Big Data vs. Small Data Strategies for Next Generation Business

প্রায় এক বছর পর আমার একটি গবেষণাপত্র প্রকাশিত হল। প্রকাশক যুক্তরাষ্ট্রের মাইন্ডকমার্স পাবলিকেশন। আমার গবেষণার বিষয় ছিল Big Data vs. Small Data Strategies for Next Generation Business তথ্য বা ডাটা নিয়ে প্রতিষ্ঠানগুলো কী করতে পারে এবং ব্যবসা মডেল কেমন হতে পারে তা নিয়ে এই গবেষণা। বড় ডাটা ও ছোট ডাটা নিয়ে অনেক ব্যবসা করা যায়। এটি আমার তৃতীয় গবেষণাপত্র। আমি শিগগিরই চতুর্থটির জন্য কাজ শুরু করব। সবার কাছে দোয়া চায়। গবেষণাপত্রটির বিষয়বস্তু দেখে নিতে পারেন। আশা করি এই গবেষণাপত্রে আমার নাম না থাকলেও আন্তর্জাতিক বেশ কিছু মিডিয়ায় এই বিষয়টি নিয়ে চর্চা হবে। The data market is booming with an ever increasing valuation. In the virtual data ocean, Big Data and small data are complementary strategies at one level and choices of scale/scope at a different level. While Big Data holds distant promises, leveraging “small data” can provide great benefits to small-to-medium business (SMB) as well as large corporations. Simply stated, the cost-performance barrier leaves many lucrative markets inadequately served by Big Data approaches. Carefully chosen data solutions and models should be more accurate, objective, and ultimately lead to improved ROI especially for more near-term time horizons and/or companies with limitations in scale/scope. This research provides the reader with an understanding of data management issues, challenges and opportunities relative to Big Data and small data approaches. The report includes analysis of small data practices and emerging small data business models and scenarios. This report evaluates strategies, considerations, and planning for a so-called “small data” strategy. It also compares and contrasts Big Data vs. small data strategies in terms of company capabilities and focus. The report also evaluates the future of Big Data including emerging business models and practices. Target Audience: SMB of all types Big Data companies Social network companies Telecom service providers Data services and analytics companies Cloud and telecom infrastructure providers Table of Contents: 1.0 EXECUTIVE SUMMARY 5 2.0 DATA TREND ANALYSIS 7 2.1 DATA SEARCH 7 2.2 DATA QUALITY 8 2.3 TIMED DATA DELIVERY 9 2.4 DATA STORAGE CAPACITY 10 2.5 DEFINING BIG & SMALL DATA INCLUDING SOURCING 12 2.6 BIG DATA VS. SMALL DATA DIFFERENTIATION 13 2.6.1 DATA MINING 13 2.6.2 DATA PURPOSE 13 2.6.3 KEY DECISIONS 13 2.7 DECISION PARAMETERS FOR BIG VS. SMALL DATA 14 2.8 USABILITY OF SMALL VS. BIG DATA IN MARKETING 15 3.0 BIG DATA MARKET ADVANCEMENT AND FORECAST 2014 - 2020 17 3.1 BIG DATA MARKET TREND 17 3.1.1 TRENDS IN THE ADVANCE OF BIG DATA 17 3.1.2 BIG DATA ANALYTICS 18 3.1.3 BIG DATA MARKET FORECAST 18 3.1.4 CASE STUDIES & STRATEGIC RECOMMENDATION