This article brings images from my work modeling with Mathematica, my experience as a Business Analyst and also my doctorate lessons. For me, the borders between a properly executed Business Intelligence and Data Science (with substantive knowledge in Management) are fuzzy. See the picture below: What is a Data Scientist ? In my understanding, someone can be a data scientist according to his domain expertise: Business management, physics, computer science, etc. DATA SCIENCE AND BUSINESS INTELLIGENCE PHASES 1) UNDERSTAND PROCESSES First of all, really understand the context, processes of the business: familiarity with technology, employees and daily routine 2) FINANCIAL ANALYSIS Second, establish business needs (among them, $$$). - Sales/Revenue - Net Worth - Gross margin - Net profit - Losses - Indexes: ROI, ROA, ROE, EBITDA, inventory turnover, liquidity, financial leverage, debt, assets and liabilities (short term and long term), horizontal and vertical balance analysis 3) DEFINE DATABASE ARCHITECTURE AND METHODOLOGY OF DATA COLLECTION AND EXTRACTION Third: a) Define database architecture to provide functionality, reliability, security and ability to provide valuable data for decision making. b) establish a methodology of data collection, sampling and market research, sources of data and KPIs in order to get a reliable data analysis provided with validity. 4) COLLECT DATA From different sources: a) Customized market research b) CRM Database: sales, clients, suppliers and processes c) Website d) Online Advertising e) Employees f) Big Data - Facial recognition - Speech recognition - Unstructured data - Structured data - Images - Social Media 5) ANALYZE DATA You can use Excel, R, SAS, Mathematica, SPSS, Pyhton 5.0. Data preparation: work on missing values, outliers (I usually analyze deeply individuals with values more than 3 standard deviations), normality of data, skewness (the 1/N trick), kurtosis (the log trick), sampling. Prepare data properly so that you can have a reliable analysis. 5.1. Descriptive statistics: a) Market Research and Database: quality perception, source of clients, demographics, sales, profit, repurchase intentions, profitable clients, profitability per sales channel, losses, evolution of KPIs over time, sales per state/neighborhood, efficiency of employees and sales force, employee performance b) Social Media: popularity, sentiment analysis, references, associations, conversions, mentions, influencers. You can use Python for unstructured data analysis (text). c) Website: visits, paths, time spent, clients' demographics, OS, enter pages, leave pages, contact forms filled, popularity, page rank d) Online advertising: bids, keywords, conversion rate, effective contacts, ROI, clients' demographics, competition strategy And here: https://www.linkedin.com/pulse/social-network-analysis-based-callsemails-rubens-zimbres 6) DEVELOP SIMULATION MODELS本帖隐藏的内容
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