{"id":970,"date":"2018-11-06T02:39:43","date_gmt":"2018-11-05T23:39:43","guid":{"rendered":"http:\/\/kusuaks7\/?p=575"},"modified":"2021-12-15T04:09:31","modified_gmt":"2021-12-15T04:09:31","slug":"the-power-of-goal-setting-in-data-science","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/the-power-of-goal-setting-in-data-science\/","title":{"rendered":"The Power of Goal-Setting in Data Science"},"content":{"rendered":"

Ready to learn Data Science? Browse\u00a0Data Science Training and Certification<\/a> courses developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

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Use Google\u2019s goal-setting method OKRs to set your Data Science project up for\u00a0success<\/h3>\n

Google<\/a>\u00a0is well aware of the power of goal-setting. In 2004, early investor John Doerr introduced the young company to \u201cObjectives and Key Results\u201d (OKRs). What has followed is a miraculous tale of growth and innovation. Larry Page, Google\u2019s long-time CEO, argues that OKRs had a substantial impact on the companies success. Google isn\u2019t alone \u2014 Amazon, Zalando, and Intel use OKRs to this day.<\/p>\n

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Photo by\u00a0Annie Spratt<\/a>\u00a0on\u00a0Unsplash<\/a><\/p>\n

First, this article explains how OKRs work. Second, it aims to convince Data Scientists to use OKRs to steer their projects. Often enough, Data Scientists drift into enduring research or optimizing less-valuable metrics. As a result, time and resources are wasted, which endanger a successful delivery. OKRs provide strategic guidance and measurability, which help you achieve your project goal.<\/p>\n

The content is based on the book \u201cMeasure What Matters<\/a>\u201d by famous Venture Capitalist John Doerr and personal experience with OKRs. Let\u2019s learn. \ud83d\udcd7<\/p>\n<\/section>\n

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The Power of Goal-Setting<\/h3>\n

\u201cA goal properly set is halfway reached.\u201d\u200a\u2014\u200aZig Ziglar<\/p><\/blockquote>\n

Goal-setting is arguably the most important step to start any project. While research isn\u2019t clear on the benefits of proper goal-setting, we can deduct the advantages and disadvantages. If we fail to state a clear goal, co-workers cannot collaborate, actions are not aligned and we don\u2019t know if we\u2019ve reached the goal. In short, havoc looms.<\/p>\n

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Photo by\u00a0Patrick Fore<\/a>\u00a0on\u00a0Unsplash<\/a><\/p>\n

Accordingly, every Data Science project aims to fulfill a goal. The breadth of goals might vary from researching a new model to creating a prototype for improving an existing system. Even so, accurate goal-setting is paramount for Data Science projects. Data Science projects are well-suited for accurate goal-setting because we can compare the results of our model with the required metric.<\/p>\n

Let\u2019s explore the Intel-pioneered and Google-perfected method for goal-setting \u2014 OKRs.<\/p>\n<\/section>\n

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Objectives and Key\u00a0Results<\/h3>\n

Objectives and Key Results is the method to transparently align and prioritize resources towards a common goal. The approach was established by Intel\u2019s\u00a0legendary\u00a0<\/a>CEO Andy Grove in the 1980\u2019s. OKRs can be used on a company, team, project or personal level by both corporations and organizations. OKRs are widely used to align strategic initiatives across companies in Silicon Valley and Europe.<\/p>\n

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Photo by\u00a0rawpixel\u00a0on\u00a0Unsplash<\/a><\/p>\n

OKRs are set at the beginning of the project and define the end-goal. Find below an example of personal OKRs from\u00a0Rick Klau<\/a>, who worked at Blogger after they got acquired by Google:<\/p>\n

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You can see that Rick declared the Objective to be \u201cImprove Blogger\u2019s Reputation\u201d along with measurable Key Results. At the end of each quarter, these Key Results are graded from 0 to 1.<\/p>\n

Objectives\u00a0<\/strong>are the\u00a0\u201cWhat\u201d<\/em>. What is the goal of the project? They\u2026<\/p>\n