数据科学的冰山一角

　　数据科学不仅仅是当今商业世界的流行词，它正在重新定义企业与客户的互动方式。
　　无论是哪个部门或行业——零售、保险、制造业、银行业、旅游业——每个大型企业都有自己处理数据科学的方式。他们必须这么做。数据无处不在。这是新的黄金，挖掘数据对任何企业的成败都至关重要。
　　数据可以让你获得区分竞争对手的那种信息。数据驱动型公司为客户提供更好的服务，并做出更好的决策——所有这些决策都有数据的支持。
　　数据科学是商业世界的下一个进化，那些不能适应这个新现实的将不复存在。另一种选择是灭绝。
　　这就是一家欧洲时尚和服装零售连锁店面临的命运。它成立于20世纪80年代初，建立了以客户为中心的高档亲身购物体验的传统。在线零售商的出现和激增对其业务造成了重大打击。当它的实体店开始陷入困境时，它本可以接受自己的命运，进入历史的垃圾箱。
　　相反，它拥抱了数字化。
　　该公司以保持积极的客户体验为重点，计划进行全渠道数字化转型，以管理客户，收集数据，并提供客户所需的产品和服务。
　　它开始于启动电子商务渠道和建立CRM系统来管理客户，并通过忠诚度计划收集数据。为了保持他们以客户为中心的商业精神，他们专注于发展专门的创新能力，以确保为消费者提供他们想要的产品和服务。最后，他们转向数字化客户流程和优化客户旅程。
　　如今，这家时尚零售商保留着自己的实体店，让购物者可以看到和体验它提供的系列。在线商店被用作与一部分客户互动的沟通渠道，并了解他们的需求和想要的东西。
　　为了保持这种新方法，我们创建了8个数字团队，并且测量了所有可以测量的东西。这种数字化转型使业务能够将90%的收入追溯到终端客户。
　　组建团队
　　对于那些还没有进入数据科学领域的公司，或者在这个领域刚刚迈出第一步的公司，第一个也是最重要的建议是保持谦逊，承认这不是你一个人能做的事情，并召集一个专业团队。
　　数据科学是一个复杂的领域，要正确使用它，需要工程师、科学家和分析师开发出能够最大限度地识别、收集、评估和利用数据的人工智能平台。他们可以制定策略，确定所需的数据类型，收集数据的最佳方法，收集信息所需的系统，以及如何确保数据干净和可用，以便将其货币化。
　　该团队还可以开发支持数据捕获和收集所需的基础设施，包括人工智能或机器学习平台和用于大型计算机存储容量的云平台。
　　云平台是关键。它实现了数据的快速部署，并大大减少了获得对业务及其客户有价值的见解所需的时间。分析工程师可以构建可靠的数据管道，支持自助报告和可视化。
　　但是，观察数以百万计的接触点，并试图找出如何从中提取有意义的信息可能是一项艰巨的任务。数据驱动不仅仅意味着简单地解锁数据、存储数据并让每个人都能访问。它是关于从收集到的信息中提取见解来预测未来的见解，建议在短期、中期和长期进行投资，减少客户流失，预测需求，优化物流链或自动化业务流程。
　　在最有用的时候，数据科学从大型数据集中提取不明显的模式，如购买、预订、索赔或银行交易，以帮助企业做出更好的决策。
　　挖掘采购数据
　　了解客户是任何企业的基本原则，客户购买模式的历史数据不仅是最常见和最容易获取的数据集，也是最重要的数据集之一。它能够预测未来的需求，并为影响未来消费者的选择提供有价值的见解。
　　客户关系管理(CRM)系统是有效使用数据科学的良好起点。零售商可以使用这些数据来识别具有相似行为和品味的顾客群体，并更好地了解经常购买的产品。华东CIO大会、华东CIO联盟、CDLC中国数字化灯塔大会、CXO数字化研学之旅、数字化江湖-讲武堂，数字化江湖-大侠传、数字化江湖-论剑、CXO系列管理论坛（陆家嘴CXO管理论坛、宁波东钱湖CXO管理论坛等）、数字化转型网，走进灯塔工厂系列、ECIO大会等
　　挖掘用户数据
　　随着数据科学的发展，客户交互变得更加个性化。与建立关于群体、特定市场或地区的广泛概况不同，关注的焦点越来越个人化。
　　流媒体服务使用数据来改善用户体验。他们向观众推荐他们的算法确定的个人可能喜欢的书籍。一个简单的假设是，这只是基于观众之前可能看过的内容。例如，因为你喜欢Tom Cruise主演的这部动作片，也许你也会喜欢Tom Cruise主演的另一部动作片。
　　然而，实际情况要复杂得多。流媒体将从分析来自世界各地的大量用户数据构建的原型档案开始。然后，它会根据个人的观看模式(标题、类型、演员、季节性)，将这些内容与世界各地的人结合在一起，以及他们正在观看的内容，得出自己的建议。
　　挖掘旅行数据
　　旅游和酒店业正在依靠数据科学帮助其从大流行中恢复过来。
　　几乎没有企业能幸免于大流行的负面影响，但旅游业受到了重创。疫情爆发前，全球机场运营市场价值约为2210亿美元。在疫情迫使边境关闭，娱乐航空旅行几乎全部关闭后，这一数字暴跌至946亿美元。2021年略有改善，达到1302亿美元，但仍远未达到预期目标。
　　我们面临的挑战是开发和实施数据驱动的解决方案，以更新收入来源，优先考虑公共卫生，增强客户体验，并支持可持续发展举措。
　　在提高运营效率的同时关注客户体验比以往任何时候都更加重要，而且预计将在没有变化的财务目标参数范围内完成。
　　世界上最大的航空公司之一正在使用数据科学来预测与延误和取消的投诉和索赔相关的成本。这帮助航空公司解决了运营中断问题，提高了客户满意度。它还能够开发和推出新的解决方案，以改进在线支付方式，启动绩效警报系统，并优化维护资金的使用。
　　从客户服务到货物运输，该航空公司现在已经有了收集和分析信息并开发新想法的流程，对内部数据分析有了更好的理解。
　　这只是开始
　　我们正站在数据科学的冰山一角。数据科学已经是一个成功企业的重要元素，它的使用将成倍增长。用不了多久，所有的交易系统——购买、预订、银行——都将在工作流程中嵌入人工智能。数据分析将被部署在每个业务的每个应用程序中。没有它，任何组织都无法在大量投资于数据分析的竞争中生存下来。
　　Vipul Baijal是Xebia美洲地区的总经理。Ram Narasimhan是Xebia的人工智能和认知服务全球主管。Xebia总部位于亚特兰大，是IT咨询和数字技术的全球领导者。
　　原文：The tip of the data science iceberg
　　Far more than a trendy buzzword in the business world today, data science is redefining how companies interact with their customers.
