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Big Data Analytics

In this digital era, more and more things and activities are recorded as data. Data size is exponentially growing and data variety is continuously increasing. However, most of data is unstructured raw data which does not have much value to businesses. Big data analytics is to analyze large size of data in various forms from various resources and turn massive amount of raw data into valuable information, strategies, and actions. Big data usually means gigabyte and terabyte in size. Although calculation principles are the same regardless of data size, working processes are very different between big data and small data. Big data analytics requires sophisticated computation algorithms to process data step-by-step toward goals for business solutions. In order to facilitate the entire process, high speed computers, large data storages, advanced analytics software, sophisticated computer programs, and efficient data transfer and display interfaces are necessary. 


Applications of big data analytics have been used in many fields. In information search and online shopping, people expect to see results instantly and to complete purchase transactions at real-time so that data computation and transfer should be super fast and real-time. In consumer based products and services industries (like retail, healthcare, and finance), customer retention is a top priority. Predictive modeling as part of big data analytics has been used to predict probabilities of customer churning and to develop strategies to improve customer loyalties. In healthcare industry, prevention medicine can use big data analytics to predict possible diseases based on many factors so as to take immediate preventive actions before diseases occur. In credit card industry, predictive analytics has been used to detect credit card frauds so as to take quick remedy actions. Since data analytics, especially predictive analytics, is very complex and labor intensive work, it needs deep thinking and specific training and skills in order to put massive amount of data and business objectives together to find meaningful insights, and develop actionable solutions and smart AI applications. Thanks to modern information technologies which make big data analytics feasible and affordable. The modern information technologies are facilitating analytical results to be quickly produced, instantly interactive, artfully displayed, and intelligently responsive as soon as analytical processes have been developed and implemented. 

Predictive Modeling

Predictive modeling is a process of developing statistical models and using the models to predict future events and discover relationships among factors based on historical data. Predictive modeling has been widely used in retail and banking in customer loyalty management and credit card fraud detections. In recent years, predictive models have been successfully applied to individual movie selections. Many types of statistical models can be used for predictions. However, commonly used models are regression and logistic regression models. Regression models are used to predict continuous numbers such as individual customer future spending, number of online purchases, and product sales. Logistic regression models are for predicting probabilities of future purchases, coupon redemptions, or credit and loan defaults at individual level. For Artificial Intelligent BI apps, predictive models can be used to do predictions. If you are interested in adding predictive modeling in your business analyses, we are pleased to help. 

Machine Learning

Machine learning is a process for computer to automatically train models from historical data and use the models to predict future events automatically. As new data is fed, the modeling algorithms automatically train statistical models in order to fit new data. The process keeps going so that the models are updating to produce updated outcomes. There are 3 types of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is for solving problems with target values which are the directions of predictions. Regression and classification analyses fall into supervised learning. Unsupervised learning has no targets so computers search for the best fitted groups from input entries. Clustering analysis and Bayesian analysis can be unsupervised learning. Reinforcement learning has no clean targets but has indicators for computers to learn and achieve the best results, such as neural network.  In general, machine learning has six components:

  • Statistical Methodologies, such as classification, linear models, Bayesian probability, and neural network, are foundations of machine learning. 
  • Modern Computers and Information Technologies facilitate data following from input to output, display analytical results, and interact with users. 
  • Statistical Analysis Software, such as Python, SAS, and R, handles lengthy complicated statistical computations which is impossible to be performed by a human. 
  • Efficient Computing Programs with Sophisticated Algorithms carry data step-by-step toward solving problems and finding solutions.
  • Data Availability ensures continuous data feeding to produce updated results from the machine. 
  • Human Intelligences and Instructions  make machines to do the right jobs.

Automatic Online Recommendations

Automatic online recommendation has been widely used in internet marketing. As users select products to review, other products are displayed on computer screens. This marketing method was initially used at Amazon.com for book sales. Online product recommendation has been adapted on many websites in order to generate more sales. A number of data analysis techniques can generate recommended products lists. Some work well while others may guide to unpleasant routes. Through consumer insights analytics to develop innovative methodologies for better predictions, automatic recommendations can be more effective and helpful in term of generating more sales for retail and also helping customers find more desirable products.  

Consumer Insights Analysis

Today's market is a consumer centric market. Understanding of consumer shopping behaviors and preferences is critical to retain customers and gain more customers. Consumer insights analysis is to gather consumer shopping data and other data such as survey and online review data to analyze for the answers of what consumers want and not want, where people shop, when and why and how they shop, what price ranges and promotion strategies can result in more profitable sales, and how to engage your customers with your products and services. Knowing consumer insights can help retailers and service providers to provide better products and services with reasonable prices and create large loyal customer bases. 

Market Data Analysis

Market is a complicated system. Retailers, customers, and products are the main players in the market system. Within the system, many other factors can be mechanisms for getting the system running. Products, price, location, promotion, advertisement, competition, etc are dynamic components so retailers and service providers can work around to generate best offers to fit their customer needs in the marketplace. 


Market data analysis is to analyze products sales and associations with other factors such as prices, promotion, advertisement, competition, time period, demographics, and location. Because general market data analysis is to give overviews of what is going on about the businesses but not to intent to find out WHY and HOW for the causes and solutions, such information is very intuitive and useful to track and evaluate market performance of products and services at various detail levels. The analytical results are usually presented in tables and charts. Online market analytical reporting tools provide retailers, CPG manufacturers, and service providers with easy to use tools to view the market performance for their products and services.