双语阅读：企业如何布局和提升大数据能力?

 

大数据作为现在最流行的一个方向，被很多的企业重视。那么如何提升企业的大数据能力，以发掘出它最大的价值呢？您可以从本文中找到答案。

一个成年人平均每天做出70个有意识的决定，一年就要做出超过25000个决定。企业的大部分决定是不重要的，但这其中会有一些决定给企业带来重大的机遇或者严重的后果。企业无法避免做出坏的决定，但是可以通过提升数据和分析能力降低做出坏决定的概率。

数据和分析并不是一个新的概念，早在上个世纪的两股宏观经济潮流中就已经形成。第一股潮流是劳动力从劳动力密集型产业向技术密集型产业转移。第二股潮流是二十世纪60年代企业引入了决策支持系统。随着不断增加的智力工作者从事于高科技工作，存储的资料和数据量也随之提升，数据分析在企业决策制定和执行中扮演着越来越重要的角色。

但是，企业在初期是很难整合数据并将数据分析应用于他们的日常运营中的。他们所收集的数据变量有限，且数据以不同的格式和结构存储在不同的地方。而且，从这些含有噪音的数据中过滤出相关的、重要的、有效的数据的困难程度随着数据量的增大呈指数级数上升。根据IDC的研究，从2005年到2012年，全球的数据量翻了27番，约达到2.5ZB。其中仅有25%的数据是有用的，仅有3%的数据贴有标签能被使用，仅有0.5%的数据被用于分析。

许多具有行业领导地位的企业已经意识到需要提升组织内部收集、存储、获取和分析这些超大量、极复杂的数据集的必要性。而且，企业需要为提升大数据能力投入更多的资源，以让其全面发挥潜在的作用。对大数据能力的投资需要遵循数据分析的价值链，布局于5个方面。

收集与前期准备：要有效地收集和管理大规模、复杂的数据集。企业数据产生于各自独立的数据库。为了后期能最大化数据的使用，企业应制定相应的数据标准，确保数据的准确性、一致性和可转换性。 处理：数据必须能被实时处理。在一些竞争激烈的领域，对企业来说，比竞争对手提前几天可能就能存活下来。因此企业需要评估基础架构、算法，编程语言，以提高数据的处理速度。 可视化：处理完的数据需要以简单易懂的方式呈现出来。人脑对大规模数据或文本数据的处理是缓慢的，因此企业可使用可视化工具提升对数据认知、洞察的能力。 解读数据：可视化数据应被正确地解读。企业应尽量避免错误的数据解读对认知造成的偏差。仅靠直觉亦或是极端推崇数据结论都可能将企业引向歧途。 改进：智力工作者必须提供反馈与指导。企业要促进利益相关者的反馈机制，形成反馈闭环。这种反馈机制能够对连续的分析、学习、问题识别给予支持，从而扩大信息的数量与范围。

企业要获得大数据的潜在价值的困难是艰巨的。这些困难横跨多个领域，如预算、技术的可获得性、已有基础架构的使用、运作模式等等。然而，能够有效使用数据、洞悉先机的企业将在行业里占有优势地位。而从长远来看，这样的企业将变成这个行业的领导者而非仅仅是参与者。

英语原文：

The average adult makes 70 conscious decisions a day, or more than 25,000 a year. Many of those decisions are inconsequential to organizations, but a few of them can create substantial opportunities or problems. While organizations cannot prevent bad decisions from being made, firms can minimize the risk by investing in data and analytics capabilities.

Data and analytics isn’t a new concept. It has been formed over the last century with the aid of two key macroeconomic trends. The first was the migration of the workforce from labor-intensive to knowledge-intensive roles and industries. The second was the introduction of decision-support systems into organizations in the 1960s. As an increased number of knowledge workers began to interact with more powerful technologies and accompanying data stores, analytics began to take a more critical role within organizational decision-making and execution.

However, firms initially had some difficulties incorporating data and analytics into their operations. They gathered a limited number of variables and stored them in multiple data stores with different formats and structures. Additionally, filtering the data to validate what is relevant and impactful, or the signal, from the noise became difficult as the amount of data increased exponentially. Based on a study conducted by IDC, an IT consultancy, the amount of data available globally grew 27-fold to approximately 2.8 trillion gigabytes from 2005 to 2012. The study also noted that roughly 25% of this data is useful, but only 3% of it has been tagged for leverage and only 0.5% of it is currently analyzed.

Most leading organizations see a need to enhance internal capabilities to collect, store, access, and analyze these exponentially large, complex datasets, increasingly known as Big Data. However, leaders need to allocate greater investments to Big Data capabilities in order to fully realize the value potential. These investments need to be made across the five segments of the data and analytics value chain.

Collection & Readiness: Large, complex datasets need to be collected and managed effectively. Organizations generate data within independent silos. In order to maximize data leverage, organizations should maintain data standards to ensure data accuracy, consistency, and transferability.

Processing: Data must be processed in real time. Gaining a few days on competitors can be the key to survival. Therefore, organizations should evaluate their architecture, algorithms, and even programming language to substantially increase processing speed.

Visualization: Processed data needs to be presented in a manner that can be readily understood. Humans struggle with processing large amounts of numerical and textual data. Organizations should use visualization tools to enhance human pattern recognition, insight, and actions.

Interpretation: Visualized data has to be interpreted correctly and communicated to knowledge consumers. Organizations should screen for biases that can distort insights, while guarding themselves against “gut-feeling” decision-makers as well as data extremists because both ends can lead a firm to act sub-optimally.

Refinement: Knowledge consumers must provide feedback and guidance to knowledge producers. Organizations should facilitate a feedback loop across diverse stakeholder groups, which can support continual analysis, learning, and issue identification in order to attain informational scale and scope.

Organizations have significant hurdles to overcome in order to capture the value potential of Big Data. These hurdles span the continuum of investment capacity, skill availability, legacy infrastructure, and operating models. However, organizations that are able to effectively leverage data and insights to drive differentiated value propositions and outcomes will dominate their industries. Ultimately, these organizations will be industry leaders rather than just industry participants.

作者：Bill Pieroni      译文：王珏  viq：大数据观察

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