{"id":1453,"date":"2019-11-11T13:40:30","date_gmt":"2019-11-11T13:40:30","guid":{"rendered":"http:\/\/xtract.io\/blog\/?p=1453"},"modified":"2025-01-21T07:58:33","modified_gmt":"2025-01-21T07:58:33","slug":"data-quality-data-preparation-mistakes","status":"publish","type":"post","link":"https:\/\/www.xtract.io\/blog\/data-quality-data-preparation-mistakes\/","title":{"rendered":"Five Data Preparation Mistakes to Avoid Like the Plague"},"content":{"rendered":"\n

Let\u2019s play a quick puzzle game.<\/p>\n\n\n\n

Question:<\/strong> Name the company.<\/p>\n\n\n\n

Clue 1:<\/strong> This company is one of the big 4 technology companies on the globe along with Amazon, Apple, and Google.<\/p>\n\n\n\n

Clue 2:<\/strong> It sprouted out in 2004.<\/p>\n\n\n\n

Clue 3:<\/strong> It reported global revenue of $55.8 billion as of 2018<\/a>.<\/p>\n\n\n\n

That should\u2019ve been a piece of cake! (I have no presents for you, though :\/).<\/p>\n\n\n\n

The answer<\/strong>: Facebook.<\/em><\/p>\n\n\n\n

Apart from these well-known facts about Facebook, I\u2019d like to take you back to a few years.<\/p>\n\n\n\n

It was 2004 when Mark Zuckerburg along with his four Harvard University friends founded Facebook. Two years went by and the team was pulling out all the stops to grow their company. In 2006, Zuckerburg hired the first Data Scientist\u200a\u2014\u200aJeff Hammerbacher, a math nerd who was fresh out of college. He was given a princely title of a research scientist, whose role was to primarily find out how people use social networking service.<\/p>\n\n\n\n

Five Data Preparation Blunders You Need to Get Rid <\/strong><\/h3>\n\n\n\n

In a Bloomberg interview<\/a>, Jeff shared his experience on crunching data and building a new class of analytical technology when Facebook had no tools to do these yet. He later channelized his data science proficiency to provide better cancer treatments by analyzing large biological datasets after leaving facebook.<\/p>\n\n\n\n

All Data Scientists like Jeff, end up spending a lot of their time on data preparation rather than channelizing their time and technical know-how into modeling, computation, and training.<\/p>\n\n\n\n

Why Faulty Data Makes Your Castles Wonky<\/strong><\/h2>\n\n\n\n

Data preparation is a tedious task. It involves a ton of time and effort and is required to be error-free to make ingenious inventions. Data Science is taking a direction towards applications of data in transforming infrastructure, transportation, environment, medicine, and many other significant arenas for a better and advanced living.<\/p>\n\n\n\n

Today, I\u2019m going to take you through certain common data preparation mistakes that are costly and cause serious repercussions like wrong insights and strategy, iterations of complex models, and dysfunction of analytical models.<\/p>\n\n\n\n

Five Data Preparation Blunders You Need to Get Rid <\/strong>off<\/h3>\n\n\n\n

1. Losing the context of the use case\u200a\u2014\u200aWhy deviation is dangerous<\/strong><\/h4>\n\n\n\n

The technical expertise vested with the IT departments enables operations and implementation of data preparation. While a combination of this control between IT and business departments gives a healthy blend of business know-how and technical expertise, data preparation completely vested with the IT department has a minor setback.<\/p>\n\n\n\n

To continue reading, head to <\/a>DZone<\/a> where this article was originally published.<\/em><\/strong> <\/p><\/blockquote>\n\n\n

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