simple analysis terms and methods rather than complex algorithms. If your customer and engineers are not able to understand your analysis then all the effort goes in vain.

·         Look for multiple data sources. 

·         Use familiar tools rather than new tools. We should stay updated with the newest technology in market but avoid abundant use of fancy new tools which are difficult to understand. Stick to classics.

·          Provide your insights with the indicators.

·         Clean your data.  Structure it properly. Look for the center, unusual features, spread, and shape of the data. 

·         It is important to move in the right direction rather than spending too much time on definitive answer.

·          Value how actually software works rather than how you understand or think software works.

Read the full article here:  http://dataconomy.com/7-habits-of-highly-effective-data-analysis/

Data provides valuable information to a firm to optimize its performance. Decision makers and strategists analyze data and take optimal decisions. Data analysis shows the firm its ongoing productivity and making predictions will lead to future growth. Employees also get benefit in terms of more productivity when they use data-driven tools providing more enhanced methods which they can use. Customer relations have been improved, as employees are more productive and can give more enhanced solutions to their customers. Analyzing the data collected from social media can determine how successful the conversations have been. This is possible just because of changing consumer behavior and innovations that lead to such change. Ideal business is one that reacts to social change. Analytical tools enhances culture among employees. More data is needed to improve customer service than before. Today there is a lot of pressure on employees as work is increasing with large data size. Here the data-driven technique  plays its role helping them to handle such pressure leading them to be more interactive and informative. Improvement in employee's performance will result in enhancement of sales process, training and innovation. This change will bring some excitement for employees, working with modern tools far better than those boring traditional tools. Building up the transparent system will bring good feedback from upper management for employees leading them to provide more optimal output. Read more at: http://www.smartdatacollective.com/daanpepijn/329438/data-changing-way-enterprises-optimize-employee-productivity

Data provides valuable information to a firm to optimize its performance. Decision makers and strategists analyze data and take optimal decisions. Data analysis shows the firm its ongoing productivity and making predictions will lead to future growth. Employees also get benefit in terms of more productivity when they use data-driven tools providing more enhanced methods which they can use. Customer relations have been improved, as employees are more productive and can give more enhanced solutions to their customers. Analyzing the data collected from social media can determine how successful the conversations have been. This is possible just because of changing consumer behavior and innovations that lead to such change. Ideal business is one that reacts to social change. Analytical tools enhances culture among employees. More data is needed to improve customer service than before. Today there is a lot of pressure on employees as work is increasing with large data size. Here the data-driven technique  plays its role helping them to handle such pressure leading them to be more interactive and informative. Improvement in employee's performance will result in enhancement of sales process, training and innovation. This change will bring some excitement for employees, working with modern tools far better than those boring traditional tools. Building up the transparent system will bring good feedback from upper management for employees leading them to provide more optimal output. Read more at:http://www.smartdatacollective.com/daanpepijn/329438/data-changing-way-enterprises-optimize-employee-productivity

Customers are choosing when and how to interact, turning customer relationship management (CRM) and data analysis as the most important thing. So, retailers now need to know about how to engage customers.  Customers have short attention spans and to overcome this, retailers have to offer a more personalized experience. CRM has been used to build a profile of a customer and thus can identify their likes and dislikes, past purchases and interests which in turn will help them to interact with customers in a better way. Personalizing the online experience and product offering is the key to make customers feel valued and listened to by the brand, thereby ensuring that they are likely to revisit in future. Read more about this in the following article link: http://digitalmarketingmagazine.co.uk/customer-experience/how-can-retailers-get-more-personal-with-their-customers/1411

Big Data means different to different industry e.g. to computer manufacturers, big data analytics could mean demand for expensive servers and storage arrays; to the communications or cloud industry, the movement of data. But, for general people it means Big Data Analytics. The impact of change could be far-reaching and unpredictable. The budget spent on advertising via electronic, print and display media could costs double for data collection and analysis with impact which is immense. In the age of analytical marketing, we see firms collect more and more data in order to fine tune their analysis and increase competitive edge. To know more, follow Barry Schaeffer (principal consultant with Content Life Cycle Consulting)’s article link: http://www.cmswire.com/cms/big-data/look-before-you-leap-into-the-big-data-revolution-026873.php

To boost manufacturing and entrepreneurship in the country, Flipkart (Indian e-commerce company)  announced its tie up with Small and Medium Enterprise (SMEs) promotion bodies. Strong data analytics that forms the base of e-marketplaces will help the sellers to improve their products easily and attract more customers. According to the Executive Director of NCDPD (National Center for Design and Product Development), analytics and market intelligence provided by Flipkart will assist NCDPD in improving their products and R&D and also enable the craftsmen to create better saleable products. The objective of this tie-up is to continue helping entrepreneurs to create products according to buyer requirements and grow significantly by expanding their business so that they may become manufacturers not only at a local but also at a national level. Read more at: http://articles.economictimes.indiatimes.com/2014-06-18/news/50678912_1_data-analytics-flipkart-market-data.

A pilot project using predictive analytics and applied natural language processing identified 8,500 patients of Carilion Clinic who are at risk of congestive heart failure. Discrete data points, such as weight and medications, can be found in structured EMR (electronic medical record) fields. Unstructured data includes physicians' notes that are typed or read into a patient's EMR or discharge papers.  The natural language processing technology searched for key words or phrases within the unstructured data as well as in structured data. In all, 20 million documents were analyzed. Because approximately half of all patients who develop heart failure die within five years, according to the “Centre for Disease Control and Prevention”, early identification is essential. About 3,500 of the 8,500 patients Carilion identified as at-risk would not have been found if the project had analyzed only the structured data, according to Steve Morgan, MD and chief medical information officer at Carilion Clinic.

To know more, please visit famous author & reporter Maggie O'Neill’s article by clicking on the following link:

http://www.baselinemag.com/analytics-big-data/analytics-ids-patients-at-risk-for-heart-failure.html/