1. Objective

1. 目标

There is a huge hype of Big Data and its features, most of them have been summed up in 9 different Vs of Big data like Volume, Velocity, Variety, Veracity, Validity, Volatility, Value, Variability, Viscosity.

有一个巨大的炒作大数据和它的特点，其中大部分已经在 9 种不同的大数据中总结出来，如体积、速度、品种、准确性、有效性、波动性、价值、可变性、粘度.

vulnerability 10th V of big data

Vs of big data

2. Big Data – Vulnerability

2. 大数据漏洞

In a recently published white paper by credit reference agency Experian, a proposal has been given to add another “V” to the Big Data features Vulnerability. With the increasing size of people personal data, they have started feeling that it is being used to pry into their behavior to sell them things by different commercial websites.

在信贷参考机构 Experian 最近发布的白皮书中，有人提议在大数据特点漏洞 .随着人们个人数据规模的不断扩大，他们已经开始觉得，通过不同的商业网站，这些数据被用来窥探他们销售商品的行为.

This is not being liked by some people who may stop doing business with such organizations where their private data is at risk while others aren’t worried about this, as they are comfortable with a transaction that involves exchanging an amount of privacy for an amount of convenience or value.
John Roughley, Experian’s head of strategy for credit services, told “We think about things emotionally … and the emotion that’s associated with data is sometimes of nervousness, anticipation or vulnerability. Stories have been heard about data breaches, and most people have experienced their data being misused as well –people getting calls for asking about payment protection insurance, or telling them they had an accident when it wasn’t.“
At current scenario, with industries running behind Big Data, when data comes up in casual conversation, we generally get excited to discuss the amazing new things we can do with the ocean of data available to us, and the ways that Big Data and analytics are changing the world for the better.
But sometimes the tone can be markedly different while discussing personal data being used by businesses and organizations and why do they need to know so much? What will happen if this data gets out, won’t it be easy for criminals to steal money from our bank accounts and even take our identities?

有些人不喜欢这样，他们可能会停止与私人数据有风险的组织做生意，而另一些人对此并不担心, 因为他们对一项交易感到满意，这项交易涉及为方便或价值交换大量隐私.
Experian 信贷服务战略主管 John Roughley 告诉我们，“我们从情感上思考事情…… 与数据相关的情感有时是紧张、期待或脆弱的.关于数据泄露的故事已经被听到，大多数人都经历过数据被滥用的经历 -- 人们接到询问支付保护保险的电话, 或者告诉他们发生了意外.“
在目前的情况下，行业落后大数据当数据出现在随意的对话中时，我们通常会兴奋地讨论我们可以用数据海洋做的惊人的新东西, 大数据和分析正在改变世界的方式变得更好.
但是有时候，在讨论企业和组织使用的个人数据时，语气可能会明显不同，为什么他们需要知道这么多？如果这些数据出来了，犯罪分子会不会很容易从我们的银行账户上偷钱，甚至拿走我们的身份？

In order to diminish this fear, organizations need to reassure customers about the safety of their personal data that it won’t be lost, misused or misplaced. This will require achieving a level of “data stewardship” far beyond a level that which is offered by most data businesses today.
Once that’s done, people will be more interested to hear about the another V – value. “We can help people in knowing how the most value from their data can be extracted like in finding a cheaper energy tariff or share their Fit bit data with their doctor to get better medical advice. All this depends on how we are currently conditioned to think about data.

为了减少这种恐惧，组织需要向客户保证他们的个人数据不会丢失、误用或放错地方.这将需要实现远远超出当今大多数数据企业提供的水平的 “数据管理”.
一旦这样做了，人们会更感兴趣地听到另一个 V 值.“我们可以帮助人们了解如何从他们的数据中提取最大价值，比如找到更便宜的能源关税，或者与他们的医生分享他们的 Fit bit 数据，以获得更好的医疗所有这些都取决于我们目前对数据的看法.

Advice in Experian’s publication, A Data Powered Future, includes taking a careful overview of the data security and having procedures in place for monitoring change of how an organization’s use of data could be more transparent to its customers. By adopting a pro-active policy of transparency, organizations doesn’t just increase trust in its customers but also opens a channel for another conversation about the value that their services can bring.

Experian 的出版物《数据驱动的未来》中的建议, 包括仔细概述数据安全，并制定程序来监控组织对数据的使用方式的变化，以便对客户更加透明.通过采用积极主动的透明度政策，组织不仅增加了对客户的信任，还为关于他们的服务可以带来的价值的另一次对话打开了渠道.

But we are far away from this approach currently. Challenges need to be faced while addressing people’s concerns about their personal data – particularly when it comes to medical or financial information. Organizations have a big responsibility to protect our personal data and be more transparent about its usage. This can be addressed by adding “Vulnerability” as another essential consideration, with regards to every piece of data which is collected. This would be a pragmatic step towards addressing the personal data problem with Vulnerability.

但是我们目前离这个方法还很远.在解决人们对个人数据的担忧时，尤其是在医疗或财务信息方面，需要面临挑战.组织有责任保护我们的个人数据，并对其使用更加透明.这可以通过在收集的每一条数据中添加 “漏洞” 作为另一个重要考虑来解决.这将是解决个人数据漏洞问题的务实一步.

https://data-flair.training/blogs/vs-of-big-data-introduction