Robert

About Robert Morris, Ph.D.

Robert Morris is Co-Founder, Chief Science Officer, and Chief Technology Officer of Predikto, Inc., an Atlanta based software company that deploys automated predictive analytics solutions for the rail and aviation industries. Robert has over a decade of research, academic, and practical experience involving advanced data science. His unique patent-pending approach toward automating the process of accounting for context in data from repeated-observations has set the Predikto solution, “Max”, above and beyond the competition. Robert has published over 60 peer-reviewed scholarly manuscripts on a range of topics, many of which have received national and local media attention. He’s the recipient of major awards for both research and teaching and has delivered over 100 conference presentations. Robert, has a Ph.D. in criminology from Sam Houston State University.
2812/2016

The Missing Link in Why You’re Not Getting Value From Your Data Science

By |December 28th, 2016|Categories: Internet Of Things (IoT), Predictive Analytics, Predictive Maintenance, Technology & Engineering|Tags: , , , , , , |

The Missing Link in Why You’re Not Getting Value From Your Data Science

by Robert Morris, Ph.D.

DECEMBER 28, 2016

Recently, Kalyan Veeramachaneni of MIT published an insightful monologue in the Harvard Business Review entitled “Why You’re Not Getting Value from Your Data Science.” The author argued that businesses struggle to see value from machine learning/data science solutions because most machine learning experts tend not to build and design models around business value. Rather, machine learning models are built […]

2302/2016

Data. The “other” four-letter word.

By |February 23rd, 2016|Categories: Predictive Analytics|

At Predikto, we work with customers who are OEMs, large-scale equipment operators, as well as some smaller operations. The volume of data they push to us ranges from a few megabytes per week to dozens of terabytes per month. Regardless of their transmission volume, every customer is tantalized by the prospect of what deploying predictive maintenance and predictive analytics solutions can do for their bottom line.

In many cases, there’s a hesitancy by corporations to trust […]

2406/2015

Deploying Predictive Analytics (PdA) as an Operational Improvement Solution: A few things to consider

By |June 24th, 2015|Categories: Industries, Internet Of Things (IoT), Predictive Analytics, Predictive Maintenance, Uncategorized|Tags: , , , , |

“…in data science…many decisions must be made and there’s a lot of room to be wrong…”

There are a good number of software companies out there who claim to have developed tools that can potentially deploy a PdA solution to enhance operational performance. Some of these packages appear to be okay, some claim that they are really good, and others seem really ambiguous other than being a tool that a data scientist might use to slice and dice data. What’s missing from […]

2304/2015

The Scalability of Data Science: Part 3 – Reality Check  

By |April 23rd, 2015|Categories: Predictive Analytics, Predictive Maintenance, Uncategorized|Tags: , |

You’re an operations manager, or even a CEO. Let’s say you have a big predictive analytics initiative and need to deploy several instances of your solution. What are your options? Ok, go ahead and hire a team of 5 data scientists, each with a relatively modest salary of $100k (US) per year (this is very conservative estimate). Now step back… You’ve just spent half a million dollars on staffing (plus the time for hiring, software procurement, etc.) […]

1404/2015

The Scalability of Data Science: Part 2 – The Reality of Deployment

By |April 14th, 2015|Categories: Predictive Analytics, Predictive Maintenance, Uncategorized|Tags: , |

To put my previous post into perspective, let me give you a for instance… An organization wants to develop a deployed predictive analytics solution for an entire class of commuter trains. Let’s be modest and go with 10 different instances from within the data (e.g.,  1) predicting engine failure, 2) turbo charger pressure loss, 3) door malfunction, … and so on…). We’ll focus on just one…

Data from dozens of assets (i.e., trains) are streaming in by the […]

804/2015

Deploying Predictive Analytics and Scalability: Part 1 – Patience is required, but can you afford it?

By |April 8th, 2015|Categories: Predictive Analytics|

Having been involved in applied data science for over a decade, one of the most substantive shortcomings of analytics that I’ve seen, in terms of a viable business product, is scalability. Data management, feature engineering, and multivariate statistics/machine learning are conceptually challenging topics and take time to master. Even for a seasoned data scientist (or team, or whatever you call it…) that can tackle the full-stack solution from data collection to predictive output/validation (i.e., end-to-end), […]