{"id":620,"date":"2018-03-09T02:11:44","date_gmt":"2018-03-08T23:11:44","guid":{"rendered":"http:\/\/kusuaks7\/?p=225"},"modified":"2025-08-27T12:56:14","modified_gmt":"2025-08-27T12:56:14","slug":"why-dataops-may-be-in-your-future","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/why-dataops-may-be-in-your-future\/","title":{"rendered":"Why DataOps May Be in Your Future"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tReady to become DevOps Engineer? Browse courses<\/a>\u00a0like AWS Certified DevOps Engineer\u2014Professional Exam Training<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nRecalling my college student days, I always adored professors who thoroughly walked through what syllabus topics would appear on an upcoming test.\u00a0 That effort made it easier to prepare, and even spark ideas on how a study group can tackle the course material.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFast forward to adulthood, and I\u2019m always searching for guidance in how to do multistep tasks, wishing for teamwork on coordinating tasks.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tI\u2019m sure analytics practitioners feel a similar pain.\u00a0 As analytics adopts machine learning initiatives, analysts are feeling tension from growing two skillsets in their professional hip pockets \u2013 one, from advance statistics, and the other, from mining data. \u00a0Understanding where to apply those skills in a business initiative has become \u2013 well, a high-pressure final exam that no one wants to fail.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tEnter DataOps, a general methodology to design, implement, and maintain a distributed data architecture.\u00a0 Its purpose is to provide teams a process framework to manage quality improvements in data and to reduce cycle time for data analysis. \u00a0DataOps blends software development technique and agile management principles into analytics pipelines based on the data.\u00a0 \u00a0Thus DataOps is a framework \u2013 a \u201csyllabus\u201d, if you will – for managers and analyst to know what skills can best manage the given data and what to do based the conditions associated with the data.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe practice of DataOps is based on a variation of DevOps. \u00a0In DevOps software developers and operation specialists are brought together under agile management to quality control software development. With DataOps data scientists and operation specialists work together to manage similar tactics, except the focus is on data and associated solutions from communication apps to analytics software.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThat last sentence may illicit a \u201cwait-a-minute\u201d feeling \u2013 aren\u2019t data scientists and analytics practitioners suppose to work with others professional, quickly responding to analytic needs anyway?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe short abridged answer is yes.\u00a0 But we have to look at the unabridged version to understand the practices behind DataOp\u2019s value.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tYes, people ultimately come together around data and metrics, all in the name of advocating real-time decisions. \u00a0But the traditional usage of analytics held a reporting perspective \u2013 metrics that noted past activity.\u00a0 Today, as machine learning is increasingly applied in operations, business intelligence must include the prevention of occurrences, especially avoiding bad decisions based on unintended yet catastrophic biases.\u00a0 Thus metrics must include statistical approaches to data for predicting actions, instead of reflecting only past activity. Data is no long just observational information, as it may have been in the context of website analytics.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThat changed perspective demands more rigor throughout a data-driven organization. \u00a0Statistics in models are far more exacting in their data requirements, creating a wider scope of decision making and actions people face.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA wider scope of data types also exists. Data can appear in different type structures, spurring a dizzying array of methodologies for specialists to analyze it.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA DataOps initiative establishes an optimization environment buoyed by continuous analytics and a multi-discipline specialist team. DataOps allows a smart scale for reducing error in data input, assessing business performance through data-based models, and managing version control for visualizing results.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSo what companies are crushing it in DataOps?\u00a0 The topic itself is still fresh-out-of-the-wrapper new \u2013 the phrase DataOps was first coined in 2015 according to Wikipedia<\/a><\/a>.\u00a0 But a good place to look is at companies that are leveraging predictive analytics in their operations \u2013 the Netflix of the world.\u00a0 The end result is the degree of disruption these companies bring to their industries.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBusiness growth is no longer just through physical scale. Optimizing business performance through models is now central in scaling a business.\u00a0\u00a0 Large acquisitions still abound in many industries, such as the recent CVS-Aetna announcement.\u00a0 But acquisitions have historically been hampered by misalign business culture and slow technology adoption. In contrast, DataOps manages cultural transformation into a data-driven organization. \u00a0Being data driven is a holy grail in business, especially as business leaders now view data and analytics changing the nature of industry competition. A McKinsey study<\/a> highlighted this view in detail.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tUltimately analytics has become collaborative and cross-functional, but with a specific eye for data lifecycles as much as that for product or process lifecycles.\u00a0 That eye will be highly scrutinized in 2018, as regulatory treatment of data, such as GDPR, takes the spotlight.\u00a0 Higher quality data will strengthen a business preparing its compliance, as well as its performance.\u00a0 That will put any firm embracing DataOps at the top of the class.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThis was originally posted on All\u00a0Analytics.com<\/a>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Ready to become DevOps Engineer? Browse courses\u00a0like AWS Certified DevOps Engineer\u2014Professional Exam Training developed by industry thought leaders and Experfy in Harvard Innovation Lab. Recalling my college student days, I always adored professors who thoroughly walked through what syllabus topics would appear on an upcoming test.\u00a0 That effort made it easier to prepare, and even<\/p>\n","protected":false},"author":249,"featured_media":3395,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[1767],"class_list":["post-620","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":1767,"user_id":249,"is_guest":0,"slug":"pierre-debois","display_name":"Pierre DeBois","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"DeBois","first_name":"Pierre","job_title":"","description":"Pierre DeBois is Founder and Chief Analytics Officer at Zimana, LLC<\/a> that provides Digital Marketing Analytics Services. He has been named among the top Facebook pages for Small Business by Small Business Trends and among the top 100 Twitter accounts for Big Data via Big Data Republic, and one of the top Big Data Executives and Experts to Follow on Twitter by CEOWorld. He is co-author of The Entrepreneur Within You Volume 3, an entrepreneurial motivation book."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/620","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/249"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=620"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/620\/revisions"}],"predecessor-version":[{"id":38097,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/620\/revisions\/38097"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3395"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=620"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=620"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=620"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=620"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}