《大数据分析报告》：未来两年内，大数据+机器学习成为大部分企业的标配

昨日（7月19日），SoftServe 发布最新调查报告称大量的大中型公司希望在未来的两年内能够用上机器学习。

SoftServe是全球领先的技术解决方案提供商，昨日发布了自己的Big-Data-Analytics-Report，研究显示62%的大中型公司希望在未来的两年内能将机器学习用于商业分析。今年四月，Vanson Bourne为SoftServe进行了这项研究，调查了多个行业的决策者对大数据技术中的风险、挑战和机遇的看法。该数据显示，大数据分析技术尽管相对较新，仍然有86%的公司运用了大数据系统。此外，大中型公司认为大数据分析是必须的，并且接受基于大数据分析的新技术。

调查对象被问到，与传统系统相比，他们看到的大数据中的最大机遇是什么？62%的人同意实时分析隐藏着当下最大的机遇。

Facebook宣布了15亿个人工智能代理计划后，过去的一年中人工智能一直占据着人们的想象力。一家荷兰财团用机器学习技术绘制了一张「新伦勃朗」画像。但是另一个让人惊叹的或许是企业已经在认真地看待大数据的机器学习。这个发展意味着，企业如何理解利用和建立新的大数据技术产生有价值的商业见解的优势。

「不久前，我们还走访了多家企业并解释了为什么他们应该了解大数据。2016年的今天，在63%的组织看来，大数据分析对保持竞争力已经是必须的，」SoftServe的技术服务副总Serge Haziyev解释。「本次调查显示，机器学习的重要性非常突出，这是非常令人鼓舞的。我发现，采取行动并使用机器学习技术的企业较早地获得了好处—这是前进的一大步，因为它提供了规范的见解，使企业不仅了解客户正在做什么，还了解他们为什么这么做。」

研究显示金融服务组织比其他行业更加重视大数据分析，他们是新技术的早期使用者。在这些组织中，67%认为大数据分析是保持竞争的必需品，68%期望在未来的两年内在大数据分析中用上机器学习。制造业紧随其后，在他们中，有60%的组织认可大数据分析是必备品，62%的组织计划使用机器学习。

这个调查也考虑了挑战以及增长上的困难。零售业最关注的是数据管理。所有受访者一致认为，相比于传统系统，大数据分析中的数据管理更值得关注。整体上有76%的公司同意这一点，表明它仍然是所用行业共同关心的问题。

SoftServe大数据调查共调查了300名大中型组织决策者，其中100名来自英国，另外200名来自美国。有150名决策者所在的公司员工在1000-3000人之间，剩下的150名决策者的公司员工超过了3000人。受访者被细分为六个重点行业：商业和专业服务，制造业，金融服务业，零售业，物流和运输业，以及其他商业领域。

　 报告目录

1、什么是大数据？

2、大数据的影响？

3、从一次机遇成为必需品

4、如何使用大数据

5、机器学习

6、大数据，高价值

7、打破孤岛

8、机遇来临

　 一、什么是大数据？

Data has been available for decades, and has always been used for analytics, for instance, so why the term “Big Data”? Primarily, it is due to the velocity, variety, and volume of data now available to us. Data isn’t new; it’s just bigger than ever before.

数据已经被使用了十几年了，比如它们总是被用于各种分析，所以为什么说是「大数据」呢？主要是因为我们现在可用数据的数据量（Volume）、处理速度（Velocity）以及数据种类（Variety）。数据并不新，只是比之前大得多。

　 二、什么使得数据更大？

Most traditional data was structured, or neatly organized, in databases. Then the world went digital and the internet came along. Most of what we do could be translated into strings of ones and noughts capable of being recorded, stored, processed and analyzed.

大部分传统数据是结构化的或整齐有序的数据集。而后，全球进入了数字化，互联网也随之而来。我们所做的大部分事都能转录成1和0组成的字符串，进行记录、存储、处理以及分析。

For large businesses the cost of data storage plummeted. Businesses now had a choice: either keep all their data on-site, in their own remote data centers, or farm it out to cloud-based data storage providers.

