自助分析工具将终结商业智能（BI）吗？

回顾过去十年，数据科学飞速发展，数据科学领域的职业人似乎也是一路升职加薪，顺风顺水。《哈佛商业评论》杂志（Harvard Business Review）称数据科学家为本世纪“最性感”的工作，很多公司也在招兵买马，急于壮大他们的数字科学队伍。数字科学的黄金时代是否已经过去了呢？对于科班出身的数据科学家来说，目前最大的威胁是自助式分析工具和非专业出身的公民数据科学家（citizen data scientist）的出现。

美国高德纳咨询公司（Gartner）预测，2017年，公民数据科学家增长速度是专业出身数据科学家增长的五倍，而全球分析和咨询公司Quantzig在其2017年数据分析行业趋势中，将自助式数据分析软件排在了第一位。自助式大数据平台在操作时不需要任何编码知识，其成本优势是极具竞争力的。

根据Glassdoor.com的统计，一个数据科学家的平均年薪是$ 119,000。受过一些训练能够使用自助式分析工具的员工，也可以完成大部分数据科学家的工作，但是他们要求的薪水却不像数据专业出身的数据科学家那么高。

事实上，让在某个行业有丰富经验的人来分析数据，可能效果更好。IBM就是这么做的。IBM更倾向于教了解网球的人分析数据，这比让数据科学家去学网球相关知识要容易得多。这一逻辑同样适用于很多其他行业。

这对数据科学家意味着什么呢？乍一看，这好像并不是一件好事。然而，自助式分析平台供应商Alteryx的产品营销副总裁鲍勃·劳伦（Bob Laurent）认为，自助式分析工具的出现，不是要将数据科学家踢出局，而是推动他们往更高端的方向发展，让他们去做更高级更有趣的工作。

他指出，“IT已经成为一个促进者。如果他们能帮助人们自主的分析数据，那么他们就不需要再一周又一周的做无聊的报告。”而且，只有让人们亲手操作，才能让他们真正认识到数据的重要性，特别是让公司的高层认可数据的价值。

我们与五位专业人士谈论了自助式分析工具的问题，询问了他们对自助式分析工具如何影响BI和数据科学家这一问题的看法。

Indeed.com网站BI分析师罗宾·拉普博士（Dr. Robyn Rap）

（注：Indeed是全球最大的招聘求职网站，每月有超过2亿独立用户使用 Indeed。Indeed 的服务跨越60个国家，支持28种语言，覆盖了产生全球94％GDP的区域。）

自助式工具说到底就是工具。能否高效的使用这些工具很大程度上取决于使用它的人，我们会用自己的判断和直觉来挖掘数据信息，决定数据分析的质量。工具会更新换代，我们可以学习如何使用这些工具。但是，要学习如何适当地处理数据是很难的，也不是所有人都像数据科学家一样对数据有好奇心和批判态度。

这就是为什么我们在Indeed的BI团队里会经常自我反思：我是否喜欢学习新的东西？我们是否对数据表现出好奇心，并质疑其准确性或可靠性？是否能够解释为什么数据看起来像现在这样吗？

在一天结束时，如果你能对这些问题回答“是”，那么你当前使用的是什么工具就根本不重要了。

Rent The Runway高级数据科学家索拉比·巴特纳格尔（Saurabh Bhatnagar）

（注：Rent The Runway是美国服装租赁先锋，被称作时尚圈的“Netflix”。2016年年底宣布获得来自Fidelity 的6000万美元E轮融资，并实现年营收超过1亿美元）

工具帮助我们以更快的速度解析更大的数据集，但最终还是只有数据分析师能准确的了解业务问题，发现深层次的信息。数据科学家有责任将这些深层次信息洞察能力融入到数据分析中。

虽然现在有很多人在开发自动数据分析工具，但这些工具暂时都还没有达到数据科学家的水平。不过，在可预见的未来，使用这些描述性和预测性工具的人还是会越来越多。

Machinima BI副总裁马特·考茨（Matt Kautz）

（注：Machinima 成立于2000年，是一家老牌的游戏视频内容服务商，专注于为YouTube平台制作游戏视频，每月浏览量超过10亿次。目前， Machinima 获得 2400 万美元 E 轮投资，投资方主要为华纳兄弟娱乐公司、谷歌等）

自助式分析工具的普及使BI从业者不用再处理机械式枯燥的数据交付方面的问题，同时，那些能够提供更直观和主动分析的BI团队会受到更多的认可。公司需要分析师深入挖掘数据信息，解释为什么数字上升了，而不是简单地提交工作报告。

美国伊利诺伊州助理首席数据师凯文·哈里森（Kevin Harrison）

我相信未来的发展在于更好的利用数据，在于让用户与数据分析工具开发商一起将这些工具的功能最大化。自助式分析工具让用户在处理数据方面变得更“聪明”，将BI的价值直接交到企业手中。

