Unit IV Big Data Analytics Social media analytics

Unit IV Big Data Analytics ü Social media analytics ü Text mining ü Mobile analytics ü Roles and responsibilities of Big data person ü Organizational impact ü Data analytics life cycle ü Data Scientist roles and responsibility ü Understanding decision theory ü creating big data strategy ü big data value creation drivers ü Michael Porter’s valuation creation models ü Big data user experience ramifications ü Identifying big data use cases

Social media analytics “Social media analytics is define as it is a process of collecting data from social media websites and examining the data with the help of social media analytics tool for taking business related decisions. “ v Social media analytics Life Cycle

o Extract first phase of social media analytics, responsible for extracting business related data Data Extraction have 2 different scope a) Define set of keyword or search term (product or brands) b) Defined set of social media profiles( likes or post ) o Analyze attempt to clean and make sense of gathered data gives suggestion for possible outcomes of every decision(indexing. . ranking) finding insight useful in various phase like product development, customer support, sales

o Participate includes the findings from listening exercise to feed input into social media plans for example, identifying popular topics related to brands once there is active participation, performance assessment improves o Assess identify best practices from participation experience goal is to achieving cost and value impact on business

v Advantages of Social Media Analytics 1. Competitive Advantage 2. Learn From the Customers 3. Improve Product and Services (includes how customer use product, what issues faced, customers view about company/product) (customer may have efficient solution to issues) (key objective, tweets, blogs, comments, complaints use to improve performance)

Text Mining “Text mining is define as it is a process of obtaining information from unstructured text” Ø Text involved in text mining Things involved in Text Mining 1. Information retrieval 2. Natural language processing 3. Information extraction 4. Data mining

v Typical Application for Text Mining Anayzing surveys responses in review response, it includes variety of questions related to topic under analysis finding insight useful in various phase like product development, customer support, sales Automatic processing of emails, msg eg. filter junk mails based on some keywords

Mobile Analytics “Mobile Analytics can be defined as it is used for capturing the data coming from mobile app, web app visitors, and website to assist companies get better engagement, and conversation” Ø Working of Mobile Analytics useful for tracking distinct user to record their behaviours it use Software Development Kit(SDK) technology for tracking It normally track the Data related events, page view, count of visits, visitors, source data, location, information Login/Logout For eg. How long user stay on website? How many features interacted? Where visitors encounter problem?

Ø Types of Mobile Analytics 1. Advertising/Marketing Analytics 2. In-App Analytics 3. Performance Analytics (Success of app depend on company capable to attract the correct type of user: those can install them, remain engaged and contribute to financial component) (apart from marketing App must satisfy expectation of user) (used to deal with actual performance of app App complexity, Hardware variation)

Roles and Responsibilities of Big Data person o MIS Reporting Executive includes the findings from listening exercise to feed input into social media plans for example, identifying popular topics related to brands once there is active participation, performance assessment improves o Business Analyst attempt to reduce the gap between business and IT BA gives solutions that are technology-based o improve business processes, such as distribution or productivity BA recognizes business requirements, crystalize the data for better understanding, modifications. use predictive, prescriptive and descriptive analyses to convert complex data into easily understood format

o Data Analyst includes the findings from listening exercise to feed input into social media plans for example, identifying popular topics related to brands once there is active participation, performance assessment improves can perform designing and deploying algorithms, extrapolating data with help oh computer model training code identifying risk o Statistician gather, arrange, present, analyse and understand data to achieve valid conclusions and make right decision mostly works in finance, market research, product development, transportation etc maintain database and statistical programs, guarantying data quality and devising new programs , models and tools.

o Data Scientist DS makes data crunches and applies some rules on it DS responsible for understanding business challenges, generating insights of data Along with predictive analysis, they use coding to filter huge amount of unstructured data to obtain insights DS clean, manage and structured data from different sources o Data Engineer/Data Architect DE make sure that organization’s big data ecosystem is running with no malfunction Responsible for developing, constructing, testing and maintaining highly scalable data management system gather, store data and add modern data management technologies

o Machine Learning Engineer MLE need to design and implement ML applications/algorithm such as anomaly detection, clustering, classification or prediction obtain better data quality by use of tooling, optimisation and testing MLE have to check reliability of ML system in organization o Big Data Engineer BDE perform activities related to big data Develop, maintain, test and estimate big data solution inside organizations must have exp of working on technologies which are based on Hadoop such as Hive, Mapreduce, Mongo. DB or Cassandra