对大型企业机构而言，数据存储成本直线下降。企业如今有了选择：要么把所有的数据存储到远程数据中心，要么转包给基于云的数据存储提供商。

Today, the world creates 2.5 quintillion bytes of data every day (IBM, “What is Big Data?”). Of course it’s not all relevant to every business, but the message behind this number is that the scale of data available to companies has grown. It is undeniable that the growth of big data is changing the face of modern business. But it’s what companies do with it that matters.

如今，全球每天创造2.5个五万亿字节（IBM，「什么是大数据？」）。当然，这些数据并不是都与商业有关，但数字之后的信息表明公司可用的数据规模一直在增长。不可否认，大数据的增长正在改变现代商业的外观。但公司如何处理数据至关重要。

Today, practically, every industry is, at some level, information driven. Finance, professional services, manufacturing, retail, distribution, transport; there is no sector that has escaped the effects of the data revolution.

实际上，如今每一个产业在某种程度上都是信息驱动的。金融、专业服务、制造、零售、物流、交通等等。没有一个分支能逃脱数据革命的影响。

From machine learning to artificial intelligence and business analytics, the large-scale application of this technology is fuelling innovation that extends far beyond the walls of the traditional IT department. But big data can’t achieve this alone. This is where insight comes in.

从机器学习到人工智能以及商业分析，这一科技的大规模应用超越了传统的IT部门的限制，推动创新。但仅仅大数据达不到这一点，它是洞见的来源。

　 三、SoftServe大数据分析调查

To help make sense of the new landscape, we present the SoftServe Big Data Analytics Survey 2016, an examination of the big data trends that are likely to disrupt businesses and organizations in the next 12 months and beyond.

为了帮助理解这一新场景，我们呈现了2016年的SoftServe大数据分析调查，这是在接下来12个月或更久的时间中可能会颠覆商业与公司的大数据趋势的审查报告。

The SoftServe Big Data Analytics Survey was completed by 300 respondents, made up of 100 UK and 200 US decision makers of medium to large organizations – 150 participants represented companies with 1,000 to 3,000 employees while the remaining 150 participants represented companies with over 3,000 employees. Respondents were broken down into six key industries:

这一调查有300位调查对象，这些中到大型公司的决策者100位来自英国，200位来自美国。其中的150位所在的公司有1000到3000位职员，剩下的150位调查对象代表的公司所有职员超过3000 位。调查对象分布于六大产业：

商业和专业服务（business and professional services）

制造业（manufacturing）

金融服务（financial services）

零售（retail）

物流与运输（distribution and transport）

其他商业部门（and other commercial sectors）

The research, conducted by Vanson Bourne in April 2016, polled a crossindustry panel of industry leaders on big data analytics and the risks, challenges, and opportunities for businesses. It aims to uncover the latest opportunities and insights to help you get the most value from the mountains of data available today.

这一调查由调研机构Vanson Bourne在今年4月份进行，跨行业调查了组织领导者对大数据分析和商业中的风险、挑战和机遇的看法。该调查的目的在于揭示最新的机会和见解，以帮助你从今天的海量数据中获得最大价值。

Our report is inspired by the opportunities identified by today’s decision makers across a range of industries, geographies, and company sizes. We explore how they are using big data and analytics to shape tomorrow for every corner of their organization and the challenges they are facing in harnessing the nascent technologies and approaches that enable that transformation. The leaders we surveyed are in the fortunate position of being at the forefront of a data revolution that promises to alter the way we do business forever.

