大数据，小脑袋，数据会让人变得弱智吗？
作者： Paolo Gaudiano 本文为36大数据独译，译者Bin
The increasing popularity of big data overlooks the importance of considering the manager’s domain expertise. Agent-based simulation combines data and expertise to solve complex problems in many different areas.
随着大数据的流行，人们渐渐忽略了参考经营者领域的专家意见。以代理为基础的模拟、数据、专业知识的结合就可以解决许多不同领域的复杂问题。
Lured by the promise of AI and machine learning, and by the ease of collecting and storing data about every facet of every activity in our daily lives, leaders across most industry sectors have embraced big data as the next “big thing” in helping them manage a variety of corporate functions, including marketing, pricing, supply chain, operations, finance and more.
在我们日常生活的方方面面中，由于受到人工智能、机器学习和轻易收集与储存大数据的优势的吸引，绝大部分行业的领导者们都不约而同的接受了大数据作为接下来的“大事件”来帮助他们执行各种各样的公司职能，这就包括市场、价格、供应链、操作，金融以及其他。
I am a big fan of quantitative approaches, I have worked in and around data science for nearly three decades, and I am a firm believer in the adage that you can’t control what you can’t measure. From this perspective, I have been delighted to see a growing trend in the past decade for leaders across all industries and corporate functions to embrace quantitative approaches that help them make better decisions.
我是定量方法的忠实粉丝。曾经在数据科学领域内外工作了近三十年的我，坚定不移地相信凡事无法度量一定无法控制。从这个角度出发，我很乐意看见在过去十年间，所有产业和公司职能的领导者们渐渐开始接纳定量方法，而这有助于他们更好地决策。
However, I believe that the adoption of big data may have gone past the point of diminishing marginal returns, and is even causing problems in some areas.
但是，我相信，大数据的采用已经过了减小边际回报的阶段，甚至已经开始在某些领域带来麻烦。
My greatest concern is what I call the “Big Data, Little Brain” phenomenon: leaders who abdicate their knowledge and rely excessively on data to guide their decisions. Specifically, in a typical big data project, a leader might engage an external or internal team to collect and process data, hoping to extract insights related to a particular business problem. The big data team has the expertise needed to wrangle raw data into usable form, and to select algorithms that can identify patterns and extract information. The results of this exercise are then presented to the leader through reports, charts, infographics and other types of visualization.
我最关心的莫过于我称之为“大数据，小脑袋”的现象：由于领导者放弃他们的知识判断并且过度依赖数据来做决定而产生。具体来说，在一个典型的大数据项目中，一个领导者也许会参与内部或外部项目组来收集并处理数据，并希望提取出一个特定商业问题的见解。大数据组能利用专业知识把原始数据处理成可用形式，并且选择能提取信息和明确模式的算法。这就导致所有结果将以报告，表格，信息图像或者其他可视化形式呈现在领导者面前。
The problem is that, in this typical scenario, the leader’s role is limited to using her expertise to make sense of the information prepared by the big data team, and to use that information to guide decisions; her knowledge of the business has no bearing on how the data is processed, or what algorithms are used to extract information from the data. This is because most business leaders are not experts in data science, while most data scientists are not an experts in the business areas of the leaders they support.
在这样典型模式下的问题就出在，领导者只能局限于用他的专业知识来解释大数据组所呈现的信息，然后用这个解释来指导决定。她的商业知识无法影响数据的处理方式，抑或是精简大数据的信息算法。这是因为大多数的商业巨头并不精通数据科学，同时大多数数据科学家也并不了解支持他们的商业领域大鳄。
It is important for decision-makers to understand the limitations of big data approaches, and to explore methodologies that let them incorporate their knowledge and expertise into the entire decision-making process, not just in the interpretation of results provided by data scientists. This includes more process-oriented, qualitative methodologies like Design Thinking, as well as more quantitative methodologies such as Agent-Based Simulation (ABS). ABS in particular is gaining popularity in a number of other fields, including economics,epidemiology, social science, medicine, finance, transportation, tourism and many more.
决策者要知道大数据方法的局限性是很重要的，探索能在整个决策过程中整合决策者意见和专业知识的方法也是很重要的，而不仅仅只是对数据学家的结果做出解释。这样就包括更以过程为中心的、定性的方法比如设计思维，以及更定量的方法如基于代理人的模型(ABS).特别的是，ABS已经在许多其他领域开始流行起来，其中包括经济学领域，流行病学领域，社会科学领域，药物学领域，经融学领域，运输业领域，旅游业领域等其他更多领域。
For the past two decades I have applied ABS to a variety of problem domains, from managing personnel for the U.S. Navy, to improving the drug development pipeline for a major pharmaceutical company, to increasing energy efficiency in buildings. In 2010, I co-founded Concentric, a company that uses ABS for marketing analytics. Back in 2014, Concentric was retained by a leading automaker to help it plan the launch of a new model. Concentric recommended doing the launch six months earlier than the client was planning. In 2016, the automaker launched the model as recommended; a year later, it found that Concentric’s simulation had predicted monthly sales for the first year with 93% accuracy.
在过去的二十年里，我将ABS运用到了许多出现问题领域，这包括提升美国海军的职员管理和主要制药公司的药物运输通道，到提高建筑物的能量利用率。在2010年，我合建了一个公司—Concentric—用ABS进行市场分析。再回到2014年，Concentric仍然被汽车制造者用来研发新模型。Concentric推荐提前6个月做计划研发。在2016年，汽车制造商研发出了推荐的模型。一年后，他发现Concentric的模型以93%的准确率预测了第一年内每个月的销售额。
More recently, advertising agency MediaStruction used the Concentric platform to help a large commercial bank forecast sales of its consumer checking, business checking and home equity products. At the close of Q1, it ran a blind comparison and found that the model’s predictions for these three product categories deviated from actual sales only by 1.1%, -0.4% and -4.1%, respectively.
近来，广告代理MediaStruction使用了Concentric的平台，建立了一个庞大的商业银行来预测消费者的校核，商业校核以及同等自家产品的销售额。快接近Q1时，进行的随机抽样显示这三个类别的实际销售额分别比模型预测的数据少1.1%，多0.4%以及多4.1%.
In summary, by embedding the manager’s expertise into a predictive model, ABS is able to solve complex problems in a transparent way with a high degree of predictive accuracy. The increased availability of commercial ABS tools and didactic materials suggest that this “Big Brain, Big Data” approach is poised to revolutionize business management.
总之，将管理者的专业知识嵌入预测模型，ABS就能以精准而又透明的方式来解决一些复杂的问题。商用ABS工具以及说明材料的普及暗示着“大数据，小脑袋”方法即将改革商业管理方式。
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