Getting Your Analytics Straight?

讓你的數(shù)據(jù)分析直接了當(dāng)

Alexander Youngblood Soria

Predictive Intelligence, Machine Learning, Artificial Intelligence, Advanced Analytics.

關(guān)鍵詞：智能預(yù)測(cè)脚线，機(jī)器學(xué)習(xí)渠旁，人工智能顾腊，進(jìn)階分析

All these terms are gaining attention at major marketing, business, and analytics conferences. If you’re like most people, you probably walk away from these conferences feeling like your company is behind in its analytical capabilities.?However, don’t let the chatter fool you. The reality is that many companies aren’t ready to digest the most advanced machine learning algorithms just yet – but that shouldn’t stop you from building a roadmap for how to get there:

以上的這些關(guān)鍵詞獲取了大部分市場(chǎng)營(yíng)銷酱鸭，商務(wù)抱既，以及數(shù)據(jù)分析會(huì)議的焦點(diǎn)蚀之∈偾矗或許你會(huì)像大部分人一樣，徑直越過(guò)這些會(huì)議痪署，覺(jué)得這些分析技能還離你公司很遠(yuǎn)。但是悯森，不要讓眾所云云迷惑你，事實(shí)上只是大部分的公司不具備消化這些先進(jìn)計(jì)算機(jī)學(xué)習(xí)算法的能力幻碱，并不意味著你要止步于摸索靠近這些技能的道路坟岔。

Step 1: Invest in People

第一步：花錢請(qǐng)對(duì)的人

Data scientists, representing that rare mix of analytical expertise and business acumen that many companies are looking for today, have never been in higher demand.?Hiring one of these sought-after talents to join your company is the first step to building out your analytics capabilities.?However, getting the right person “on the bus,” as leadership expertJim Collinsadvises in his book “Good to Great,” is difficult in itself.

數(shù)據(jù)分析師邻耕，代表著很少部分的人啼辣，這些人既是數(shù)據(jù)分析的專家鸥拧，也有商務(wù)上敏銳的嗅覺(jué)，這些人是大部分公司都在尋找的削解，并且現(xiàn)在的需求是前所未有的高漲富弦。為自己的公司聘請(qǐng)一個(gè)受市場(chǎng)歡迎的人才是塑造公司數(shù)據(jù)分析能力的第一步。當(dāng)然氛驮，找到一個(gè)這樣對(duì)的人并不是意見(jiàn)簡(jiǎn)單的事腕柜，領(lǐng)導(dǎo)力專家 Jim Collins 在他的書(shū)“從優(yōu)秀到卓越”提到這招聘的難度矫废。

The skillset required of a data scientist has?evolved; not only are data scientists asked to be the creators and translators of data-driven analytics solutions for the business, they are also increasingly the conduit to technology teams. You want to look beyond purely technical skills on a resume and seek out a talent for communicating complex topics in a way that a business user can understand, as well as some understanding of the technology environments from which the data originates and in which the solutions will be deployed.

數(shù)據(jù)分析師不再是傳統(tǒng)意義上盏缤，只懂分析數(shù)據(jù)，需要升級(jí)為掌握一套的技能蓖扑，需要分析并解讀數(shù)據(jù)唉铜，為解決商業(yè)難題提供方法，同時(shí)也需要跟技術(shù)團(tuán)隊(duì)對(duì)接赵誓。在瀏覽人才簡(jiǎn)歷的時(shí)候打毛，不單只是尋找懂相關(guān)技術(shù)技能的，還需要找能懂得復(fù)雜商務(wù)問(wèn)題俩功，能像業(yè)務(wù)同事一樣去溝通的幻枉，同時(shí)也要能分析讀取數(shù)據(jù)的，知道需要用什么方法來(lái)開(kāi)展業(yè)務(wù)的人才诡蜓。

ACTION: Look for data scientists with a diverse background.

舉措：找數(shù)據(jù)分析師要找有復(fù)合背景的熬甫。

Step 2: Get Back to the Basics

In the business world, the answer isn’t always found in the newest, coolest advanced algorithms or sophisticated machine learning applications – opportunity almost always reveals itself in the basics.?In analytics, the “basics” typically refers to descriptive, predictive, and prescriptive statistics. What’s the difference?TechTargetdefines them this way:

在商業(yè)世界里，答案不一定隱藏在最新潮先進(jìn)的算法或者最復(fù)雜得計(jì)算機(jī)學(xué)習(xí)應(yīng)用中——恰恰是隱藏在基礎(chǔ)中蔓罚，在數(shù)據(jù)分析中椿肩，“基礎(chǔ)”一詞恰恰指的的描述性瞻颂，預(yù)測(cè)性，指定的數(shù)據(jù)分析中郑象。具體是什么呢贡这？ Tech Target給出以下的定義：

Descriptive analytics aims to provide insight into what has happened.

