Is Your Organization Ready for Machine Learning?

As data science has become increasingly popular, many organizations rush to hire Machine Learning experts without laying the proper foundation to ensure their success, including creating proper database architecture, building out essential data science technology, establishing data governance, and instilling data-driven decision-making throughout the organization. Absent these elements, many ML experts join companies excited to deploy their data science expertise only to end up marred in data cleaning or lobbying for tech resources.

At the Machine Intelligence Summit on 23-24 March in San Francisco, Amy Gershkoff, Chief Data Officer at Ancestry, will be discussing how organizations can prepare their organization for success, as well as how candidates can diagnose whether the organization is truly ready for ML.

- Tell us about your work at Ancestry.

As Chief Data Officer at Ancestry, I am responsible for our data assets "end to end," from ensuring that we have scalable, industry-leading data infrastructure, to overseeing data governance, to extracting insights from our data using data science and machine learning, to creating an optimized, personalized, data-driven experience for each customer throughout their journey. 

- What do you feel are the leading factors enabling recent advancements in Machine Learning?

As computing power has increased exponentially, algorithms can now be run faster than ever before. Algorithms that were previously too computationally-intensive to be practical for most organizations can now be run quickly in distributed computing environments. Code that used to take hours or even days to finish running now finishes in a matter of minutes or even seconds. These advancements in computing infrastructure have fundamentally changed the art of the possible when it comes to machine learning.

Interest in machine learning has also exploded. As an increasing number of employers are now seeking data science talent, many more universities are offering academic degree programs in data science at both the undergraduate and graduate level.

- Which industries will be most disrupted by Machine Intelligence?

The industries that will be most disrupted by Machine Intelligence in the next five years will be healthcare and energy.

In healthcare, machine learning can enhance every facet of a patient’s experience, from selecting providers to diagnosis to custom treatments and to specialized pharmacology. We are already seeing the beginning of the revolution of Big Data in health care: President Obama’s Precision Medicine Initiative, IBM Watson’s healthcare AI, and the growth in consumer genomics are only the tip of the iceberg. 

In the energy sector, growth in alternative energy sources such as wind and solar can be further fuelled in part by machine learning to optimize the placement of these installations and govern the way each installation interacts with the energy grid leveraging a myriad of variables include real-time weather data. Even the financing itself of solar and wind investment projects is ripe for disruption.

- What developments can we expect to see in Machine Intelligence in the next 5 years?

In the last five years, many organizations have rushed to hire machine learning experts without properly preparing the organization: data governance may not have been established; data infrastructure may be woefully lacking or not suitable for machine learning; organizationally, business units may not be ready to implement machine-learned insights. In this scenario, the data scientists are under-utilized, spending their energies lobbying for resources or attempting to lay the necessary foundation, rather than leveraging machine learning to help the organization.

In the next five years, organizations will catch up. More C-suite executives will understand the foundation necessary in order to gain the most from their data science teams: 

• Chief Technology Officers will become more knowledgeable about setting up optimal machine learning data architecture.
• Chief Financial Officers will become more comfortable with making data science infrastructure-related investments.
• Chief Marketing Officers will become ready to let Machine Intelligence govern decisions about where, when, and to whom to advertise, with personalized messages.
• Chief Product Officers will more commonly integrate AI into product design. 

This surge in understanding of Machine Learning by other members of the C-suite will, in turn, unlock a great deal of innovation in ML, as ML experts are freed from laying the organizational groundwork and can focus on pushing the envelope on Machine Intelligence.

On 23-24 March we will be holding the inaugural Machine Intelligence Summit in San Francisco. We will be hosting the European edition in Amsterdam on 28-29 June. Each Summit will feature an additional track on Autonomous Vehicles.

The summits are a unique opportunity to meet leading academics, industry pioneers, influential technologists and cutting edge startups. Interact with and learn from experts in machine learning, computer vision, IoT, data mining, and predictive intelligence. Plus, share best practices to advance the impact and opportunities of AI.

Early bird tickets for the Machine Intelligence Summit in San Francisco end on 3 February. Don't miss out on this huge saving, register here.

This interview was first published on the RE?WORK blog.