　　No matter the sector or industry—retail, insurance, manufacturing, banking, travel—every large enterprise has its own way of dealing with data science. They have to. Data is everywhere. It’s the new gold, and mining that data is critical to the success or failure of any business.
　　Data gives access to the kind of information that separates competitors. Data-driven companies provide better service to their customers and make better decisions—all because those decisions are backed by data.
　　Data science is the next evolution in the business world, and those that fail to adapt to this new reality will cease to exist. The alternative is extinction.
　　That was the fate facing a European fashion and clothing retail chain. Founded in the early 1980s, it built a legacy on a client-focused, upscale in-person shopping experience. The advent and proliferation of online retailers dealt a big blow to its business. When its brick-and-mortar stores started struggling, the store could have accepted its fate and moved into the dustbin of history.
　　Instead, it embraced digitization.
　　Maintaining a positive customer experience as its focus, the company planned an omnichannel digital transformation to manage customers, collect data, and provide products and services sought by their customers.
　　It started with the launch of an e-commerce channel and the building of a CRM system to manage customers and collect data through a loyalty program. To maintain their customer-centric business ethos, they focused on developing a dedicated innovation capability to ensure it was providing consumers with the products and services they wanted. Finally, they moved to digitizing client processes and optimizing the customer journey.
　　Today, the fashion retailer maintains its brick-and-mortar stores to allow shoppers to see and experience the collections it offers. The online store is used as a communication channel to interact with a subset of its customers and build an understanding of what they need and want.
　　To maintain this new approach, eight digital teams were created and everything that can be measured is measured. This digital transformation has enabled the business to trace 90% of its revenue back to the end client. Building a team
　　For companies that have yet to jump into the data science game, or are in their first steps in the space, the first and biggest piece of advice is to be humble, acknowledge this is not something you can do on your own, and pull together a team of professionals.
　　Data science is a complex field, and to be used properly it needs engineers, scientists and analysts to develop the AI platforms that will identify, collect, assess and utilize the data to its maximum advantage. They can develop the strategy that identifies the kind of data that is needed, the best methods to collect that data, the systems needed to gather the information and how to ensure the data is clean and usable so that it can be monetized.
