According to a recent McKinsey Technology report, “Yesterday’s data architecture can’t meet today’s need for speed, flexibility, and innovation.” Indeed, the demands on today’s data infrastructure, systems, and apps is greater than ever as companies have rapidly deployed new data-driven solutions. This includes预测分析solutions aimed at improving marketing and sales results, data mining solutions that help drive customer personalization, and automation solutions that assist with repetitive tasks to increase workforce efficiency.

While most executives report added value and improved business outcomes resulting from data analytics initiatives, the benefits have not come without a cost. According the McKinsey Technology report, “These technical additions—from data lakes to customer analytics platforms to stream processing—have increased the complexity of data architectures enormously, often significantly hampering an organization’s ongoing ability to deliver new capabilities, maintain existing infrastructures, and ensure the integrity of artificial intelligence (AI) models.”

If this sounds familiar, you’re not alone. The problem is that a growing number of emerging and more agile companies are launching newer data technologies, such as serverless data platforms, that enable them to develop and launch products to market more quickly than traditional players. One needs to look no further than Amazon and Google for leading examples, but the threat to established companies can come from any direction, including from start-ups and smaller companies.


根据麦肯锡技术报告,“为公司建立竞争优势 - 甚至保持奇偶校验,他们需要一种新方法来定义,实施和集成他们的数据堆栈,利用两个云(超越基础设施作为服务)和新的概念和组件。“该报告继续提供“six shifts” that any company can make to their data architecture to enable more rapid delivery of new capabilities,同时简化现有方法。这些包括移动:

  1. From on-premise to cloud-based platforms:根据报告,今天基于云的数据平台释放了“完整的新数据架构方法”。此班次使所有尺寸的业务能够在规模中部署和运行数据基础架构,平台和应用程序。启用技术包括无服务器数据平台,例如Amazon S3和Google BigQuery以及集装箱数据解决方案。
  2. 从批处理到实时数据处理:实时数据通讯服务的成本较低,创新公司(包括运输和制造),包括更加个性化的服务和通过实时流动的警报。这种转变是通过消息传递和警报平台和流式处理和分析解决方案等技术启用的。
  3. From pre-integrated commercial solutions to modular, best-of-breed platforms: According to McKinsey, companies are “moving toward a highly modular data architecture that uses best-of-breed and, frequently, open-source components that can be replaced with new technologies as needed without affecting other parts of the data architecture.” This new and more flexible approach is being enabled by data pipeline and API-based interfaces and analytics workbenches.
  4. 从点对点到解耦数据访问: Companies today can allow employees and data teams to access more up-to-date data via APIs, while also ensuring greater control and security. This improves collaboration and speeds the adoption of data-driven technologies, like AI. Technologies, including anAPI管理平台and “a data platform to buffer transactions outside of core systems,” are enabling this shift.
  5. From an enterprise warehouse to domain-based architecture据麦肯锡报告称,“许多数据建筑领导人已经从中央企业数据湖枢转到”域驱动“的设计,可以定制和适合旨在改善新数据产品和服务市场的时间。”这允许产品所有者更好地管理数据集,使得团队成员和可能需要它们的业务的其他部分更容易消耗。数据作为服务平台,数据虚拟化,数据编目工具正在帮助公司进行这种转变。
  6. From rigid data models to flexible, extensible data schemas: Today’s most innovative companies are shifting from pre-defined and proprietary data models to “schema-light” approaches that drive more flexibility into data analytics approaches and initiatives. Benefits include agile data exploration, greater flexibility in data storage, and reduced complexity, according to the McKinsey report.Data vault and graph database technologies have helped bring about these changes.

To learn more, check out thecomplete McKinsey Technology report。如果您要进一步探索此主题,请参阅此相关APEX of Innovation post on why you need a sound data management strategy


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