譯文丨2017年會(huì)成為大數(shù)據(jù)的掃盲年嗎？

來(lái)源：數(shù)據(jù)觀 時(shí)間：2017-01-04 10:16:35 作者：燈塔大數(shù)據(jù)

　　過(guò)去一年內(nèi)，我們看到了大數(shù)據(jù)的井噴式發(fā)展，數(shù)據(jù)處理分析成為熱門(mén)，大數(shù)據(jù)行業(yè)呈現(xiàn)出信息激進(jìn)之勢(shì)。這導(dǎo)致數(shù)據(jù)科學(xué)家、數(shù)據(jù)應(yīng)用程序員和商業(yè)分析師等大數(shù)據(jù)方面的人才成為當(dāng)下職場(chǎng)最炙手可熱的崗位。

　　但是，我們也能發(fā)現(xiàn)，有能力處理日益增長(zhǎng)的大規(guī)模數(shù)據(jù)計(jì)算的專家和人才，還遠(yuǎn)遠(yuǎn)達(dá)不到市場(chǎng)需求的數(shù)量。

　　有人預(yù)測(cè)，隨著商業(yè)數(shù)據(jù)不斷增多，2017年將成為新數(shù)字信息時(shí)代的開(kāi)始。但是如果沒(méi)有足夠多的專家對(duì)這些數(shù)據(jù)進(jìn)行分析利用，那么這些資源將在很大程度上得不到充分的利用。

　　很不幸，事實(shí)情況是大數(shù)據(jù)的發(fā)展要遠(yuǎn)遠(yuǎn)快于我們學(xué)習(xí)利用數(shù)據(jù)的速度。

　　很多公司的決策者就只能依靠自己的直覺(jué)進(jìn)行決策，這是因?yàn)樗麄冄矍暗臄?shù)據(jù)規(guī)模龐大，雜亂無(wú)章，有些數(shù)據(jù)呈現(xiàn)出的信息甚至看似矛盾，導(dǎo)致了很多重大決策上的失誤。

　　這種情況亟待改變。要改變現(xiàn)狀，就必須提高數(shù)據(jù)科學(xué)家的水平。但是2017年更重要的任務(wù)是讓更多的人了解大數(shù)據(jù)，即為更多的人提供數(shù)據(jù)分析工具和數(shù)據(jù)分析訓(xùn)練，來(lái)提高普通民眾的大數(shù)據(jù)素養(yǎng)水平。

　　100年前，我們說(shuō)要教普通民眾讀書(shū)寫(xiě)字，進(jìn)行掃盲，現(xiàn)在我們也需要針對(duì)大數(shù)據(jù)掃盲，因?yàn)榇髷?shù)據(jù)分析能力將成為未來(lái)最重要的商業(yè)技能之一。

　　那么，要進(jìn)行大數(shù)據(jù)掃盲，我們應(yīng)該怎么做呢?

　　以下是我的一些看法

　　1、大數(shù)據(jù)的組合

　　大數(shù)據(jù)規(guī)模的重要性將逐漸讓位于大數(shù)據(jù)組合。

　　數(shù)據(jù)的存儲(chǔ)殘片越來(lái)越多，很大一部分來(lái)自于云以外的數(shù)據(jù)源，這種情況下，如果沒(méi)有明確目的性的話，儲(chǔ)存數(shù)據(jù)的成本將大大提高。

　　這意味著我們需要建立一個(gè)新的模型，讓公司企業(yè)能夠快速組合他們的大數(shù)據(jù)和小數(shù)據(jù)，方便他們獲得全面的信息并盡快確定數(shù)據(jù)的價(jià)值。組合數(shù)據(jù)也將幫助提高數(shù)據(jù)的準(zhǔn)確性和可讀性。

　　2、混合思維

　　2017年，混合云和多平臺(tái)將成為數(shù)據(jù)分析的主要模型。

　　云的優(yōu)勢(shì)顯而易見(jiàn)，存儲(chǔ)數(shù)據(jù)方便，擴(kuò)容難度小，因此云已經(jīng)成為大數(shù)據(jù)的主要發(fā)展方向。但是單個(gè)的云是不夠的，因?yàn)閿?shù)據(jù)和工作負(fù)荷(workloads)將需要多個(gè)平臺(tái)。此外，數(shù)據(jù)的重要性也決定了多個(gè)云備份的重要性?；旌显坪投嗥脚_(tái)環(huán)境將成為大數(shù)據(jù)的主導(dǎo)模型，工作負(fù)荷和數(shù)據(jù)公布將在云和預(yù)制數(shù)據(jù)儲(chǔ)存系統(tǒng)之間展開(kāi)。

　　3面向全民的自助式服務(wù)

