Self-service analytics is an approach to data analytics that enables non-tech savvy users or business users to access data for more informed decision making.  For success in self-service analytics, employees should have the culture of using data to start, propagate or conclude every conversation. A few areas required to support this cultural change are, organizational readiness which will help in determining the type of self service tool required for the organization. Next is data readiness i.e. continuous feedback about data quality practices should be given. Third is data security readiness i.e. data security, compliance and data access should be carefully examined during making a transition to self-service analytics. Fourth is that users should be adaptable and willing to use new technology. And lastly, data shouldn’t be interpreted just by preparing charts instead it should be used to make theoretical interpretations. Read more at: https://www.blueoceanmi.com/blueblog/self-service-analytics-need-cultural-change/

With all the talk about predictive machine learning and deep learning applications, one can lose sight of the data engineering, some might call it data art that is needed to prepare the data to work on. Many questions go into the planning for deep learning applications like should the processing be disturbed; how much noise obscures the signal arriving from internet of things devices such as cell phones. In the case of mobile phone sensors, data preparation for deep learning applications can present unique problems, data preparation can involve considerable preprocessing. Insurance and other industries are entering the golden age of sensor data, but the data needs preprocessing because the data initially is very noisy. Given the Data, the algorithms will figure out the right transformations of the data. Read more at: http://searchdatamanagement.techtarget.com/news/450419925/Data-prep-for-deep-learning-applications-means-careful-planning

Many data analytics firms are working for the betterment of the farmers. These companies integrate satellite, weather, and IoT analytics with the agricultural sector. They use its proprietary machine learning and parallel computing techniques, to resolve complex relationships like crop growth and soil health. Using analytics farmers can opt for a smart sampling procedure using satellite – based crop clustering techniques, which reduces the time for identification of these plots and optimize their locations. While the former requires timely crop intelligence, crop insurance companies need highly accurate assessment of risk. The satellite imaging analytics serves two purposes: First, it ensures that the farmers receive a fair and immediate compensation for crop loss due to adverse climatic conditions. Second, it enables insurers to settle claims speedily due to the availability of data in near-real time without any manual intervention. Read more at: https://yourstory.com/2017/05/satsure/

Programming languages and Mathematical algorithms are not sufficient for a successful career in Analytics. Communication skill plays a vital role. Explaining the results of the analysis performed in a simple language is as important as ability to analyze the data. According to the author, the key aspects of communication are simplicity, narrative and action. Secondary research is another way to stand out in the analytics profession. What counts is the simplified presentation and explaining the consequences of several course of action to build a successful career in analytics. Read more at: https://www.forbes.com/sites/metabrown/2017/05/30/if-you-want-to-succeed-with-analytics-effective-communication-is-a-must/4/#1a1f627d4c44

According to a report, it was found that the global CRM market is expected to reach $81.9 billion by 2025, and in turn attributes this growth to the ability of today’s CRM solutions. This makes it easier for companies to understand and connect with their customers and so deliver a better customer experience. Nowadays, the in-store shopping experience is enhanced by leveraging cloud-based point-of-sale with store operations management systems and further, the new Commerce Cloud Store will deliver a mobile-first point-of-sale (POS) system for brick and mortar venues that will help store associates to engage with their customers and make predictive product recommendations that are based on a customer’s online and in-store shopping histories and interests. Read more at: http://it.toolbox.com/blogs/insidecrm/real-time-customer-view-for-best-cx-76707

Subsets of AI are diversifying and algorithms are growing advanced. AI had an alarming impact in many instances. Certain applications of AI are called black box because it is difficult to understand how the result have been generated. Decoding the black box technique involves optimizing a given function in isolation, and sharing it as necessary. This makes the work a lot easier and scales the data. Firms need to make people aware of AI's applications in order to make it more transparent. AI cannot be completely trusted with certain applications. In future, we have to embrace AI and develop trust on it because it has many advantages and black box is a positive step in this direction. Read more at: http://analyticsindiamag.com/making-sense-black-box-artificial-intelligence-trust-ai-completely/

