Monica Rogati 是領英的數據科學家，她給了我們在挖掘數據時十個應該避免的常見錯誤。
- 假設數據是干凈的。數據清洗通常占了工作中大部分時間，而且簡單的清洗動作也常常揭示出重要的模式。比如問道“是這個方法導致數據中的30%都為NULL嗎？90210這個郵編對應的客戶真的有那么多嗎？”在拿到數據時就進行核對，以確保其有效和有用。
- 數據處理不規范。假設你正在制作一個熱門結婚圣地的列表。你可以計算飛去某地參加婚禮的人數，但如果不考慮所有去那個地方旅客的總人數的話，你的列表僅僅代表了一個航空業發達的城市列表。
- 剔除異常值。假設有21個人每天使用你的產品一千次，這些人可能是你的超級粉絲，當然也可能僅僅是爬你網站的爬蟲程序。但不管他們是誰，不應該隨便的剔除他們。
- 包含異常值。從某個角度來說這21個人每天用1000次你的產品很有趣，因為他們能帶給你意想不到的東西。但處理這些人沒有合適的通用模型，所以在某些功能上需要剔除他們，否則“推薦功能”可能給你所有的忠實粉絲都推了千篇一律的東西。
- 忽視時間周期性。看了數據后驚嘆實習生是今年增長最快的職位，定睛一看才發現是7月。在尋找規律時，如果忽視了時刻、工作日、月份會導致錯誤的決策。
- 匯報增長情況時忽視規模。情境非常重要，否則剛剛開始時，你爸爸注冊了一次，增長率就翻了一倍。
- 數據輸出，如果你不知道該看什么，那dashboard基本沒什么用。
- 狼來了。你設置了很多報警好在出問題時第一時間修復，但當你的閾值設的很敏感時，這些警報就像“狼來了”一樣，你慢慢就開始無視它們。
- 不要采集這里的數據綜合癥。將你的數據和其他來源的數據混合，可能會產生有價值的東西。“你最好的客戶來的地方都非常喜歡日料嗎？”。這些會給你很多很好的下一步行動的想法，甚至會影響你的增長策略。
- 聚焦噪聲數據。即使什么都沒有，我們人類也能給他找出模式來。擺脫虛榮指標的數據，退后一步關注更遠大的目標。
How to Think Like a Data Scientist
Monica Rogati, a data scientist at LinkedIn, gave us the following 10 common pitfalls that entrepreneurs should avoid as they dig into the data their startups capture.
- Assuming the data is clean. Cleaning the data you capture is often most of the work, and the simple act of cleaning it up can often reveal important patterns. “Is an instrumentation bug causing 30% of your numbers to be null?” asks Monica. “Do you really have that many users in the 90210 zip code?” Check your data at
the door to be sure it’s valid and useful. - Not normalizing. Let’s say you’re making a list of popular wedding destinations. You could count the number of people flying in for a wedding, but unless you consider the total number of air travellers coming to that city as well, you’ll just get a list of cities with busy airports.
- Excluding outliers. Those 21 people using your product more than a thousand times a day are either your biggest fans, or bots crawling your site for content. Whichever they are, ignoring them would be a mistake.
- Including outliers. While those 21 people using your product a thousand times a day are interesting from a qualitative perspective, because they can show you things you didn’t expect, they’re not good for building a general model. “You probably want to exclude them when building data products,” cautions Monica. “Otherwise, the ‘you may also like’ feature on your site will have the same items everywhere—the ones your hardcore fans wanted.”
- Ignoring seasonality. “Whoa, is ‘intern’ the fastest-growing job of the year? Oh, wait, it’s June.” Failure to consider time of day, day of week, and monthly changes when looking at patterns leads to bad decision making.
- Ignoring size when reporting growth. Context is critical. Or, as Monica puts it, “When you’ve just started, technically, your dad signing up does count as doubling your user base.”
- Data vomit. A dashboard isn’t much use if you don’t know where to look.
- Metrics that cry wolf. You want to be responsive, so you set up alerts to let you know when something is awry in order to fix it quickly. But if your thresholds are too sensitive, they get “whiny”— and you’ll start to ignore them.
- The “Not Collected Here” syndrome. “Mashing up your data with data from other sources can lead to valuable insights,” says Monica. “Do your best customers come from zip codes with a high concentration of sushi restaurants?” This might give you a few great ideas about what experiments to run next—or even influence
your growth strategy. - Focusing on noise. “We’re hardwired (and then programmed) to see patterns where there are none,” Monica warns. “It helps to set aside the vanity metrics, step back, and look at the bigger picture.“
節選自Alistair Croll,Benjamin Yoskovitz,《Lean Analytics》
