善用數據，為企業決策服務
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????幾個月前我收到了一份便函，要求我所在的埃森哲(Accenture)辦公室的員工們必須保持室內整潔，接受定期檢查。碰巧我是一個喜歡整潔的人，但我希望知道是否有數據證明整潔的辦公室就能促進生產效率的提高。 ????毫不奇怪，我的提問沒有得到很好的回答，答復是“基肖爾，整潔的辦公室會給到訪的客戶留下更好的印象。” ????這聽起來有幾分道理，因此我繼續問，是否有數據支持這種觀點，即在拜訪過我們整潔的辦公室后，客戶更可能購買我們的服務或對我們有更正面的看法。至此，我似乎是在本應顯而易見的事情上浪費人們的時間了，有幾位同事甚至建議我別再糾纏這個問題了。 ????在當今高度競爭的全球商務環境中，你應該如何運用數據來支持你的大小決定，正是企業應該探討的話題。而且隨著商業分析理論的完善，沒有理由不基于充分的信息作決定，而且很多時候支持性數據完全可以隨手拈來。 ????如今，你的企業可以輕松獲得關于客戶購買模式、自身供應鏈內商品動向等多年的數據。而且，你的雇員、你的客戶、你的競爭對手以及你競爭對手的雇員和客戶也都在談論，包括在博客和微博上提供對你的企業可能有用的信息。當今的一些技術——如數據/文字挖掘和機器學習——能幫助你對所有這些數據進行分析，而云計算也將信息研究規模提升到了可能幾年前還不可想象的水平。 ????大多數企業領導人現在都要求重要決定必須要有經驗數據的支持。隨著分析理論的進步，現在我們已差不多到了這樣的地步：即便是最日常的決定，各個層面的管理人士都必須問這樣一個問題，“我們認為是這樣，還是我們知道是這樣？” ????越來越多的公司都在朝著這個方向轉變。他們必須要了解運用數據為其決策和行動提供指導的潛在機會和挑戰： 1. 謹防數據誤用 ????分析理論是個強大的工具，借用蜘蛛俠的話，“能力越大，責任越大”(with great power comes great responsibility)。企業應謹防三類常見的數據誤用。 ????首先，擁有實時數據并不意味著你能夠或應該做出實時決定。不同種類的數據有不同的時間尺度：例如，收銀機反映的是當時的銷售額，但供應鏈數據只能反映上次下單或上次訂單的運輸派車。必須所有數據在手，才能做出好的決定，因此你的決策速度只能取決于最慢的因素。 ????第二，分析理論能幫助你優化企業流程，將冗余和低效降至最低。但企業流程不能過度優化，否則可能導致犯錯余地為零。高度優化的流程——如零庫存或保持極低的庫存，根據需求隨時補充——是非常脆弱的，因為可能出現你無法控制的局面，而你的犯錯余地為零。 ????最后，不要做無謂的決定。有好的數據，并不意味著你總要據此做點什么決策。 2. 做好準備，隨時應對瞬息萬變的信息世界 ????一個基于數據采取行動的公司能做出非常具體、精確的決定。事實上，你的決定可能基于一些細微之處，如“周日晚上在那些近期表現不錯的主場足球隊所在地區多備些啤酒”。但這樣的決定隨時可能調整，隨球隊的命運而快速變化。 3. 解讀海量數據 ????當今企業擁有的信息已超過了他們所能利用或能采取行動的范圍，因為很多不同的信息往往都是孤立的。未來的企業將需要花大量的時間和精力來整合它們擁有的有用信息。 ????以醫藥公司為例，傳統上依賴臨床試驗數據確立新藥的功效和副作用。如果臨床試驗沒有問題，他們就能宣稱對藥物的不良反應不承擔法律或道德責任。但隨著互聯網和社交媒體的出現，如今他們必須監控公共信息源，將這些信息與臨床數據結合。當一家公司出現問題時，我們將更多地聽到公司回應以“我本該知道”，而不是“我不知道”或“我不可能早就知道”。 4. 不要迷失于信息汪洋 ????如此多的數據可能很容易就會讓未來的企業經理們誤入“拖延決策，直到完成所有數據分析”的陷阱，但完成所有數據分析可能是無法完成的任務。你應該警惕陷入分析迷局的三個警示信號。 ????首先，警惕管理層的“過擬合”傾向——統計學詞匯“過擬合”指的是一旦模式已經發現，搜集更多數據的價值趨于下降。數據搜集是有代價的。不行動也是有代價的。一個具有數據頭腦的公司必須知道過擬合成本。 ????第二，不要苦等不存在的數據。具有數據頭腦的公司知道信息差的存在，知道如何通過實驗打破此類僵局。 ????最后，要知道你的企業在行動時愿意承受何種水平風險。如果員工因為行動失敗所受處罰多于不行動，大多數員工都會寧愿不行動，也不愿將事情搞得一團糟。針對行動失敗和根本不行動建立健全的懲罰機制，能提供幫助。 5. 發揮直覺 ????依賴數據并不意味著不需要直覺。是的，科學確實是以經驗為根據，是理性的。但科學家們不是。大多數受人尊敬的科學家們都是在保持客觀性的同時，發揮創造力、直覺和冒險精神。這為企業提供了一個良好的參照。 ????未來基于分析決策的企業將明顯不同于今日的企業。回到文章開始我描繪的那些干凈的辦公桌、效率、客戶以及是否有數據支持這樣一個日常性決定。就此案而言，無數據提供。但為防萬一，我還是將自己的辦公桌弄得比以前更整潔了一些。 ????本文作者基肖爾?斯瓦米納坦(Kishore S. Swaminathan)是埃森哲的首席科學家，以及埃森哲技術實驗室(Accenture Technology Labs)的系統集成研究全球總監。 |
????A few months ago, I received a memo saying that employees in my facility at Accenture must keep their offices clean, subject to regular inspections. As it happens, I am fairly tidy, but I wanted to understand if there was any data to show that clean offices lead to higher productivity. ????Not surprisingly, my request was sidestepped, and I was told, "Kishore, clean offices leave better impressions with visiting customers." ????That sounded reasonable, so I asked if there was any data to show that our customers are more likely to buy our services or view us more favorably after visiting our clean offices. Now I was wasting people's time on what should be obvious, and a few colleagues even suggested that I move on. ????In today's highly competitive global business environment, how you should use data to support your decisions -- large and small -- is exactly the kind of conversation that organizations should be having. And with advances in business analytics, there is every reason to make well-informed decisions since supporting data is, in many cases, readily available at your fingertips. ????Your company now can easily gain access to several years of data about your customer's buying patterns and the movement of goods through your supply chain. And your employees, your customers, your competitors, as well as the employees and customers of your competitors are all talking, blogging and tweeting, providing potentially useful information for your business. Today's technologies -- such as data and text mining and machine learning -- allow you to analyze all this data, and cloud computing allows you to examine this information at a scale that was not possible just a few years ago. ????Most business leaders now demand empirical data to support important decisions. With advances in analytics, we are nearing the point where every executive at every level will have to subject even the most mundane business decision to the following question: "Do we think this is true, or do we know this is true?" ????As more organizations move in this direction, though, they ought to be aware of the potential opportunities and challenges that go along with using data to guide more of their decisions and actions: 1. Avoiding the misuse of data ????Analytics places