我们的报告是受到了如今各产业、地方、大小公司内的决策者公认的机遇的启发。我们探索了他们如何使用大数据与分析重塑各自公司的方方面面，也探索了他们利用这一新生的技术与方法进行转型时所面临的挑战。我们调查的领导者非常幸运的能处于数据革命的前沿位置，这场革命有望彻底改变我们做生意的方式。

四、大数据的影响

站在未行者的前方

In an increasingly fast-paced business climate, an overlooked detail can have a meaningful impact on the bottom line. Multiply that one mistake many times over and the effect can be catastrophic. In a global race, increasingly dominated by the fastest players, data is the key to building a more agile, responsive, and productive business.

处于一个步伐不断加速的环境中，一个细节的忽视就会对底线产生重大的影响，一个错误多犯几次的影响将会是灾难性的。在全球竞争中，快节奏的玩家越来越占据主导地位，数据成为建立更加灵活、反应敏捷、多产的商业的关键。

Businesses around the world are beginning to recognize big data as a key trend and, most significantly, starting to invest significant amounts of time and money into analytics services. From helping banks track real-time trends, to offering retailers the insights that help them better understand consumers’ shopping habits, analytics can unlock new opportunities across every industry.

全世界的企业开始公认大数据是一个关键趋势。更重要的是，他们开始将大量的时间与金钱投入到分析服务。从帮助银行追踪实时趋势，到为零售商提供洞见从而帮助他们更好地理解消费者的购物喜好，分析能为每一行业开启新的机遇。

The ascendance of big data to its current position of strategic importance has occurred within a short timeframe. But the report finds that big data analytics technology, despite being relatively new, is widespread, with 86 percent of organizations already using some form of big data analytics. Of these, 45 percent use big data across their organization while 41 percent use it in parts. For those that haven’t yet started down the big data pathway, 11 percent plan to use big data in the future. The message: big data is soon to play a part in the operations of every organization in the world.

对其当前地位的战略重要性而言，大数据的优势在短时间内就显现了出来。但这一报告发现，尽管大数据分析技术相对较新却分布广泛，86%的公司已经使用某种形式的大数据分析了。其中，45%在全公司内使用大数据，41%部分使用。剩下的还未开始使用大数据的公司中，有11%计划在未来使用大数据。信息：大数据将很快在全球每一组织的运行中起作用。

　 五、从一次机遇成为必须品

Big data is no longer an opportunity; it’s a necessity. It’s not long ago that organizations were asking why they should look into big data. Today, big data analytics has moved onto the point where 63 percent of the organizations surveyed believe it is vital in order to remain competitive. Furthermore, organizations are now more receptive to new technologies that build on big data analytics methodology.

大数据不再只是一次机遇，它成为了必需品。不久之前，公司还在问他们为什么需要关注大数据。如今，大数据分析已经达到了一个点：63%的被调查公司相信它对保持竞争力至关重要。此外，公司也变得更加容易接受建立在大数据分析方法论上的新技术。

With 60 percent of IT organizations using big data, technology enterprises are leading the way as the industry with the highest big data uptake. The retail, distribution, and transport sectors, meanwhile, have most work to do, with only 29 percent of these organizations using big data to support their existing strategies. This isn’t for lack of application. Big data can be big business for those that employ it. Retailers can use big data to analyze web browsing patterns, industry forecasts and customer records to predict demand, pinpoint customers, optimise pricing, and monitor real-time trends.

60%的IT公司使用大数据，在产业采用大数据达到最高度的过程中，科技企业带路前行。同时，零售、物流、运输行业还有很多事情要做，这些公司中只有29%使用大数据支持现有的策略。这并不是因为这些行业缺乏应用。对使用大数据的人来说，大数据能成为大商业。零售商可以使用大数据分析网页浏览模式、产业预测以及消费者记录，从而预测需求、定位消费人群、优化定价以及监控实时趋势。

In the US, big data has been advancing in leaps and bounds. This has left the UK playing catch up, with only 23 percent of UK respondents using big data across their organization, compared to 56 percent in the US. However the UK looks set to make significant strides in the future, with 53 percent already using it in pockets of their organization while 16 percent have plans in place to incorporate big data into their strategies (compared to 35 percent and 9 percent in the US respectively).