描述性指的是洞察出究竟發(fā)生了什么。

Predictive analytics helps model and forecast what might happen.

預(yù)測(cè)性指的是通過(guò)模型等預(yù)測(cè)將會(huì)發(fā)生什么厂榛。

Prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters.

指向性指的是在眾多的方法中找到最佳的或者是效果最大化的方法盖矫，給出已知參數(shù)。

Think of descriptive, predictive, and prescriptive modeling as related tools in your data science toolbox; building off of these initial analytics techniques creates a solid foundation of knowledge to move to the next level in data science.?That next level may very well involve more complex machine learning – but start with the basics.

綜合以上三種維度出來(lái)的分析模型工具運(yùn)用在你的數(shù)據(jù)分析中击奶，通過(guò)這些最初的分析為數(shù)據(jù)分析進(jìn)入下一階段奠定堅(jiān)實(shí)基礎(chǔ)辈双。下一階段的數(shù)據(jù)分析將會(huì)涉及更加復(fù)雜得計(jì)算機(jī)學(xué)習(xí)技能——建立在以上基礎(chǔ)上。

ACTION: Frame a problem in terms of descriptive analytics, which may very well answer 95 percent of a business stakeholder’s questions right away.

舉措：通過(guò)描述性的分析來(lái)搭建問(wèn)題框架柜砾，這基本上能直接解決一個(gè)投資人98%的疑問(wèn)湃望。

（真的做不完了，還有一堆工作沒(méi)處理痰驱，下面簡(jiǎn)短【捂臉哭】）

Step 3: Organize Data in an Actionable Format

第三步：將數(shù)據(jù)輸出變成一種可執(zhí)行的部署

Data science is much more than cleaning and transforming data, running queries, and writing code, although there is a lot of that too. It’s about providing results back to your business stakeholders in a way that’s easily consumable. Spreadsheets full of numbers force business users to work through minute details of how a problem was solved when most of the time what they’re really looking for are answers and a recommendation to act. Even when the data science behind a solution is complex, you should consistently communicate the results in ways that simplify and summarize the solution in clear language. Even better, look for opportunities to deploy dashboarding and reporting software technologies to help automate and respond to some of the simpler, more commonly asked questions (e.g. what is our year-over- sales by product category?). This empowers your business stakeholders and frees up the data scientists to work on the hardest problems.

ACTION: Enable key business stakeholders to answer their own questions with data via dashboards and reporting.

舉措：讓數(shù)據(jù)分析的結(jié)果簡(jiǎn)單易懂证芭，這樣重要的投資者也能通過(guò)白板上的數(shù)據(jù)來(lái)進(jìn)行商業(yè)匯報(bào)。

Step 4: Make Data Meaningful for the Business

第四步：讓數(shù)據(jù)分析對(duì)商業(yè)更具有意義

We know it’s important for a data scientist to be an effective translator, finding the secrets hidden within the data and translating them into language that business users can understand and act upon. This deep dive into an understanding of customer behaviors lets marketers tailor their communications, interactions, and messaging in meaningful and relevant ways. Similarly, the data scientist should work hand in hand with the business to help it understand how industry trends and consumer preferences might change in the future, and to predict which customers may be affected most by a change.

ACTION: Integrate your data scientists tightly with the business so that they can learn to identify more relevant stories arising from the analytics, and anticipate key questions from stakeholders.

舉措：讓數(shù)據(jù)分析師緊密的融合在商業(yè)進(jìn)行中担映，讓他們能更好的理解數(shù)據(jù)發(fā)生的背景檩帐，甚至讓他們來(lái)解答投資人的關(guān)鍵問(wèn)題。

The industry is excited about the possibilities that predictive intelligence can unlock. With the rise of machine learning, everyone has an eye towards harnessing the power of advanced analytics to take the guesswork out of predicting the future. While there is no doubt that these complex capabilities are change agents for how marketers and business strategists will generate insights with data in the long term, don’t overlook the value of deploying your data scientists to solve problems with more straightforward analytics – the basics. As an acquaintance of mine puts it, “Don’t be the science fair in the back room – provide actionable insights for the business.” This can only be done by getting your analytics straight.