Big Data Analytics 简明教程
Big Data Analytics - Core Deliverables
大数据分析需要处理和分析大型和多维数据集，发现隐藏的模式、相关性、见解和其他有价值的信息。如大数据生命周期中所述，下图中提到了大数据分析的一些核心交付成果-
Big data analytics entails processing and analysing large and diverse datasets to discover hidden patterns, correlations, insights, and other valuable information. As mentioned in the big data life cycle, some core deliverables of big data analytics are mentioned in below image −
Machine Learning Implementation
这可能是一种分类算法、回归模型或细分模型。
This could be a classification algorithm, a regression model or a segmentation model.
Recommending System
目标是开发一个能够根据用户行为推荐选项的系统。例如 - 在 Netflix, 上，根据用户对特定电影/网络剧集/节目的评分，会推荐相关的电影、网络剧集和节目。
The objective is to develop a system that can recommend options based on user behaviour. For example – on Netflix, based on users' ratings for a particular movie/web series/show, related movies, web series, and shows are recommended.
Dashboard
业务通常需要工具来可视化聚合数据。仪表板是对数据的图形化表示，可以根据用户的需要进行过滤，并将结果反映在屏幕上。
Business normally needs tools to visualize aggregated data. A dashboard is a graphical representation of data which can be filtered as per users' needs and results are reflected on screen.
例如，公司的销售仪表板可以包含过滤器选项，以按国家、州、区、分区或销售产品等方式可视化销售情况。
For example, a sales dashboard of a company may contain filter options to visualise sales nation-wise, state-wise district-wise, zone-wise or sales product etc.
Insights and Patterns Identification
大数据分析可以识别数据中可用于做出更明智决策的趋势、模式和关联。这些见解可能是关于客户行为、市场趋势或运营低效的。
Big data analytics identifies trends, patterns, and correlations in data that can be used to make more informed decisions. These insights could be about customer behaviour, market trends, or operational inefficiencies.
Ad-Hoc Analysis
大数据分析中的即席分析是一个即时或自发分析数据以回答特定、直接查询或解决即席询问的过程。与依赖于预定义查询或结构化报告的传统分析不同，即席分析允许用户交互式地探索数据，而无需预定义查询或报告。
Ad-hoc analysis in big data analytics is a process of analysing data on the fly or spontaneously to answer specific, immediate queries or resolve ad-hoc inquiries. Unlike traditional analysis, which relies on predefined queries or structured reporting, ad hoc analysis allows users to explore data interactively, without the requirement for predefined queries or reports.
Predictive Analytics
大数据分析可以通过分析先前的数据预测未来的趋势、行为和事件。预测分析帮助组织预测客户需求、估计需求、优化资源和管理风险。
Big data analytics can forecast future trends, behaviours, and occurrences by analysing previous data. Predictive analytics helps organisations to anticipate customer needs, estimate demand, optimise resources, and manage risks.
Data Visualization
大数据分析需要以图表、图形和仪表盘等可视化形式呈现复杂的数据。数据可视化使利益相关者能够通过图形化方式更好地掌握和分析数据见解。
Big data analytics entails presenting complex data in visual forms like charts, graphs, and dashboards. Data visualisation allows stakeholders to better grasp and analyse the data insights graphically.
Optimization and Efficiency Improvement
大数据分析使组织能够通过识别改进领域和低效率来优化流程、运营和资源。这可能包括优化供应链物流、简化制造流程或改进营销策略。
Big data analytics enables organisations to optimise processes, operations, and resources by identifying areas for improvement and inefficiencies. This could include optimising supply chain logistics, streamlining manufacturing processes, or improving marketing strategies.
Personalization and Targeting
大数据分析使组织能够通过分析大量客户数据，根据个人偏好和行为来个性化其产品、服务和营销活动。这种个性化策略增加了客户满意度和营销投资回报率。
Big data analytics allows organisations to personalise their products, services, and marketing activities based on individual preferences and behaviour by analysing massive amounts of customer data. This personalised strategy increases customer satisfaction and marketing ROI.
Risk Management and Fraud Detection
大数据分析可以检测出表明存在欺诈活动或潜在威胁的异常情况和模式。这在金融、保险和网络安全等企业中尤其关键，及早发现可以避免大量损失。
Big data analytics can detect abnormalities and patterns that indicate fraudulent activity or possible threats. This is especially crucial in businesses like finance, insurance, and cybersecurity, where early discovery can save large losses.
Real-time Decision Making
大数据分析可以提供实时或接近实时的见解，使企业能够根据数据做出决策。这种能力在需要快速决策以利用机会或管理风险的动态环境中至关重要。
Big data analytics can deliver insights in real or near real-time, enabling businesses to make decisions based on data. This competence is critical in dynamic contexts where quick decisions are required to capitalise on opportunities or manage risks.
Scalability and Flexibility
大数据分析解决方案旨在管理来自不同来源和格式的大量数据。它们提供可扩展性以支持不断增长的数据量，以及灵活性以应对不断变化的业务需求和数据源。
Big data analytics solutions are built to manage large amounts of data from different sources and formats. They provide scalability to support increasing data quantities, as well as flexibility to react to changing business requirements and data sources.
Competitive Advantage
有效利用大数据分析能够使公司获得竞争优势，使其能够创新、优化流程、更好地了解消费者和市场趋势。
Leveraging big data analytics efficiently can give firms a competitive advantage by allowing them to innovate, optimise processes, and better understand their consumers and market trends.
Compliance and Regulatory Requirements
大数据分析可以帮助企业确保遵守相关法规和标准，通过分析和监控数据以满足法律和道德要求，尤其是在医疗保健和金融行业。
Big data analytics could help firms in ensuring compliance with relevant regulations and standards by analysing and monitoring data for legal and ethical requirements, particularly in the healthcare and finance industries.
总体而言，大数据分析的核心交付集中于利用数据来推动战略决策制定、提高运营效率、改善客户体验以及在市场中获得竞争优势。
Overall, the core deliverables of big data analytics are focused on using data to drive strategic decision-making, increase operational efficiency, improve consumer experiences, and gain a competitive advantage in the marketplace.