在美国，大数据已经在飞速发展。这使得英国望尘莫及，只有23%的英国调查对象在全公司内使用大数据，相比于此美国是56%。然而，英国看起来在未来会大步前行，53%的公司已经部分使用大数据，同时16%的有计划将大数据并入他们的策略中（相比于此，美国分别是35%与9%）。

　　 六、如何使用大数据

The opportunities presented by big data range from cost savings to improved analysis.

由大数据提供的机遇范围从节约成本到改进分析等。

Respondents were asked what they saw as the biggest area of opportunity for big data in comparison to traditional systems, with 62 percent agreeing that they consider real time analysis as the biggest potential growth opportunity today.

调查对象被问及相比于传统的系统，他们看到的大数据提供的最大机遇是哪个领域。62%的认为实时分析是如今最大的潜在增长机会。

What opportunities does Big Data Analytics offer your company compared to traditional systems?

相比于传统系统，大数据分析为你公司提供了什么机会？

Organizations in the financial services industry primarily view the opportunities of big data as coming from real time analysis (70 percent) and trend analysis (67 percent).

金融服务行业内的公司主要认为大数据的机遇来自于实时分析（70%）以及趋势分析（67%）。

Financial services organizations are shown to value big data analytics more than other industries, and are also early adopters when it comes to new technologies with an above average response of 67 percent citing it as a necessity to stay competitive, and 68 percent expecting to implement machine learning for business insights within the next two years.

金融服务公司比其他产业内的公司更加重视大数据分析的价值，当出现新技术时也更早的采纳，其中67%的调查对象称它为保持竞争力的必需品，68%期望在两年内使用机器学习捕捉商业洞见。

Financial institutions are under increasing pressure to close the gap between the experiences they provide and what consumers have come to expect.Banks are taking their cue from customers, as well as learning from other industries, such as media, mobile and retail, and setting expectations based on their experiences in those other industries.

想要缩小提供的体验与消费者的期望之间的差距让金融机构面临的压力日益增大。银行正在采用来自消费者的线索，也从其他产业学习，比如媒体、移动以及零售，并且基于这些其他产业内的经验设定期望。

In finance, more than many other industries, knowledge can provide a competitive edge that can drive millions in added revenue. Technology that can provide such insight is highly sought after and tools such as big data analytics are on the rise. For the financial sector, big data is an important part of meeting an increasing client demand for a faster and more accurate service.

在金融领域，知识能提供竞争优势，驱动数百万的附加收益，这比其他产业要多。能提供这种洞见的科技成为了高度追求的对象，大数据分析这样的工具也在上升。对金融部门而言，大数据是迎合客户需求，提供更为快速、准确的服务的关键部分。

The manufacturing industry shares this view, with 60 percent agreeing that big data analytics is a necessity, and 62 percent planning on implementing machine learning in the future.

制造业有同样的看法，60%的调查对象认为大数据分析是必需品，且62%计划在未来部署机器学习。

For the IT industry, the benefits of big data are largely viewed in terms of cost reduction (80 percent of respondents) reflecting their awareness of licensing and commodity hardware savings.

对IT产业而言，大数据的好处大多可视为是降低成本（80%的调查对象），反映出他们的使用许可以及节约硬件的意识。

Machine learning is a new term but it has real-life application.

　　 七、机器学习是一个新术语，但它有现实生活中的应用

One of its main benefits is the ability to analyze large amounts of data at a speed and efficiency that would otherwise require a large team of humans. This has proven to be very effective in the financial services industry, where insurance companies, banks, and lenders need actionable insights quickly. Machine learning also helps financial institutions provide better customer experience and increased power to spot trends and patterns to mitigate risk.