　　免費(fèi)增值將成為新常態(tài)。

　　2017年用戶將更方便的對(duì)他們的數(shù)據(jù)展開(kāi)分析。越來(lái)越多的數(shù)據(jù)可視化工具將以更低的價(jià)格出現(xiàn)在市場(chǎng)上，甚至免費(fèi)。這樣一來(lái)，一些分析學(xué)將面向全民開(kāi)放。越來(lái)越多的人開(kāi)始學(xué)習(xí)分析學(xué)，那么數(shù)據(jù)素養(yǎng)水平自然將會(huì)提高——越來(lái)越多的商人會(huì)知道他們需要哪些數(shù)據(jù)和工具，這些數(shù)據(jù)對(duì)他們的公司有什么用。信息激進(jìn)發(fā)展也將進(jìn)一步得到刺激。

　　4、擴(kuò)容

　　兩年前的用戶導(dǎo)向數(shù)據(jù)挖掘已經(jīng)成為當(dāng)今企業(yè)廣泛使用的商業(yè)智能。

　　2017年，這種商業(yè)智能將取代過(guò)時(shí)的報(bào)告優(yōu)先平臺(tái)。隨著商業(yè)智能成為新的商業(yè)參考結(jié)構(gòu)，它將成為面向全民的自助式數(shù)據(jù)分析工具。商業(yè)智能還將能承接擴(kuò)容、運(yùn)營(yíng)、管理、安全方面不同的需求。

　　5、發(fā)展中分析

　　2017年，我們的關(guān)注點(diǎn)將從“高級(jí)分析”(advanced analytics)轉(zhuǎn)向“發(fā)展中分析”(advancing analytics)。

　　高級(jí)分析是至關(guān)重要的，模型的創(chuàng)建、管理和策劃只有高技術(shù)水平的數(shù)據(jù)專家才能夠做到。但是一旦模型建立起來(lái)了，更多的人就能夠從這些模型中受益，普通人也可以使用這些自助服務(wù)工具。

　　此外，通過(guò)賦予軟件更多智能，提高模型的分析能力，降低復(fù)雜性和分析數(shù)據(jù)洞察的難度。但數(shù)據(jù)分析不應(yīng)該被簡(jiǎn)單定義為黑盒子或過(guò)于規(guī)范化的概念。

　　最近“人工智能”被炒的火熱，但它并不能取代人類分析，只能作為輔助人類的分析工具。人工智能固然能夠幫助回答一些問(wèn)題，但是和回答問(wèn)題同樣重要的是提出問(wèn)題，這只能由人腦來(lái)完成。

　　6、可視化將從純分析工具發(fā)展成為適用于全信息供給鏈的 重要概念。

　　可視化將成為統(tǒng)一中心的強(qiáng)大組件，它采用視覺(jué)方法管理信息資產(chǎn)，準(zhǔn)備視覺(jué)自助服務(wù)數(shù)據(jù)，從而支持現(xiàn)實(shí)視覺(jué)分析。此外，可視化作為傳達(dá)交流信息方式上將取得重大進(jìn)展。這樣一來(lái)，數(shù)據(jù)供應(yīng)鏈可服務(wù)的用戶數(shù)量將會(huì)增加。

　　7、從定制分析應(yīng)用程序到應(yīng)用程序內(nèi)分析

　　應(yīng)用程度的使用者不一定是這個(gè)應(yīng)用程序的開(kāi)發(fā)者。

　　但是我們也應(yīng)該讓這些使用者能夠發(fā)掘他們自己的數(shù)據(jù)。提高大數(shù)據(jù)的素養(yǎng)水平之后，人們就能更好的從分析學(xué)中獲益，因?yàn)樗麄兛梢允褂酶鞣N應(yīng)用程序來(lái)幫助他們結(jié)合自身情況進(jìn)行數(shù)據(jù)分析，還能運(yùn)用分析學(xué)工具自己進(jìn)行數(shù)據(jù)分析的工作。由此看來(lái)，開(kāi)放可擴(kuò)展的、可定制的情景化的分析工具將在未來(lái)成為主流。

　　這些趨勢(shì)為不僅能提高信息活動(dòng)水平，而且將為提高數(shù)據(jù)素養(yǎng)水平提供了基礎(chǔ)。畢竟，可以抓住“另一半人群”(即技術(shù)不熟練的信息工作者和行動(dòng)工作者)的新平臺(tái)和技術(shù)將幫助我們進(jìn)入一個(gè)新時(shí)代，讓合適的數(shù)據(jù)與合適的人以及他們的想法聯(lián)系在一起——這將彌補(bǔ)我們現(xiàn)有數(shù)據(jù)水平與我們從中獲得洞察力的能力之間的鴻溝。

　　這是我們應(yīng)該選擇的道路，它能帶領(lǐng)我們走向一個(gè)更加開(kāi)明的，信息驅(qū)動(dòng)的和基于事實(shí)的新時(shí)代。

　　英文原文

　　2017: The Year Of Data Literacy?