　　This team can also develop the infrastructure required to support data capture and collection, including the AI or machine learning platform and a cloud platform for large computer storage capacity.
　　The cloud platform is key. It enables quick deployment of data and drastically cuts the time required to gain valuable insights into a business and its customers. Analytics engineers can build reliable data pipelines that enable self-service reporting and visualization.
　　But looking at millions of touch points and trying to figure out how to extract meaningful information from it can be a daunting task. Being data-driven means more than simply unlocking data, storing it, and giving everyone access. It’s about pulling insights from the information gathered to predict future insights, advise where to invest in the short term, mid-term, and long term, reduce customer churn, predict demand, optimize the logistics chain or automate business processes.
　　When most useful, data science extracts non-obvious patterns from a large data set, such as purchases, reservation bookings, claims, or banking transactions, to help a business make better decisions. Mining purchasing data
　　Knowing your customer is a basic principle for any business, and the historical data of customer buying patterns is not only the most common and easily accessible data set, it is also among the most important. It enables predictions of future wants and needs and provides valuable insight to influence future consumer choices.
　　A customer relationship management (CRM) system is a good starting point for effectively using data science. Retailers can use this data to identify groups of customers who have similar behaviors and tastes, and also build a better understanding of products that are frequently purchased together.
　　One of North America’s leading apparel manufacturers has a proud 150-year history, and over the years has built up its production capacity, expanded its sales network, and invested in marketing. But perhaps its most important initiative today is its data science analysis. The data science pision reports directly to the CEO, and works with an ocean of data on a Google platform to engage customers more effectively.
　　During the COVID pandemic, as more clothing shoppers were pushed online, the company’s data science pision flourished, improving the company’s digital footprint to collect as much consumer data as possible—who is buying online versus who is shopping in-store, what they are checking out online, how much they spend, how they pay for their purchases, what they end up buying—and using all of this information to create profiles and track patterns.
　　The data was then monetized by marketing campaigns that directly targeted the consumers that fit within those profiles. Mining user data
　　As data science progresses, customer interactions are becoming much more personalized. Rather than building broad profiles about groups, specific markets, or regions, the focus becomes increasingly inpidual.
　　Streaming services use data to improve the user experience. They offer viewers recommended titles that their algorithm has determined the inpidual may enjoy. The easy assumption is that this is simply based on what the viewer may have previously watched. For example, because you enjoyed this action movie starring Tom Cruise, maybe you will enjoy this other action movie starring Tom Cruise.
　　However, it is much more complex than that. The streamer would start with archetype profiles built by analyzing mountains of user data from around the world. Then it will take the inpidual’s viewing patterns (titles, genres, actors, seasonality), weave them in with others within that profile from around the world, and what they are watching, to come up with its recommendations. Mining travel data
　　The travel and hospitality sector is relying on data science to help it recover from the pandemic.
　　Few businesses were spared negative impacts from the pandemic, but the travel sector was decimated. Before the pandemic, the global airport operations market was worth an estimated $221 billion. After the pandemic forced the closure of borders and all but shut down recreational air travel, that figure plummeted to $94.6 billion. There was a slight improvement in 2021 to $130.2 billion, but it is still far from where they want to be.
　　The challenge is to develop and implement data-driven solutions that will renew revenue streams, prioritize public health, enhance the customer experience, and support sustainability initiatives.
　　Focusing on the customer experience while improving operational efficiency is more crucial than ever, and it is expected to be done within the parameters of financial targets that have not shifted.
　　One of the world’s largest airlines is using data science to forecast costs related to complaints and claims for delays and cancellations. This has helped the airline solve operational disruptions and improve customer satisfaction. It was also able to develop and roll out new solutions for improving online payment methods, initiating a performance-alerting system, and optimizing the use of maintenance capital.
　　From customer service to cargo shipments, the airline now has processes in place to collect and analyze information and develop new ideas, with a greater understanding of internal data analytics. Only the beginning
　　We are standing on just the tip of the data science iceberg. Data science is already a vital element of a successful business, and its use is going to multiply a hundredfold. It will not be long before all transaction systems—purchases, reservations, banking—will have AI embedded in the workflow. Data analytics will be deployed across every application of every business. Without it, no organization will survive against competition that is heavily invested in data analysis.
　　本文主要内容原作者Vipul Baijal and Ram Narasimhan，仅供广大读者参考，如有侵犯您的知识产权或者权益，请联系我提供证据，我会予以删除
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