机器学习带来的一大主要益处是能快速而有效地分析海量数据，而人类要做到这一点需要庞大的团队。这已经在金融服务业被证明是有效的，在这个行业内，保险公司、银行和贷款机构需要有价值的及时的洞见。机器学习还帮助金融机构提供更好的客户体验，以及更强的识别发展趋势和模式的能力，从而减小风险。

For example, banks can use predictive analytics to improve the loan approval process. Using a standardized set of criteria across a large anonymized data set, banks can accelerate their approvals process from days to a matter of minutes.

例如，银行可以使用预测性分析改善贷款批准流程。使用遍及大型匿名数据集的一套标准化准则，银行可以将他们的批准过程从几天加速到几分钟。

Businesses are discovering that when they take the plunge and implement machine learning techniques they can realise a host of insights in a short period of time, moving from understanding what customers are doing to why.

公司正意识到这点，当他们孤注一掷，部署机器学习技术时，他们可以在短时间内领悟许多洞见，从消费者在做什么转变为理解消费者为什么这样做。

The report suggests that, over the next decade, big data, machine learning, and artificial intelligence will be integrated seamlessly into the structure of many different organizations. The research highlights that the “sweet spot” of big data varies for each organization, but that considerable advantages can be gained across every sector. From increased customer loyalty to faster business processes, the rewards from big data are by no means small. As such future investment is expected to be significant.

这份报告显示，在下一个十年，大数据、机器学习和人工智能将无缝对接到许多不同公司的结构体系中。研究结果强调，大数据「甜蜜点」对每家公司是相异的，但是每个部门都能获得相当大的收益。从日益增长的顾客忠诚度到更快的业务流程，来自大数据的奖赏绝不会是微不足道的。像这样的未来投资预计是有意义的。

　 八、大数据，高价值

However, while big data has the potential to provide significant value, it can also present new challenges. The survey considered the growing pains of every industry. The retail sector, for instance, was most concerned by data governance.

然而，当大数据有潜力提供重大价值时，它也存在新的挑战。调查考虑了各行各业的增长困难。例如，零售行业最关心数据管理。

Data governance is more of a concern with Big Data Analytics than it is with traditional systems

相比于传统系统，大数据分析的数据管理更值得关注。

Overall, 76 percent of companies agreed with the statement that data governance is more of a concern when implementing big data analytics than with traditional systems, indicating that governance remains a challenge for every industry. What this demonstrates is that organizations need to take a proactive approach to privacy, security, and governance to ensure all its data and insights are protected and secure.

总体上看，76%的公司赞同在进行大数据分析时，数据管理比传统系统更值得关注的，这表明数据管理对所有行业而言仍然是一大挑战。这还证明，公司需要针对隐私、安全和管理采用积极主动的方法做好隐私，安保，和管理工作，从而保证所有数据和洞见都被安全地保护起来。

　 九、打破信息孤岛

Organizational silos have traditionally compounded problems of data accuracy. Businesses are creatures of habit, and many have become accustomed to working in silos with a singular focus. This can lead to independent databases and ad-hoc initiatives which can, in turn, lead to incomplete or inaccurate data. Failing to link these data sources prevents separate departments from gaining critical insights that could mean the difference between success and failure.

公司孤岛都有传统上数据准确性的复合问题（compounded problem）。商业领域有自己的习惯，许多已经习惯于在孤岛中专注于某一焦点上的工作。这会导致独立的数据集以及临时行动，而这些反过来会产生不充分或不精确的数据。未能将这些数据源联系起来，阻碍了不同部门获得关键洞见，这可能就意味着成功与失败的差别。

And it’s no surprise that those closest to the finance world see this as a priority. Poor data quality costs businesses $14.2M annually, according to Gartner (“The State of Data Quality: Current Practices and Evolving Trends”). It is unlikely that there is another item on the financial statement responsible for this level of attrition. A big data transformation is able to overcome this challenge by systematically integrating these silos – and turning bad data into good information.