Small businesses are vulnerable to hacking. Hackers attack smaller businesses assuming that they can quickly get in and steal customer data. So, to strengthen the customer data security the following can be followed. The first thing to keep in my mind, is it as data grows the security system should be improved. Next is building up the online sales. Once it can be shown that the threats of hackers and viruses are taken care of, customers will rely on the company more and thus consume online. Third is to use the right technology to protect the data. Installing some software which will constantly monitor the system and give alerts if someone tries to break into the secured information can be helpful. Additional protection measures such as authenticator tabs and biometrics should be considered. Fourth is that only technology shouldn't be relied upon. Fifth is risks shouldn't be underestimated i.e., companies should always be prepared beforehand and have proper security measures. Lastly, it is important for the companies to know where the data is located, this way, they can be prepared in the event of a natural disaster or if any other problem hits the location where the cloud servers are located. Read more at: http://www.analyticbridge.datasciencecentral.com/profiles/blogs/tips-for-reducing-fraud-and-bolstering-customer-data-security

Principal Component Analysis is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possible correlated variables into a set of values of linear uncorrelated variables called principal components. The goal is to explain the maximum amount of variance with the fewest number of principal components. PCA transforms the initial features into new ones that are linear combinations of the set of variables. For this analysis, first the original values should be normalized and the covariance matrix should be formed. The eigenvalues and eigenvectors should be calculated and the eigenvector with the highest eigenvalue has to be chosen. For the highest eigenvalue the data set matrix has to be multiplied and finally the mean can be put back which was removed in the beginning. However, if the original data set is correlated the solution can be unstable. Read more at: http://www.datasciencecentral.com/profiles/blogs/introduction-to-principal-component-analysis

In the time series, after isolating trends and periodicity, a normalized time series is left. To check whether the data follow some well known stochastic process, model fitting is done. If the model has an autocorrelation then it is de-correlated and after de-correlation it is checked whether it behaves like white noise, or not. The article further explains how to remove auto-correlation in a time series with the help of first order autocorrelation and linear algebra framework in PCA with an example as well. Read full article at: http://www.datasciencecentral.com/profiles/blogs/how-and-why-decorrelate-time-series

Data science and mathematics together makes learning more interesting. Passionate data scientists towards mathematics solve many modern math problems using data science. This article gives selection of 12 interesting articles, about mathematical problems, math-free algorithms and statistical theory. Most of them can be understood by the layman. Some of them include R code and some include processing vast amounts of data. Some of the articles are: Simple Proof of the Prime Number Theorem, Fascinating Facts and Conjectures about Primes and Other Special Number,Factoring Massive Numbers: Machine Learning Approach.

Read more at: http://www.analyticbridge.datasciencecentral.com/profiles/blogs/10-interesting-reads-for-math-geeks

The main responsibility of DevOps and IT operations teams include solving problems and facing challenges which is becoming tougher by each day. This is where real-time and centralized log analytics come to the rescue. It helps them in understanding the essential aspects of their log data, and easily identify the main issues. While Artificial Intelligence (AI) was a big thing a few decades ago, it is now being commonly used. As IT operations becoming more complex, AI is becoming a powerful and essential tool. One solution can be to have a platform that has collected data from the internet about all kinds of related incidents, observed how people using similar setups resolved them in their systems and scanned through your system to identify the potential problems. Cognitive Insights can be introduced, this technology uses machine-learning algorithms to match human domain knowledge with log data, along with open source repositories, discussion forums, and social thread. Read more at: http://readwrite.com/2017/05/15/artificial-intelligence-transform-devops-dl1/

Artificial intelligence (AI) is going to steal your job and. If we do not prepare now, we may face a future where AI runs free and dominates humans in society. The AI revolution is going underway. So, how to survive the AI revolution. First, recognize AI. It is already here. For example, Google suggestion, Facebook timeline ranking, YouTube suggestion are some of the examples of AI. Second, identify where it is growing. AI is particularly good at any task that requires an enormous amount of repetitive processing. If this is your job, then it is the time for looking for an alternative for survival. Third, plan an action for AI revolution. One thing you can do is oppose this AI or just make yourself comfortable with it. In other words, be ready to upskill where possible. AI can learn very well, but it cannot learn flexibly (yet). You can. There are new jobs now available that did not exist five years ago. So if you allow AI to grow, you will find that it will help to increase your standard of life given that you prepare well. Read more at: http://dataconomy.com/2017/05/survival-guide-ai-revolution/

