{"id":15509,"date":"2018-07-23T19:42:26","date_gmt":"2018-07-23T17:42:26","guid":{"rendered":"https:\/\/timoelliott.com\/blog\/?p=15509"},"modified":"2018-07-23T19:43:29","modified_gmt":"2018-07-23T17:43:29","slug":"predictive-machine-learning-roi-beyond-cool","status":"publish","type":"post","link":"https:\/\/timoelliott.com\/blog\/2018\/07\/predictive-machine-learning-roi-beyond-cool.html","title":{"rendered":"Predictive Machine Learning: ROI Beyond Cool"},"content":{"rendered":"

\"\"<\/a><\/p>\n

I was a guest last week on the Game-Changing Predictive Machine Learning<\/a> radio show last week hosted by Bonnie Graham<\/a>. The other guests were\u00a0Gil Gomez of Deloitte<\/a> and\u00a0Hudson Harris of HarrisLogic<\/a>.<\/p>\n

Here are some of my remarks from the show:<\/p>\n

The impact of machine learning<\/h4>\n

\u201cMachine learning is really about complex-but-repetitive decisions, and being able to automate those in new ways. And when you think about it that way, it\u2019s hard to think of an area where it won\u2019t have an impact.<\/p>\n

Almost every job, every role, has some aspect of that job that involves complex repetitive decisions and you can now automate that, and elevate the person doing the role to a higher level, allowing them to do more with less.\u201d<\/p>\n

The importance of data governance and privacy<\/h4>\n

\u201cGovernance is an area that is not sufficiently discussed when it comes to machine learning. You\u2019re essentially delegating your machine learning to machines and that means you have to be incredibly careful.<\/p>\n

Algorithms are basically sociopaths, and that\u2019s how they should be treated. They\u2019re very intelligent, but they have no empathy, no emotion, no appreciation of the human condition. So they can be very useful, but you have to be very mistrustful about their use, and you have to put in place strong safeguards \u2014 transparency, and monitoring to make sure they don\u2019t result in abusive situations.<\/p>\n

Interestingly enough, as I talk to audiences about machine learning, there is always part of the audience that is always concerned about how the algorithm is doing what it does \u2014 what are the mathematical factors behind the algorithm. And I understand that concern, but it\u2019s actually much more important to have a robust governance about the use of the algorithm. No matter how good the math is behind your predictive analytics, it could still be very badly used. So being very human about the use of artificial intelligence is absolutely essential.\u201d<\/p>\n

What’s the easiest way to get ROI with machine learning?<\/h4>\n

\u201cThe background is that when people hear terms like machine learning and artificial intelligence, they inevitably start thinking about robots or the kinds of human assistants that we see in movies, like Jarvis in Iron Man. But the reality, today at least, is that it\u2019s a lot less about that kind of sentience and a lot more about automated pattern matching. Really, we\u2019re just talking about implementing automated pattern matching inside your business processes.<\/p>\n

And there are lots of areas of business where that kind of complex automated pattern matching makes up a big chunk of the daily work. McKinsey believes that up to 70% of financial processes could be automated using these latest techniques, for example. And we\u2019ve been using computers to automate and optimize business processes now for forty or fifty years. This is \u201cjust\u201d, at one level, the next big opportunity to do so.<\/p>\n

We can certainly do other things with this technology \u2014 Harris Logic is a great example of how we can use it to improve the human condition \u2014 but for right now, the easiest way to get ROI is through automating repetitive parts of business processes.\u201d<\/p>\n

\u201cThe other big opportunity is to help optimize the customer experience. It used to be that your experience as a customer was fairly linear \u2014 you\u2019d see an ad, you\u2019d go into a store, you\u2019d buy something. Everybody followed pretty much the same process. But now we\u2019re in a much more complex, omni-channel world, where you can buy online, or through a store \u2014 there are lots of different customer touchpoints.<\/p>\n

Leading organizations are now using information in real time, at each customer touchpoint to optimize and guide that customer journey \u2014 \u201cwhat are the products the customer would be most interested in?\u201d \u201cwould a discount right now overcome their hesitation to buy?\u201d \u201cwhat level of service do they require, through what channel.\u201d<\/p>\n

So just constantly updating and optimizing that experience is a fantastic opportunity \u2014 using machine learning to optimize that end-to-end customer journey.\u201d<\/p>\n

Machine learning predictions<\/h4>\n

\u201cNone of us really knows what\u2019s going to happen. But I think one of the big changes will be one that companies are going to go from being primarily reactive to becoming far more proactive.<\/p>\n

For example, Harris Logic helps patients by identifying when there\u2019s a likelihood of a person getting in trouble, so they\u2019re able to intervene before there\u2019s a crisis, before somebody gets arrested. And that\u2019s better for everybody involved: it\u2019s better for the patient, it saves a lot of money for the jail system and the health system.<\/p>\n

I\u2019ve worked in analytics for around thirty years now, and most of the analytics we do are fundamentally about \u201clook, here\u2019s what went wrong in the past.\u201d<\/p>\n

These new technologies hold out the possibility of being able to intervene before<\/em> things go wrong \u2014 spotting the pattern \u201cthese kinds of situations led up to something bad.\u201d<\/p>\n

Now I can step in and stop that bad thing from happening. It could be something very human, such as a mental health patient, or it could be something much more trivial in business, like \u201cthis delivery is going to be late\u201d \u2014 therefore I can at least phone the customer to warn them that it\u2019s going to be late, or I could chose an alternative way of transporting it so it does get there on time.<\/p>\n

We\u2019re going to see these types of technologies applied to every business process to help them become more proactive, to help them learn and improve automatically over time.\u201d<\/p>\n

 <\/p>\n

Adapted ice image<\/a> creative commons by\u00a0Andreas Tille<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Opportunities, predictions and concerns about Predictive Machine Learning from a recent “ROI Beyond Cool” radio show. <\/p>\n","protected":false},"author":2,"featured_media":15513,"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":[7],"tags":[518,537,1175,1238,1377,835,891,894],"class_list":["post-15509","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-interviews","tag-game-changers","tag-governance","tag-machine-learning","tag-ml","tag-pattern-matching","tag-predictive","tag-risk","tag-roi"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/07\/roi-beyond-cool.jpg?fit=1000%2C588&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3X9RF-429","_links":{"self":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/15509","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=15509"}],"version-history":[{"count":0,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/15509\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media\/15513"}],"wp:attachment":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media?parent=15509"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/categories?post=15509"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/tags?post=15509"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}