{"id":13023,"date":"2016-10-07T17:59:12","date_gmt":"2016-10-07T15:59:12","guid":{"rendered":"http:\/\/timoelliott.com\/blog\/?p=13023"},"modified":"2016-10-07T22:09:08","modified_gmt":"2016-10-07T20:09:08","slug":"a-shortcut-guide-to-machine-learning-and-ai-in-the-enterprise","status":"publish","type":"post","link":"https:\/\/timoelliott.com\/blog\/2016\/10\/a-shortcut-guide-to-machine-learning-and-ai-in-the-enterprise.html","title":{"rendered":"A Shortcut Guide to Machine Learning and AI in The Enterprise"},"content":{"rendered":"

\"advanced-predictive-proactive-etc-two-men-fighting\"<\/a>
\nPredictive analytics \/ machine learning \/ artificial intelligence is a hot topic \u2013 what’s it about?<\/b><\/p>\n

Using algorithms to help make better decisions\u00a0has been the “next big thing in analytics” for over 25 years. It has been\u00a0used in key areas such as fraud\u00a0the entire\u00a0time. But it’s now\u00a0become\u00a0a full-throated mainstream business meme that features in every enterprise software keynote — although\u00a0the industry is battling\u00a0with what to\u00a0call it.<\/p>\n

It appears\u00a0that\u00a0terms like\u00a0Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers. The term Cognitive Computing<\/a> seemed to be poised to win, but IBM’s strong association with the term may\u00a0have backfired — journalists and analysts want to use language\u00a0that is\u00a0independent of any particular company. Currently, the growing\u00a0consensus seems to be\u00a0to use\u00a0Machine Learning when talking about the technology and Artificial Intelligence when talking about the business uses.<\/p>\n

Whatever we call it, it’s generally\u00a0proposed in two different forms: either\u00a0as an extension to existing platforms<\/a> for data analysts; or as new embedded functionality in diverse\u00a0business applications such as sales lead scoring<\/a>, marketing optimization, sorting HR resumes, or financial invoice matching<\/a>.<\/p>\n

Why is it taking off now, and what\u2019s changing?<\/b><\/p>\n

Artificial intelligence is now taking off because there’s a lot more data available and\u00a0affordable,\u00a0powerful systems to crunch through it all. It’s also much easier to get access to powerful algorithm-based software in the form of open-source products or embedded as a service in enterprise platforms.<\/p>\n

Organizations today have\u00a0also more comfortable with manipulating business data, with a new generation of business analysts aspiring to become “citizen data scientists.” Enterprises can take their traditional analytics to the next level<\/a> using these new tools.<\/p>\n

However, we’re now at the “Peak of Inflated Expectations” for these technologies according to Gartner’s Hype Cycle<\/a> — we will soon see articles pushing back on the more exaggerated claims. Over the next few years, we will find out the limitations of these technologies even as they start bringing real-world benefits.<\/p>\n

What are the longer-term implications?<\/b><\/p>\n

First, easier-to-use predictive analytics engines are blurring the gap between “everyday analytics” and the data science team. A “factory<\/a>” approach to creating, deploying, and maintaining predictive models means data scientists can have greater impact. And sophisticated business users can now access some the power of these algorithms without having to become data scientists themselves<\/a>.<\/p>\n

Second, every business application will include some predictive functionality, automating any areas where there are “repeatable decisions.”\u00a0It is hard to think of a business process that could not be improved in this way, with big\u00a0implications in terms of both efficiency and white-collar employment<\/a>.<\/p>\n

Third, applications will\u00a0use these algorithms on themselves to create “self-improving” platforms\u00a0that get easier to use and more powerful over time (akin to how\u00a0each new semi-autonomous-driving Tesla car can learn something new and pass it onto the rest of the fleet<\/a>).<\/p>\n

Fourth, over time,\u00a0business processes, applications, and workflows\u00a0may have to\u00a0be rethought. If algorithms are available as a core part of business platforms, we can\u00a0provide\u00a0people with new paths through typical business\u00a0questions<\/a> such as “What\u2019s happening now? What do I need to know?\u00a0What do you recommend? What should I always do? What can I expect to happen? What can I avoid? What do I need to do right now?”<\/p>\n

Fifth, implementing all the above will involve deep and worrying moral questions<\/a>\u00a0in terms of data privacy and allowing algorithms to make decisions that affect people and society. There will undoubtedly be many\u00a0scandals and missteps before the right rules and practices are in place.<\/p>\n

What first steps should companies be taking in this area?<\/b>
\nAs usual, the barriers to business benefit are more likely to be cultural than technical.<\/p>\n

Above all, organizations need to make sure they have the right technical expertise to be able to navigate the confusion of new vendors offers, the right business knowledge to know where best to apply them, and the awareness\u00a0that their technology choices may\u00a0have unforeseen moral implications.<\/p>\n","protected":false},"excerpt":{"rendered":"

Data mining \/ predictive analytics \/ advanced analytics \/ cognitive \/ machine learning \/ data science \/ artificial intelligence… What do you need to know?<\/p>\n","protected":false},"author":2,"featured_media":13024,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[14],"tags":[79,1176,1174,339,344,1175,836],"class_list":["post-13023","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thoughts","tag-advanced-analytics","tag-artificial-intelligence","tag-cognitive","tag-data-mining","tag-data-science","tag-machine-learning","tag-predictive-analytics"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2016\/10\/advanced-predictive-proactive-etc-Two-men-fighting.jpg?fit=1000%2C578&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3X9RF-3o3","_links":{"self":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/13023","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/comments?post=13023"}],"version-history":[{"count":0,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/13023\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media\/13024"}],"wp:attachment":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media?parent=13023"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/categories?post=13023"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/tags?post=13023"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}