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/

Before we start discussing about Tools for Social Media Analysis, first of all we have to know what social media analytics is? Instead of thinking it as a noun, take it as a verb. Precisely, it's gathering data from social platforms to help guide marketing strategy.

# the process begins with the prioritizing goals.

# The second step is determining key performance indicators (KPIs) i.e. likes and shares your posts receives, replies and comments, and more importantly the clicks your links and content earn analysis.

Now, as the definition is clear, we will directly come to the social media analytic tools. To read more, follow: https://keyhole.co/blog/list-of-the-top-25-social-media-analytics-tools/

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

Machine learning has showed tremendous potential to transform companies from inside out. Everyday new algorithms are coming up that are being used to encounter data and tackle new problems. On the other hand, a closer look at machine learning reveals it to be nothing more than a branch of statistics for a world of big data. Business executives with a thorough understanding of machine learning have the ability to reach efficient business outcomes. In this age of data, firms have to work with large scale data. Both advanced software and hardware is needed to manage, analyze and store it. Herein lies the applicability of machine learning. To know more, please follow: http://www.dataversity.net/what-business-execs-need-to-know-about-machine-learning/

If the country is changing faster than the expectations, then organizations are faced with the challenge of moving with those changes. It is affected by fast evolving markets, ever changing technology, increasing competition and changing preference. So how to react to these changes? First is by gathering intelligence, using big data analytics to discover patterns by tracking commercial data, for example in consumer spending, movements of competitors. Next is to look for the unexpected. This involves constantly improving your business model, inculcating the changes that raise inside and outside the industry. Read more at: http://www.forbes.com/sites/bensimpfendorfer/2015/07/07/the-agility-factor-in-asias-competitive-markets/

Amalgamation of data, technology and marketing helps the manufacturing industry acquire new opportunities for earning profits. Data obtained from the customers are stored in various different systems like inventory, billing etc. And normally the manufacturers have to gather information from a common source thus having no competitive advantage in the market.
Industrial manufacturers are now gradually shifting from traditional marketing methods to data-driven ones so as to gain more prospects. Solutions such as Data-as-a-Service (DaaS) are influencing the Big Data ecosystem increasingly. It mines the appropriate data required from the Big Data sets. Patented Web Mining is also an effective and efficient way to find new prospects. Social media also actively helps in promoting the manufacturers.  Thus industrial manufacturers can increase their market share to a great extent by shifting to data-driven strategies.
Read more at: http://www.smartdatacollective.com/lbedgood/327457/how-manufacturers-can-use-big-data-acquire-new-customers

In order to achieve accuracy of understanding customer’s experience the company executives have to come up with varied surveys and several tricky questions. We can gain a more empathetic experience of customers experience with the appropriate amalgamation of observations, sensors, data and designs. Being aware of the users experience is very critical for the technology design.
There are certain emotions which cannot be explained by words. Sensors can pick up these stress signals, combine them with the available textual data and thus identify the emotional triggers of a customer’s experience.
The comprehensive data on the experience of the user can be gathered by observing the way user interacts with the product or application. It exactly determines how the design is performing and whether it needs improvement or not.
Read more at: http://www.informationweek.com/it-life/can-data-teach-us-empathy/a/d-id/1321022?

A metadata fabric that provides efficient data analysis and data driven decisions, is of great value to an enterprise which utilises IOT generated data. The metadata fabric presents data and analytics, together in a business consumable format and interface. The major types of metadata are maps, derivations and complex events. An enterprise’s first concern is the ability to create and store the metadata in a business friendly interface, which will also enable its exploration, its usage in data analysis and will accept updates with changing business trends. The metadata fabric needs to adapt itself to situations, where data values can change over time or appear in a fragmented manner or even encrypted, at times. The information, regarding the analyses performed by previous personnel needs to be a part of the metadata layer, available for successive employees. When users and systems are not able to search through, or update metadata, the metadata fabric is probably broken, and so it is, when data exists in disconnected islands. Read more at:

http://www.cio.com/article/2939114/data-analytics/the-grand-unified-theory-of-metadata-governance.html

Business decisions in modern times are being taken on the basis of appropriate data analysis and projections, which are generated by crunching the data. But business leaders need to stop and think once about the quality of the data; the most important factor because all the forecasts and predictions are done in the light of the data. So here are some points which should be kept in mind before starting data processing. They are: • The source of the data, i.e. survey/ public domain data etc. should be known correctly.

• Purpose of data collection: objective of data collection should be clear.

• Data collection method should not have any sampling bias.

• The nature and scope of the data should be clear. Read more at: Business decisions in modern times are being taken on the basis of appropriate data analysis and projections, which are generated by crunching the data. But business leaders need to stop and think once about the quality of the data; the most important factor because all the forecasts and predictions are done in the light of the data. So here are some points which should be kept in mind before starting data processing. They are: • The source of the data, i.e. survey/ public domain data etc. should be known correctly.

• Purpose of data collection: objective of data collection should be clear.

• Data collection method should not have any sampling bias.

• The nature and scope of the data should be clear. Read more at:

http://www.huffingtonpost.com/2015/05/27/simple-questions-about-data_n_7453668.html?ir=India&adsSiteOverride=in

Data analysis conventionally involves a series of process of collecting data from several sources and then analyzing them after some time. Normally these data come with a expiry time i.e. there is a specific short time by which these data needs to be analyzed, processed and utilized. With development in IT world we can no longer wait for data to be collected and then analyzed and processed. The need of the moment is to shift the Big Data nearer to the data source. Thus the value obtained from the data is more important than the way it is collected. Read more at: http://www.tibco.com/blog/2015/04/14/data-analytics-how-to-be-advanced-pervasive-and-invisible/

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/

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