tools offered for free to fight the virus<\/a> – the results are not noticeable. <\/p>\n\n\n\nThe smartest people on the planet with the sexiest job and cutting-edge tools did not achieve anything close to remarkable. <\/p>\n\n\n\n
Most achieved nothing at all.<\/p>\n\n\n\n
It’s not entirely unusual or surprising.<\/p>\n\n\n\n
It is unsurprising because this is how the typical Data Science delivery looks behind the corporate walls. <\/p>\n\n\n\n
Lots of buzzwords, big promises, PowerPoint decks touting huge potential but in reality… in reality business stakeholders increasingly started voicing their concerns about Data Scientists not delivering ROI.<\/strong><\/p>\n\n\n\nAfter many years of admiration for the data wizards congregating in their ivory towers, some senior execs finally gather the courage to admit: “We don’t know what they are doing over there”.<\/em><\/strong> <\/p>\n\n\n\nAnd while they might have cut a cheque in the good times – will they still do it in the era of massive layoffs and the global economy contracting?<\/p>\n\n\n\n
To understand what is going on, we need to look back at how it started in the first place.<\/p>\n\n\n\n
Creation of Data Scientist<\/strong>s<\/h2>\n\n\n\n\u201cWeeks of Data Engineering, 1-day training Machine Learning models. Welcome to Data Science.\u201d<\/em><\/p>\n\n\n\nThe key skill needed to create a Predictive Model is the ability to train Machine Learning (ML) models.<\/p>\n\n\n\n
ML learns based on a large pool of examples and without them it\u2019s useless.<\/p>\n\n\n\n
And here is the crux of the issue – the real-life data is very rarely laid out as examples. <\/p>\n\n\n\n
It’s more of long-winded stories<\/strong> recorded in the database introducing the characters, developing them through time, sometimes jump over plot-holes, just to end in the least expected moment.<\/p>\n\n\n\nML can’t take any of it.<\/p>\n\n\n\n
Hence our ML specialists spend their lives on of single-handedly transforming the databases into an ML-friendly format. <\/p>\n\n\n\n
They improvise, hacking their way through, desperately trying to reshape the data by any means available. <\/p>\n\n\n\n
People on the outside are not even trying to understand what is going on. <\/p>\n\n\n\n
Over time it may look like this person \u2013 called a Data Scientist now<\/strong>, is the only one able to start with the raw data and work out their way to a Model. <\/p>\n\n\n\nA unicorn.<\/em><\/strong><\/p>\n\n\n\nIt seems though that everybody lost the sight of the fact that there have been plenty of professionals around, working on reshaping the data. <\/p>\n\n\n\n
They are called Data Engineers.<\/strong><\/p>\n\n\n\nWeeks of Data Engineering plus 1 day of training Machine Learning models. <\/p>\n\n\n\n
Welcome to Data Science.<\/p>\n\n\n\nData Scientists are Machine Learning specialists forced to do ad-hoc Data Engineering. <\/p>\n\n\n\n
It\u2019s that simple.<\/p>\n\n\n\n
And it\u2019s bad for several reasons.<\/p>\n\n\n\n
Machine Learning specialists are not trained in best practice in Data Engineering. <\/p>\n\n\n\n
As such, typically they are not very good at it. <\/p>\n\n\n\n
They also don\u2019t like it.<\/p>\n\n\n\n
They resent that work and often feel it\u2019s beneath them.<\/p>\n\n\n\n
Reality Check<\/strong><\/h2>\n\n\n\n“The slowness of Data Scientists created the impression that the world needs a lot more Data Scientists. But it doesn\u2019t.<\/em>“<\/p>\n\n\n\nThis is a good time to mention that over the last few years almost anybody who was tasked with independently delivering something, anything<\/em><\/strong> of business value from a messy data started calling themselves a Data Scientist, making the popular understanding of the role extremely muddied.<\/p>\n\n\n\nHow does that link to the “fight Covid-19” fiasco?<\/p>\n\n\n\n
Firstly – most of the Data Scientists declaring the “fight with the virus” did not fully realize that there is no data to work on. <\/p>\n\n\n\n
It’s the same hubris that promised to change the business with AI and later delivers a logarithmic chart.<\/p>\n\n\n\n
Secondly – those who got access to the real data<\/strong>, meaning – patient-level data with a medical history, will spend weeks if not months<\/strong> on engineering this mess into a format ready for analysis.<\/p>\n\n\n\nBecause data engineering executed by Data Scientists is very, very slow.<\/p>\n\n\n\nThis slowness created the impression that the world needs a lot more Data Scientists to do the data science.<\/p>\n\n\n\n
But it doesn\u2019t.<\/p>\n\n\n\n
It\u2019s been three decades \u2013 how do we fix this?<\/strong><\/h2>\n\n\n\n\u201cExtracting insights from the data should be a part of the fabric in the organisation \u2013 a part of a process, not a project.\u201d<\/em><\/p>\n\n\n\nThis inefficient, expensive, over-promised, and misguided execution of analytics has been in place for over 3 decades. <\/p>\n\n\n\n