Sentiment Analysis for Product Rating is the system that detects the hidden sentiments in the comments posted by the consumers and rates the product accordingly. This system uses a sentiment analysis method. This is an E-Commerce web system where the registered users will view and comment the product. And then the system will rank the product after analyzing the users’ comment. Comments will be compared by with the keywords stored in the system database. On this basis, system will specify whether the product is good, bad or worst. This application also works as an advertisement which makes many people aware about the product. This system is also useful for the users who need review about a product. Read more at: http://nevonprojects.com/sentiment-analysis-for-product-rating/

Big data may be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It can also help to improve the market and customer relations. By collecting data on your company, it is easier to know what the customer wants, the services they like and also helps in improving operations and make them more efficient and saves time and cost. Companies, big and small alike should find ways to mine all the information and use it to their advantage. For further information, please visit :

 http://it.toolbox.com/blogs/this-is-it/combine-big-data-and-your-business-75513

The Big Data analytics remains a highly significant factor when there is a point of mobile marketing. Every business is aware of the role of mobile apps and big data analytics in establishing a brand image and marketing their products. The apps's anywhere-anytime nature helped the businesses acquire more insights on the user data based on input, usage patterns, and the user behavior. This huge reserve of mobile user data is used further for the purpose of optimizing the mobile user experiences, to drive as well as build the mobile traffic, to drive more user interaction and engagement and to push the business conversion. Some critical facets need to keep business in their mind: i) Data driven approach to marketing must be cross disciplinary. ii) focus must be on the right KPIs and importance must be given to insights between lines than just numbers. Read more at: http://analyticsindiamag.com/mobile-apps-leverage-big-data-drive-sales-marketing/

Organizations do the required amount of substitution between 'defensive' and 'offensive' uses of data and also between control and flexibility in its use. Defensive data is about minimizing downside risk. Activities include ensuring acceptance with regulations and using analytics to identify and uphold fraud. Defensive efforts are applied to ensure the integrity of data flowing through a company's internal systems. Data offense emphasizes on supporting business goals like increasing revenue, profitability, and customer satisfaction. It includes activities which produce customer insights & market data to support managerial decision making. Each strategy has its own working infrastructure. Elements of data strategy  that should be taken into account are: Data definitions, Data ownership, data access, data assessment. Read more at: https://thefinancialbrand.com/65419/data-strategy-playing-offense-defense/

Microsoft is illuminating several places with big data and analytics offerings. In Finland, Microsoft along with CGI  developed a data driven smarter transit system, which saw Microsoft utilize the city's existing warehouse systems to create a cloud-based solution that could assemble and analyze travel data. Boston serves as another example where Microsoft is working to spread information about the variety of urban farming programs. Microsoft has also partnered with Athena Intelligence in developing the hill city of San Francisco. As a part of this, Microsoft is influencing Athena's data processing and visualization platform to gather valuable data about land, food, water, and energy.For further information.Read more at:

 http://analyticsindiamag.com/big-data-analytics-now-used-greening-planet/

Marketing Cloud integration assimilates all aspects of marketing in support of a company's market goals.To begin with,security and privacy are the fundamental building blocks of trust between a company and its prospects, customers, partners, investors and employees.Next,finance and monetization are the next most important fundamental to the successful relationship between all stakeholders.Marketing collaboration and marketing analytics also are indespansable for the effeciency of the marketing cloud architecture.Web, Content Management Systems, CRM and eCommerce Platforms are core marketing platforms.Careful evaluation of current capabilities and a realistic assessment of available resources  helps in appropriate  selection of the systems needed to enhance and optimize each of these core platforms and marketing mix tools.In many cases,the task of assessing these capabilities is beyond the focus of many marketers and executives.IT management will be called upon in these instances to implement the integrations needed to optimize marketing cloud operations.Read more at : 

http://it.toolbox.com/blogs/integrate-my-jde/marketing-cloud-integration-76575

Each and every sector of the economy is about personalized experiences. Business analytics and business intelligence is now a necessity. Data driven marketing strategies directly translate into conversions and revenues.  It is high time for the financial services industry to adopt the same.  The financial institutions are lagging in terms of consumer expectations and level of customization. Exclusive focus should be on their consumers and their specific needs.  In fact, successful implementation can add up to 14% to the annual revenue. Read more at: https://thefinancialbrand.com/65396/banking-data-analytics-marketing-personalization/