　　We've seen an explosion of data in the past 12 months, so I'm sharing my predictions of what's in store for 2017

　　Over the past 12 months, we’ve seen an explosion of data, an increase in processing it and a move towards information activism. This means the number of employees actively able to work with – and master – the huge amounts of information available, such as data scientists, application developers, and business analysts, have become a valuable entity.

　　Unfortunately, however, there still aren’t enough people with the expertise to handle the ever-increasing, vast levels of data and computing. You would assume, with all the information currently being produced and held by businesses, that 2017 would see us in a new digital era of facts. But, without the right number of specialists to consume and analyse it, there’s a gap in resources. Data is, unfortunately, growing faster than our ability to make use of it.

　　For many business leaders then, this means a reliance on gut instinct to make even the most important decisions. Unable to hone in on the most important insights, they’re presented with multiple – and sometimes conflicting – data points, so the most important ones seem unreliable.

　　The situation needs to change. Yes, that will mean upskilling more data scientists in 2017, but there will be a greater focus on empowering more people more broadly1. That will go beyond information activists and towards providing more people with the tools and training to increase data literacy. Just as reading and writing skills needed to move beyond scholars 100 years ago, data literacy will become one of the most important business skills for any member of staff.

　　So, what will change to see culture-wide data literacy become a reality? Here are my predictions:

　　1. Combinations of data – Big data will become less about size and more about combinations.

　　With more fragmentation of data and most of it created externally in the cloud, there will be a cost impact to hoarding data without a clear purpose. That means we’ll move towards a model where businesses have to quickly combine their big data with small data so they can gain insights and context to get value from it as quickly as possible. Combining data will also shine a light on false information more easily, improving data accuracy as well as understanding.

　　2. Hybrid thinking – In 2017, hybrid cloud and multi-platform will emerge as the primary model for data analytics.

　　Because of where data is generated, ease of getting started, and its ability to scale, we’re now seeing an accelerated move to cloud. But one cloud is not enough, because the data and workloads won’t be on one platform. In addition, data gravity also means that on-premise has long staying power. Hybrid and multi-environment will emerge as the dominant model, meaning workloads and publishing will happen across cloud and on-premise.

　　3. Self-service for all – Freemium is the new normal, so 2017 will be the year users have easier access to their analytics.

　　More and more data visualization tools are available at low cost, or even for free, so some form of analytics will become accessible across the workforce. With more people beginning their analytics journey, data literacy rates will naturally increase — more people will know what they’re looking at and what it means for their organization. That means information activism will rise too.

　　4. Scale-up – Much a result of its own success, user-driven data discovery from two years ago has become today’s enterprise-wide BI.

　　In 2017, this will evolve to replace archaic reporting-first platforms. As modern BI becomes the new reference architecture, it will open more self-service data analysis to more people. It also puts different requirements on the back end for scale, performance, governance, and security.

　　5. Advancing analytics – In 2017, the focus will shift from 'advanced analytics' to 'advancing analytics.

　　Advanced analytics is critical, but the creation of the models, as well as the governance and curation of them, is dependent on highly-skilled experts. However, many more should be able to benefit from those models once they are created, meaning that they can be brought into self-service tools. In addition, analytics can be advanced by increased intelligence being embedded into software, removing complexity and chaperoning insights. But the analytical journey shouldn’t be a black box or too prescriptive. There is a lot of hype around 'artificial intelligence,' but it will often serve best as an augmentation rather than replacement of human analysis because it’s equally important to keep asking the right questions as it is to provide the answers.

　　6. Visualization as a concept will move from analysis-only to the whole information supply chain – Visualization will become a strong component in unified hubs that take a visual approach to information asset management, as well as visual self-service data preparation, underpinning the actual visual analysis. Furthermore, progress will be made in having visualization as a means to communicate our findings. The net effect of this is increased numbers of users doing more in the data supply chain.

　　7. Focus will shift to custom analytic apps and analytics in the app – Everyone won’t — and cannot be —both a producer and a consumer of apps.

　　But they should be able to explore their own data. Data literacy will, therefore, benefit from analytics meeting people where they are, with applications developed to support them in their own context and situation, as well as the analytics tools we use when setting out to do some data analysis. As such, open, extensible tools that can be easily customized and contextualized by application and web developers will make further headway.

　　These trends lay the foundation for increased levels of not just information activism, but also data literacy. After all, new platforms and technologies that can catch 'the other half' (i.e., less skilled information workers and operational workers on the go) will help usher us into an era where the right data becomes connected with people and their ideas — that’s going to close the chasm between the levels of data we have available and our ability to garner insights from it. Which, let’s face it, is what we need to put us on the path toward a more enlightened, information-driven, and fact-based era.

　　注：本文摘自數(shù)據(jù)觀入駐自媒體—燈塔大數(shù)據(jù)，轉(zhuǎn)載請(qǐng)注明來(lái)源，微信搜索“數(shù)據(jù)觀”獲取更多大數(shù)據(jù)資訊。

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責(zé)任編輯：湯德正