In today’s business climate, strategic moats are built with data. Long gone are the days when you could build a new business line on software without a data play. Data was originally compared to oil, suggesting data fuels innovation engines. More recently, the Economist penned the comparison of data to sunlight because, like solar rays, data will be everywhere and underlie everything. Data is also the new infrastructure on which savvy business people erect differentiated business models.

在当今的商业环境中，战略护城河是用数据构建的。 不用数据就可以在软件上建立新业务线的日子已经一去不复返了。 最初将数据与石油进行了比较，表明数据推动了创新引擎的发展。 最近，《 经济学人 》将数据与日光进行了比较，因为就像太阳光线一样，数据将无处不在，并且构成一切的基础。 数据也是精明的商人在其上建立差异化业务模型的新基础架构。

Designing data products is costly. Data scientists and machine learning engineers top the charts of highest paid professionals, next to surgeons and doctors. Needless to say, it takes financial prowess and aligned business incentives to graduate a data science project from an experiment into a production application. The blueprint for successful data products consists of three core elements: business workflows, distribution channels, data sources.

设计数据产品的成本很高。 数据科学家和机器学习工程师紧随外科医生和医生之后，成为薪酬最高的专业人士。 不用说，要使数据科学项目从实验毕业到生产应用程序，就需要财务能力和一致的业务激励。 成功数据产品的蓝图包含三个核心元素：业务工作流，分销渠道，数据源。

业务流程 (Business Workflows)

Data products emerge as an application layer built on top of business workflows. Data products have a track record of success when deployed in operational settings such as admin process automation, customer support, regulatory compliance. That is to say that data products are currently assigned to the “safe” back-office where failures in performance are less costly.

数据产品作为构建在业务工作流之上的应用程序层出现。 数据产品在操作设置(例如管理流程自动化，客户支持，法规遵从性)中部署时具有成功的记录。 也就是说，当前将数据产品分配给性能故障成本较低的“安全”后台。

Not every business workflow can enable a data product. I’ve prepared and vetted with a number of enterprise companies a scorecard to qualify business workflows for data product applications. Check it out!

并非每个业务工作流程都可以启用数据产品。 我已经为许多企业公司准备了一个计分卡，并对其进行了审查，以使数据产品应用程序的业务工作流程合格。 看看这个！

数据源 (Data Sources)

Public data or open data is available for everyone to access, modify, reuse, and share. Open data organizations are the counterparts of organizations supporting open source software. Their work empowers citizens and can strengthen democracies, streamline processes and systems in society, government, and private businesses. A few awesome open data sources are World Bank Open Data, Global Health Observatory Data, Google Public Data Explorer, Registry of Open Data on AWS, US Census Bureau.

公开数据或开放数据可供所有人访问，修改，重用和共享。 开放数据组织是支持开源软件的组织的对等组织。 他们的工作赋予公民权力，可以加强*制，简化社会，*和私营企业的流程和系统。 几个很棒的开放数据源包括世界银行开放数据 ， 全球卫生观察站数据 ， Google公共数据浏览器 ， AWS开放数据注册处 ， 美国人口普查局 。

Private data sources are the backbone of well-differentiated companies like Google, Amazon, and Facebook. A first-mover strategy enables a company to leapfrog in data aggregation games → data gravity. Search results, product/movie recommendations, and social networks improve with data. That’s why established players are here to stay unless we make it plain simple for machine learning systems to share and learn from disparate data sources.Licensing rights for private data get complex. A common problem across the board is that the owner of the data source cannot sub-license data externally. This means that private data can only be leveraged by products owned by the same organization that owns the data. Catch-22? If data was collected according to a license with sub-licensing clauses, this opens up opportunities for commercializing private data outside the parent organization.We have to address the elephant in the room. Across companies, data management practices fall on a broad spectrum. Leading companies set an example by following ethical, privacy, and security rules. Some industries took matters in their own hands and established data privacy standards and frameworks. In healthcare and financial services, data privacy is enforced by regulatory agencies. Consumer industries have to abide by consumer privacy acts. Rule of thumb for everyone and anyone: always de-identify data and license silos of aggregated data as often as possible.

私有数据源是差异化的公司(如Google，Amazon和Facebook)的骨干。 先行者策略使公司能够在数据聚合游戏→数据重力方面实现跨越式发展。 搜索结果，产品/电影推荐和社交网络会随着数据的增长而改善。 这就是为什么除非我们简单地让机器学习系统共享不同的数据源并从中学习，否则成熟的参与者会留下来的原因。私有数据的许可权变得复杂。 全面的普遍问题是数据源的所有者无法在外部对数据进行再许可。 这意味着私有数据只能由拥有该数据的同一组织拥有的产品来利用。 赶上22？ 如果数据是根据具有分许可条款的许可收集的，这将为将私有数据商业化到其上级组织之外提供了机会。我们必须解决这个问题。 在整个公司中，数据管理实践涉及广泛。 领先的公司通过遵循道德，隐私和安全规则树立了榜样。 一些行业自行处理事务，并建立了数据隐私标准和框架。 在医疗保健和金融服务中，数据隐私由监管机构执行。 消费行业必须遵守消费者隐私法。 每个人和任何人的经验法则：总是尽可能多地取消识别数据并许可汇总数据的孤岛。

Synthetic data is a saving grace depending on the data product at hand. Computer algorithms have gotten really good at generating synthetic data: be it videos of celebrities or Nature articles, we can fake it all. Similar techniques can be used to generate synthetic data that trains the machine learning models behind a data product. To bootstrap such algorithms with relevant data seeds, companies can set up data donation programs — internal or external- with the proper data use agreement in place.

根据手头的数据产品， 合成数据是一种节省的选择。 计算机算法已经非常擅长生成合成数据：无论是名人视频还是《 自然》杂志的视频，我们都可以伪造。 可以使用类似的技术来生成综合数据，以训练数据产品背后的机器学习模型。 为了用相关的数据种子引导此类算法，公司可以建立适当的数据使用协议的内部或外部数据捐赠程序。

分销渠道 (Distribution Channels)

A product well built is only half the story. Your product is signed and sealed, now it needs to be delivered. A few distribution channels are available for enterprise products. Each distribution channel has implications on the product pricing model and on the overall product strategy (build vs buy vs acquire).

精心打造的产品只是故事的一半。 您的产品已签名并盖章，现在需要交付。 企业产品有一些分销渠道。 每个分销渠道都对产品定价模型和整体产品策略(构建，购买与获取)有影响。

On a final note, data-driven products will require continuous monitoring for quality performance. You might ask why all this scrutiny, humans doing the same task are not monitored 24/7. Let’s just say that humans undergo quarterly training on ethics and are responsible for their actions. Machines act in silence so we need to inquire about their behavior using monitoring scripts. It’s a good practice to monitor product performance and flag corner cases. Start by defining internal policies for failure management, product ethics, and human-in-the-loop review.

最后，数据驱动产品将需要持续监控质量性能。 您可能会问，为什么没有对所有执行相同任务的人员进行全天候24/7监控。 可以说，人们每季度接受一次道德操守培训，并对自己的行为负责。 机器处于静默状态，因此我们需要使用监视脚本来查询它们的行为。 监视产品性能和标记极端情况是一个好习惯。 首先定义内部策略以进行故障管理，产品道德规范和人在回路审查。