tremendous power in the hands of its users, and to borrow from Spiderman, "with great power comes great responsibility." Organizations should watch for three common misuses of data. ????First, just because you have access to real-time data doesn't mean you can or should make real-time decisions. Different types of data have different time scales: for example, your cash register reflects your sales the moment they happen, but your supply chain data can only reflect the last time an order was placed or a truck carrying your order was dispatched. Best decisions are made with all the data at hand, so you can only make decisions as fast as your slowest moving event. ????Second, analytics enables you to optimize your business processes to minimize redundancies and inefficiencies. However, be careful not to overly optimize your business processes to the point that there is no room for error. Highly optimized processes -- just-in-time inventory or keeping a very small inventory and constantly replenishing it based on demand being an example -- are very fragile because circumstances beyond your control could arise, and there is little room for error. ????Finally, watch out for making decisions where none are needed. Having good data does not mean you always need to act on it. 2. Preparing for a rapidly changing information world ????A company that bases its actions on data can make very specific, fine-tuned decisions. In fact, your decisions can be based on subtleties such as "stock more beer on Sunday nights in locations where the home football team is on a winning streak." But these kinds of decisions are highly sensitive and can change as rapidly as the fortunes of a football team. 3. Making sense of a ton of data ????Today's enterprises have more information than they can use or act on because many difference pieces of information are often isolated from each other. The enterprise of the future will need to devote a lot of time and energy toward integrating the useful information it has. ????Pharmaceutical companies, for example, have traditionally relied on clinical trials data to establish the efficacy and side effects of drugs. If a problem didn't come up in clinical trials, they could claim legal or ethical immunity from adverse effects of their drugs. But with the advent of the Internet and social media, they must now monitor public sources and integrate that information with their clinical data. "I should have known" will be the new normal, replacing the "I did not know" or "I could not have known" response to a company's unexpected problems. 4. Avoiding paralysis by information overload ????With access to so much data, the business manager of the future could easily fall into a trap of putting off decisions until everything has been analyzed, which may never happen. Look out for three warning signs of analysis-paralysis. ????First, beware the managerial tendency to "over-fit the curve" -- a statistical term that refers to the diminishing value of gathering additional data once you find a pattern. Data collection has a price. Not taking action also has comes at a price. And a data savvy organization must understand the cost of over-fitting. ????Second, do not fall into the trap of waiting for data that just does not exist. Data savvy organizations understand information gaps and how experimentation can break these kinds of logjams. ????Finally, know what level of risk your organization is willing to tolerate when they take action. If you penalize employees more for failed action than for inaction, most employees will prefer to not take action rather than mess up. Having solid guidelines for how to treat failure versus not acting at all can help. 5. Intuition isn't dead ????Relying on data does not mean that there is no room for intuition. Yes, it is true that science is empirical and dispassionate. But scientists are not. Most respected scientists blend objectivity with creativity, instinct and risk taking. It's a good model for organizations. ????The enterprise of the future, based on analytical decision making, will be considerably different from today's enterprise. All of this goes back to that original scenario I painted about clean desks, efficiency, clients and whether there was any data to support a rather mundane policy decision. In this case, none was provided. But I keep my desk a littler cleaner just in case. ????Kishore S. Swaminathan is Accenture's chief scientist and the global director of Accenture Technology Labs' systems integration research. |