FOR START-UP BUSINESSES 19 3.2 BIG DATA VALUE CHAIN 22 3.2.1 CREATING TRANSPARENCY 22 3.2.2 ENABLING EXPERIMENTATION 22 3.2.3 SEGMENTING POPULATIONS TO CUSTOMIZE ACTIONS 23 3.2.4 REPLACING/SUPPORTING HUMAN DECISION MAKING WITH AUTOMATION 23 3.2.5 INNOVATING NEW BUSINESS MODELS, PRODUCTS, AND SERVICES 23 3.3 BIG DATA IMPLEMENTATION RECOMMENDATIONS 23 3.3.1 FOR VENDOR COMPANY 23 3.3.2 INDUSTRY VERTICAL 24 3.3.3 CONSIDERATION FOR INDUSTRY STRUCTURE 24 3.4 BIG DATA TECHNIQUES 24 3.4.1 A/B TESTING 25 3.4.2 ASSOCIATION RULE LEARNING 25 3.4.3 CLASSIFICATION 25 3.4.4 CLUSTER ANALYSIS 25 3.4.5 CROWD SOURCING 26 3.4.6 DATA FUSION AND DATA INTEGRATION 26 3.4.7 DATA MINING 26 3.4.8 OTHER TECHNIQUES 26 3.5 BIG DATA TECHNOLOGY 27 3.6 BIG DATA INDUSTRY INSIGHTS & ANALYSIS 27 3.6.1 US HEALTHCARE 28 3.6.2 EUROPE PUBLIC SECTOR ADMINISTRATION 28 3.6.3 GLOBAL PERSONAL LOCATION DATA 28 3.6.4 US RETAIL INDUSTRY 28 3.6.5 GLOBAL MANUFACTURING INDUSTRY 28 3.6.6 ANALYSIS 28 3.7 CASE STUDIES 29 3.8 EMERGING BUSINESS MODELS 30 3.8.1 AGGREGATING AND SYNTHESIZING PATIENT CLINICAL RECORDS/CLAIMS 30 3.8.2 ONLINE PLATFORMS AND COMMUNITIES 30 3.8.3 PUBLIC HEALTH 31 3.8.4 RETAIL INDUSTRY 31 3.8.5 PLACEMENT AND DESIGN OPTIMIZATION 32 3.8.6 PRICE COMPARISON SERVICES 33 3.8.7 WEB-BASED MARKETS 33 3.8.8 PERSONAL LOCATION DATA 33 3.8.9 SMART ROUTING 34 3.8.10 AUTOMOTIVE TELEMATICS 34 3.8.11 MOBILE LOCATION-BASED SERVICES 35 3.8.12 SHOPALERTS 35 3.8.13 CIVIC POWERED MODEL 35 4.0 SMALL DATA 37 4.1 DIFFERENCE VS. RELATIONSHIP WITH BIG DATA 37 4.2 SMALL DATA TECHNOLOGIES 38 4.3 HOW TO ANALYZE SMALL DATA? 38 4.4 HOW BANKS ARE LEVERAGING DATA 39 4.5 SMALL DATA MODELING IN THE CLOUD 39 4.6 SMALL DATA PROBLEMS IN MASS MARKET 40 5.0 SMALL DATA DRIVEN EMERGING BUSINESS MODELS 41 5.1 SOCIAL NETWORK 41 5.1.1 CASE STUDY: RENREN 41 5.2 LITTLE DATA CONTROLLING 42 5.3 KEY CONSIDERATION FOR DELIVERING SMALL DATA STRATEGY 43 5.4 SMALL DATA FRAMEWORK 44 5.5 SMALL DATA PRINCIPLES 45 5.6 SMALL DATA WITH BIG IMPACT 45 5.7 CASE STUDIES 45 5.7.1 THE REAL DELI 45 5.7.2 GOOGLE 46 5.7.3 ADOBE 46 5.7.4 FOURSQUARE 47 5.7.5 TWITTER 47 5.7.6 ATTIVIO 47 5.7.7 OTHER COMPANIES 47 5.8 RECOMMENDATION 47 5.8.1 FOR E-COMMERCE COMPANY 47 5.8.2 FOR MULTI-STORE CHAINS 48 5.9 GUIDELINES FOR SMALL DATA BEST PRACTICES 48 6.0 CONCLUSIONS 49 List of Figures Figure 1: Data Search Trends in Google 2009 – 2013 7 Figure 2: Global Data Search, Generation, and Delivery Trends 9 Figure 3: Global digital vs. Analog Data Storage Capacity 1986 – 2021 11 Figure 4: Big Data Market Value 2011 - 2020 19 Figure 5: Big Data Value Chain 22 Figure 6: Small Data Implementation Framework 45 List of Tables Table 1: Decision Points for using Big Data 14 Table 2: How Small Data is Different 37