48%的金融服务公司认同大数据分析提供了整合数据孤岛的机会。在情理之中的是，与金融世界最紧密相连的公司将大数据视为优先事项。根据Gartner的报道（「数据质量现状：现行的做法和发展趋势」），低质量的数据每年让公司付出1420万美元。财务报表上不可能存在该对这种级别的损失负责的条目。通过系统地整合这些数据孤岛，大数据转变是可以克服这个挑战的——而且可以将低劣的数据转换为有用的信息。

　　 十、机遇来临

But, overall, the future is bright. From data-driven marketing to guiding oilfield operations, big data is fuelling innovation, efficiencies, and new revenue opportunities for every type of organization.

但是，大致来说，未来是光明的。从数据驱动的市场营销到指导油田运营，大数据正在为每种类型的公司加速创新、推动效率以及创收提供机会。

To become a big player in the big data game, an organization needs to take three vital steps. The first relates to the data itself: make sure your information is in a format that allows for easy access and analysis. Most large companies already have this – in fact, they generally have more than they can use. The second is Big Data ready tools, such as Hadoop, MPP Data Warehouse and NoSQL. Both proprietary and open-source tools and platforms are widely available these days – all you need are people capable of putting them to work. That brings us to the third, and usually most challenging issue: expertise. Advanced analytics requires staff with state-of-the-art skills in everything from data science to worldwide privacy laws, along with an understanding of the business and the relevant sources of value.

若想在大数据领域成为重要玩家，一家公司需要采取三个至关重要的步骤。第一步是关于数据本身：确保你的信息形式是方便获取和分析的。大多数大公司实际上已经做到这点了，他们拥有的数据通常比他们使用的数据多得多。第二步是可利用的大数据工具，比如Hadoop、MPP Data Warehouse和NoSQL。近来，拥有专利的或开源的工具和平台随处都可以获得——你需要的是能够利用这些工具和平台完成工作的人。然后我们到了第三步，这通常是最有挑战的问题：专业知识。高级的分析需要员工具备从数据科学到全球范围的隐私法等方方面面的最先进技能，还需要了解商业以及与相关的价值来源。

Big data isn’t just one more technology initiative. In fact, it isn’t a technology initiative at all; it’s a business program that requires specialist technical knowledge. So you can’t just add more capacity and expertise, and expect your IT or marketing functions to begin generating data-based insights. Even if they did, the rest of the company would be unlikely to act on those insights.

大数据不仅是一种技术倡议。事实上，它根本不是技术倡议；它是需要专业的科技知识的商业项目。所以，你不能只是加入更多的能力和专业知识，就期待你的IT或市场部门开始产生基于数据的洞见。即使他们做到了，公司的其他部门也极有可能不会执行这些洞见。

As the analytics leaders have discovered, succeeding with big data requires a different approach: You need to embed big data and skilled people that understand what questions to ask, deeply into your organization. This is the best way to ensure that information and insights are shared across different business units and departments. This also ensures that the whole company recognizes the scale benefits that a well-implemented analytics program can provide.

正如进行数据分析的领导者所发现的那样，在大数据方面取得成功需要另辟蹊径：你需要大数据嵌入和能深入理解你的公司知道提出何种问题的人。这是确保信息和洞见能在不同业务和部门之间分享的最佳方法。这也确保整个公司能认识到一个运行良好的分析程序能提供规模效应。

Ultimately the best way to be prepared is to consult an expert on the best Big Data solutions for your business needs. The process can be further streamlined with a full-service solution that can help you identify the parts of your business where big data analytics can offer the most benefit. The future of data is big and, for the companies that use it eff ectively, the possibilities are endless.

最终，事先做好准备的最佳方式是咨询一位专家，让其针对你的商业需求提供最好的大数据方案。利用一个全套解决方案，这个过程可以进一步流水线化，这个解决方案能帮助你识别出大数据分析能为你的哪些业务带来最多的利益。未来数据会非常大，对于有效使用数据的公司而言，发展潜力是无穷尽的。

责任编辑：陈近梅