Become a Data Science Superhero

Read how Nathan of Symphony Post Acute Network achieved it

Google is Rethinking its Business – What About You?

May 26, 2017 by

Google recently started promoting automated machine learning (what they call AutoML) – something that DataRobot customers have been doing for years. So what is driving Google to now embrace this transformative approach? Guest blogger Damian Mingle explains. 

You might be asking yourself why Google, a company with over $600 billion in market cap, is rethinking its business, and if it has any relevance to your own business. In truth, Google is a business like any other, but beyond their P&L they represent an attitude towards disruption with technology. While there is considerable evidence of this, here are few recent examples:

  • Google search now ranks differently using machine learning
  • Google Maps Street View automatically recognizes signs
  • Video calling uses machine learning for low-bandwidth situations

As a business person it may be difficult to connect with a few esoteric statements about Google, so let me create a scenario for you to consider. Which organization do you think is more likely to succeed in today’s business environment – Business A or Business B?

  • Business A: an organization that uses a 30-day lag report on the number of items sold to indicate both how they are doing and what they should do next
  • Business B: an organization that uses  an automated machine learning technology, like DataRobot, in a way that they can predict the likelihood of what they will sell next month, predict price fluctuations, use their website data to identify client questions which have the same intent to provide the optimal response, predict which products a customer will purchase again, predict which recommended content each user will click, pair products with people every time they visit their site, maximize sales and minimize returns based on transaction data, predict the likelihood of which clients may leave them and what month that will happen over the next year, and combine various data sources with their own organic data to allow for the business to have increased context for decision making

My hope is that you see the value in Business B.

Like business, technology has entered a new era. This new era allows you to reframe old problems in new ways at a rapid pace and without requiring the pedigree of a Ph.D. from an Ivy League school.

Let me explain:

Most companies understand and make use of programming, which often exists to relieve individuals of performing rote tasks. Business has benefited greatly from this and will continue to into the future. However, nowadays we are hearing more and more about machine learning as a complement to programming. The difference between programming and machine learning is that machine learning allows computers to learn how to best perform these rote tasks, and that optimization is significant. Additionally, because of the early success of machine learning, companies like Google have made machine learning/artificial intelligence front and center of their respective organizations and have created momentum for technology they call AutoML. Automated machine learning allows computers to go even one level further by automating the optimization of how to best perform these rote actions.

Remember our vignette above about which organization will likely succeed in today’s business – Business A or Business B? Let me share with you what so many organizations (both big and small) tell themselves that keep them stuck in the position of Business A, and my responses to those excuses:

Excuse: “Our data is a mess. It’s not in the right format and is not well documented.

Response: Maybe true — figure it out or chances are you will be out of business in 3 years.

Excuse: “We don’t have the right data.” 

Response: I have worked with domain experts for years who often are trying to run their business straight from the gut. Don’t get caught in the trap of thinking you know what the right data is. IF you do this, you effectively constrain all the technology we are talking about so that it fits in how you see and reason about the world.

Excuse: “Our culture is not right for machine learning or automated machine learning. 

Response: Change the culture with machine learning. Start with the things you measure in a business intelligence report. For whatever reason, these reports have proven to be important to the business and you most likely have data that can be used to fuel the growth of your business.

Excuse: The timing is not right; I have other initiatives.

Response: Stop and think for a moment. Do you think your competitor is worried about initiatives like paving the parking lot or figuring out a new coffee vendor? Be open to the idea that your current initiative is the last thing that will actually help your business survive the current business environment.

Excuse: We are in the wrong industry

Response: It’s clear some industries are slower to adopt change than others. If you are in a fast-adopting industry, you have no time to lose – get busy using this sort of technology. If you are in an industry that’s slow to adopt change, disrupt away!

Excuse: We don’t have access to that sort of talent. 

Response: What used to come out of the ivory towers after years of research from world experts is now more commonplace than you think. In fact, technology like automated machine learning makes it possible for you to invert your current business hierarchy and allows you to tap into more of the organization’s human capital. With automated machine learning technology, you can move many of your individuals from “doer” roles to more “thinker” roles. Your business is most likely rife with these thinker individuals already; you just aren’t making use of machine learning.

The Question You Have To Answer

The question that your organization is answering each day is, “How will I compete in the new world?” Just so we are clear, the answer is binary: “I will have a future” or “I will not have a future.” The takeaway is that your competitor is most likely unknown to you, is 17 years old, working with messy data, not dealing with culture in the least, and who instead of having a Ph.D. is using automated machine learning to disrupt an industry, out of her parent’s basement. What you decide to do is up to you, but the question cannot go unanswered.

 

Damian Mingle is Chief Data Scientist for Intermedix, a global leader pioneering innovations in data analytics and cloud-based technology to deliver best-in-class revenue cycle management, practice management and emergency management solutions. As a leading authority on healthcare data science, Mr. Mingle speaks nationally and internationally on patient safety, global health, and applied data science. Mr. Mingle’s work has been published in “The International Clinical Pathology Journal,” “Current Trends in Biomedical Engineering & Biosciences,” and “The International Journal of Biomedical Data Mining”. He is ranked in the top 1 percent globally as a data scientist through regular competitions. Mr. Mingle is married, has three daughters and lives in Franklin, TN.