简而言之，BI的角色已经从“报告和参数的处理者”转变成了“商业辅助者”，变成了“合作伙伴”的角色，而不仅仅是一种功能。

美国基督教青年会高级BI与数据分析总监马里奥·特雷斯科恩（Mario Trescone）

自助式分析工具使BI的未来充满无限可能。不论是大型的营利性组织，还是小型非营利性组织，他们收集、整理数据并提炼信息的能力得到了极大地提高，决策过程也比以前更加科学。

这些工具使他们能够更快地对市场变化做出反应，预测未来可能会发生的情况。这些工具还将在开发侧和用户终端提供工作机会，吸收更多数据、预测分析和数据科学方面的人才就业。

本文由灯塔大数据公众号 翻译。

英语原文：

Expert View: Do Self-Service Analytics Tools Mean The End Of BI As We Know It?

Way back at the turn of the decade, data science looked like a career path that was only on the up. HBR had named it the sexiest job of the century and organizations were hiring as fast as they could. But are these halcyon days over? In a previous article, we asked whether data science could be automated. Perhaps a more clear and present danger, however, is self-service analytics tools and the rise of the citizen data scientist.

Gartner predicted that by this year, the number of citizen data scientists will have grown five times faster than their highly trained counterparts, while global analytics and advisory firm Quantzig listed self-service data analytics software as its number one trend in data analytics for 2017. The cost benefits of using self-service big data discovery platforms that require no coding knowledge to use are potentially massive. According to Glassdoor.com, the average annual salary of a data scientist is $119,000. A trained employee could leverage the data in the same way, but is unlikely to command the kind of salary someone with coding expertise does. Having someone with actual industry and business experience analyze the data may actually yield greater insights. IBM prefers to teach people with professional tennis experience how to analyze the data at Wimbledon rather than teach data scientists about tennis because it’s easier to do it that way round, and this logic applies across many industries.

What does this mean for data scientists? At first glance, it may look like it is a bad thing. However, Bob Laurent, VP of product marketing for self-service analytics platform provider Alteryx, argues that rather than push data scientists out, it will simply push them up, and give them more interesting work to. He notes that ‘IT has become more of a facilitator. If they’re able to give people access to data with the proper guardrails, then they’re out of the business of having to do mundane reports week in and week out.’ There are also advantages to be had from giving so many people the greater appreciation of data that can only be gained when they use it for themselves, particularly when it comes to getting senior buy-in for data projects. We spoke to five leading industry figures about how they felt self-service analytics would impact business intelligence and the role of the data scientist.

Dr. Robyn Rap, Business Intelligence Analyst at Indeed.com

Self-service tools are just that – tools. Using them effectively depends greatly on the person who’s using it, their judgment, and their intuition to dig into the data and its quality. Tools are going to come and go, and you can teach people how to use them. It’s much harder to teach someone how to approach data appropriately, with curiosity and a healthy amount of skepticism. That’s why on Indeed’s BI team we ask ourselves: Does this person like to learn new things? Do they demonstrate curiosity about data, and question its accuracy or reliability? Do they come up with hypotheses for why the data looks the way it does? At the end of the day, if you can answer ‘yes’ to those questions, it shouldn’t matter what tools you’re currently using.

Saurabh Bhatnagar, Senior Data Scientist at Rent The Runway

Tools are helping us dissect larger data sets with speed, but ultimately it is up to the Data Analyst to understand the business problem and find the insight.

It is the responsibility of the Data Scientist to package that insight into a data product.

There are efforts like automated statistician but they are not up to the mark yet. For the foreseeable future, humans using these descriptive and predictive tools will the norm.

Matt Kautz, VP of Business Intelligence at Machinima

The ubiquity of self-service tools frees BI professionals from the rote, pedantic numbers delivery aspect of the job, and puts a lot more value on BI teams that can offer more intuitive and proactive analysis. It requires analysts who thrive on digging deeper to determine why the numbers went up, rather than simply delivering reports showing that they did.

Kevin Harrison, Assistant CDO of the State of Illinois

I believe the future is in combining the data, providing governance, and working with/maturing the business users to best utilize these tools. The self-service tools combined with business users becoming more savvy helps put the value of Business Intelligence into the businesses hands directly, which shows the value of BI directly. In short, BI work has changed from a reports/metrics ‘doer’ role, into an enabler role for the business and can make BI more of a partner and not just a function.

Mario Trescone, Senior Director of Business Intelligence and Data Analytics at YMCA of the USA

Self-service analytics tools leave the future of Business Intelligence one without boundaries. Organizations big or small, for-profit or not-for-profit, now more than ever have the ability to gather, organize, and distill information to produce actionable insights quicker, thereby enhancing their decision making process. These tools ultimately provide them the ability to react more quickly to market shifts and give them a glimpse as to what tomorrow may bring. These tools will also create opportunities for those with backgrounds in statistics, predictive analytics and data science on both the development side as well as the end-user side.

End.

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