In 2018\/2019 though there has been a growing realisation that the ROI on slow analytics is not there.<\/strong><\/p>\n\n\n\nAdd the pandemic to the mix and the decision-makers wake up every day thinking – how yesterday’s news will impact us going forward? What changed in our business in the last few days? Are our months-old models any good right now?<\/p>\n\n\n\n
So how do we fix this?<\/p>\n\n\n\n
Our take via people-process-technology lenses:<\/strong><\/h2>\n\n\n\nPeople<\/strong><\/h3>\n\n\n\nBusiness Stakeholders<\/strong><\/h4>\n\n\n\nBusiness Stakeholders<\/strong><\/strong> need to pull their heads out of the sand and take a hard look at the data they want to use in the decision making. <\/p>\n\n\n\nIf their data infrastructure needs radical improvement (it does) then they need to put the money where their mouth is and sponsor this.<\/p>\n\n\n\n
Data Scientists<\/strong><\/h4>\n\n\n\nData Scientists<\/strong> need to accept the reality that in 99% of the cases right now the \u201cdata\u201d is not ready for \u201cscience\u201d. <\/p>\n\n\n\nIt actually is many miles away from it. <\/p>\n\n\n\n
Get down from your ivory towers and start delivering FAST using automated data engineering platforms instead of competing on Kaggle or your ground will be shaky soon. <\/p>\n\n\n\n
Some of you got hit already.<\/p>\n\n\n\n
Data Engineers<\/strong><\/h4>\n\n\n\nData Engineers<\/strong> can be the second to get some cheese by quickly upskilling themselves in Machine Learning and jumping on the already slowing Data Science bandwagon. <\/p>\n\n\n\nIt\u2019s not that hard and you will kick ass with your data engineering experience.<\/p>\n\n\n\n
Processes<\/strong><\/h3>\n\n\n\nDelivering Data Science has been traditionally project-based. <\/p>\n\n\n\n
In the current day and age this design is obsolete. <\/p>\n\n\n\n
The need for fresh insights and models just jumped up a notch – while the need for analysis older than 2 months evaporated.<\/p>\n\n\n\n
The rapidly changing economy monitoring changes and extracting insights from the data should be a part of the fabric in the organisation \u2013 a part of a process, not a project.<\/p>\n\n\n\n
Technology<\/strong><\/h3>\n\n\n\nIn the rampant progress of automation – talking about the world needing thousands of new Data Scientists is madness.<\/p>\n\n\n\n
If the data side of the house is fixed and the right technologies are put in place then an average organisation only needs one Data Scientist for their data science efforts. <\/p>\n\n\n\n
It\u2019s a C-level role when a senior exec is responsible for organising the infrastructure to allow Business Users to interact with the data without the layers of people in-between.<\/p>\n\n\n\n
And this is what the new world will expect – being connected directly to the data. <\/p>\n\n\n\n
In real-time. <\/p>\n\n\n\n
With ease.<\/p>\n","protected":false},"excerpt":{"rendered":"
We took a look at how reality may change for the cosy world of data science and the Data Scientists working in it. “Despite thousands of bodies throwing themselves at the problem – the results are not noticeable. “ The impact of Covid-19 will be massive across the globe, affecting every industry and every profession. …<\/p>\n","protected":false},"author":44,"featured_media":1606,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","footnotes":""},"categories":[39],"tags":[],"ppma_author":[523],"class_list":["post-1598","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data","author-maciekw"],"authors":[{"term_id":523,"user_id":44,"is_guest":0,"slug":"maciekw","display_name":"Maciek Wasiak","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/64e580e5f224b672974e44b4c119da58?s=96&d=mm&r=g","last_name":"Wasiak","first_name":"Maciek","job_title":"","user_url":"https:\/\/xpanse.ai\/","description":"Maciek Wasiak is the CEO of Xpanse AI, has a Ph.D. in AI, and 15 years of experience in leading Data Science delivery across Telecom, Banking, Insurance, Airline, and Healthcare sectors."}],"_links":{"self":[{"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/posts\/1598","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/comments?post=1598"}],"version-history":[{"count":4,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/posts\/1598\/revisions"}],"predecessor-version":[{"id":1628,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/posts\/1598\/revisions\/1628"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/media\/1606"}],"wp:attachment":[{"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/media?parent=1598"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/categories?post=1598"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/tags?post=1598"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/ppma_author?post=1598"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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