To become a digital expert, customer experience should be put above everything and methodologies should be found to adjust to the continuously changing demands. The following are some ways to do so. The first is to become a top-notch expert with industrialized IT services, where quality should be improved and it should be ensured that the product line is aligned with business needs. The next is to modernize, during which architecture must be planned and implemented. The last stage is Optimize in which improvements are made. The second is switching to agile operations to achieve maximum efficiency. Third, is to create an engaging experience for the consumers by designing a unique omni-channel approach, analyzing real-time data, thus improving purchase journey. Lastly, opportunities for digital services must be availed. The end result is the delivery of a truly digital experience. Read more at: http://www.datasciencecentral.com/profiles/blogs/enterprise-journey-to-becoming-digital

With increasing competition, a few ways to become a better marketer are suggested. First, along with authenticity and high personalization, adapting to customers' changing needs is very important. Companies need to impress customers buy their products so that they are willing to advocate. It is necessary to collaborate on defining the same shared target audience for an equal improvement in sales and marketing. Also, marketers need to have knowledge about every customer's lifestyle, their choices, and thus analyze how the company can make profit from them. These will help companies sell products according to customer needs and help them shift to outcome-based marketing, thus generating more profit. Read more at: https://blog.insideview.com/2017/04/28/3-marketing-nation-summit-takeaways-to-make-you-a-modern-day-marketer/

Quantum computing is the area of study focused on developing computer technology based on the principles of quantum theory, which explains the nature and behavior of energy and matter on the quantum level. In Quantum it can’t be clearly predicted which key element of the technology has entered commercialization and resulted in a massive change. Commercial applications like Temporal Defense Systems (TDS), Westpac, Commonwealth, and Telstra, and QuantumX are being adopted in Quantum computers by Lockheed Martin. Quantum computers can be used for simulation, optimization and sampling. Therefore, the most important action for data science is plotting how Quantum will disrupt the way we approach deep learning and artificial intelligence. Read more at: http://www.datasciencecentral.com/profiles/blogs/quantum-computing-and-deep-learning-how-soon-how-fast

Given the power of the consumers, their demands and the most extreme competitions in the market every business is fighting to survive. Recently the businesses have invested mostly in digital transformation. Since data play a very important role, retail businesses are using the following trends to analyze those data. Firstly, retailers are considering data from in-store technologies and analyzing them to measure their effect on consumer's decisions and thus providing the products demanded more by customers.  Secondly, by location analytics, geo-targeted push notifications are sent out to customers and products are arranged more effectively in the stores.  Thirdly, with the help of explanatory analytics and predictive analytics more demanded products are stocked up. And lastly, cross-platform analytics helps retailers understand the customer's demands and provide effective support to them. Therefore, we can say that data analytics are an essential part of growth. Read more at: http://www.datasciencecentral.com/profiles/blogs/top-data-analytics-trends-for-reatilers-of-2017

Fast data is the application of big data analytics to smaller data sets in real-time in order to solve a problem or create business value. The goal of fast data analytics is to quickly gather and mine structured and unstructured data so that customer experience can be improved by creating a more streamlined process for marketing strategies and customer service implementation. It has been observed that fast data analytics helped businesses turn their raw machine data into actionable insights by tracking transactions, identifying issues with hardware and software, and reducing customer complaints. It also helped in staying compliant with government regulations, avoiding preventable losses and improving the personnel’s efficiency by pinpointing errors. Thus, fast data analytics services significantly improve business’ customer experience by solving issues faster and more efficiently. Read more at: http://www.datasciencecentral.com/profiles/blogs/how-you-can-improve-customer-experience-with-fast-data-analytics?xg_source=activity

Social media analytics or SMA, is the practice of gathering data from social media websites and analyzing that data to make business decisions. The most common use of social media analytics is to mine customer sentiment to support marketing and customer service activities and turns the vast amounts of semi-structured and unstructured social media data into actionable business insights. Depending on the business objectives, social media analytics can take four different forms. The first two are reactive in nature, while third and fourth are proactive in nature. First is descriptive analytics. Descriptive analytics gather and describe social media data in the form of reports, visualizations, and clustering to understand a well-defined business problem or opportunity. Second is diagnostic analytics, it can distill this data into a single view to see what worked in the past campaigns and what didn't. Third is predictive analytics, it involves analyzing large amounts of accumulated social media data to predict a future event. And the last one is prescriptive analytics, it suggests the best action to take when handling a scenario. Read more at: http://www.analyticbridge.com/profiles/blogs/4-types-of-social-media-analytics-explained

Random forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. It is called random because there are two levels of randomness; at row level and at the column level. In spite of it being such a convenient process to deal with large datasets it has a few disadvantages. In case of smaller datasets linear regression is a better method than this. Next is that any relationship between the response and independent variables can't be predicted. Also, this process is very cumbersome and can't take values from outside the datasets. Even then, random forest is advantageous because keeping the bias constant it can decrease the variance in the datasets and it helps us ignore most of the assumptions like linearity in datasets. Read more at: http://www.datasciencecentral.com/profiles/blogs/random-forests-explained-intuitively

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/

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

Data federation and data virtualization are both terms that have been coined to describe the process of collecting and compiling data. Data virtualization is a superset of the ten-year old data federation technology. It also includes the advanced capabilities of performance optimization as well as self-service search and discovery. Data federation is a distributed access to data residing in multiple systems with the purpose of joining the data together as if it came from the same system. Data virtualization on the other hand, has evolved from data federation by improving performance and adding other advanced capabilities such as self-service search and discovery. Advanced data virtualization products like the Denodo Platform have evolved to include dynamic query optimization techniques to determine the best query execution plan thus delivering optimal performance times.  Read more at: http://www.datavirtualizationblog.com/leading-analysts-mixing-data-federation-data-virtualization-terms/

A virtual private network (VPN) is a network that is constructed using public wires - usually the Internet - to connect to a private network, such as a company's internal network. There are several ways how business benefits from VPN implementation. First, regarding defense against malware, a VPN can be installed in unprotected office devices to keep the communications private, secured and anonymous, i.e. it improves security. The VPN service enables role-based access to data, meaning, it enables one to limit sensitive data flow to only the authorized personnel. Third VPN helps to go anonymous online so that internet censorship can be bypassed and foreign contents can be accessed. The anonymity quotient also means a robust wall against the hackers as no longer they would be able to track the actual IP. Fourth, a VPN can remote access to vital data this way, one can assure the smooth reach to mission-critical business data. And finally, VPN services assure low maintenance costs post implementation. Although there is no dearth of VPN service providers today, yet not all would be perfectly compatible. Read more at: http://www.datasciencecentral.com/profiles/blogs/how-does-vpn-can-help-your-business

Data Lake helps data scientists by reducing their time taken to gather data and start their real work of data analysis. Copying data physically to one centralized environment may be problematic because storing big data can be costly, copying of data can be prohibited. Metadata describing the data is commonly not copied along with the data and therefore not available to the data scientists. Also, technical and organizational management of a data lake is required. Since data scientists ask for easy and quick data access, a more practical solution to it is a logical data lake. A logical data lake, hides where the data is physically stored and whether it has been copied or not. Logical data lakes can be developed with data virtualization servers such as the Denodo Platform. While, copying and physically storing the data twice is the default approach for the physical data lake, it's optional for the logical data lake. It offers access to data without copying if required and to copy data when needed.  Thus, logical data lake is a better solution for data scientists. Read more at: http://www.datavirtualizationblog.com/data-scientists-physical-data-lakes/

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