Predictive analytics

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 […]

1510/2015

How Predikto hired thousands of data scientists

By |October 15th, 2015|Categories: Predictive Analytics, Technology & Engineering|Tags: , , , |

GE’s Jeff Immelt was recently interviewed on the Predictive Analytics investments and overall initiatives that have been ongoing over the last 5 years within his walls. The transcript, available here, is an excellent read on how a legacy company is attempting to transform itself for the digital future, leveraging vast amounts of sensor data to predict failure in large machinery. This marks a pivotal moment in GE’s history, where turning around a Titanic-size ship won’t […]

1310/2015

ARC Guest Blog: Counting toilet flushes help improve bullet train reliability

By |October 13th, 2015|Categories: Internet Of Things (IoT), Railroad|Tags: , , |

Greg Adams was a recent guest blogger on the ARC Advisory Group’s IIoT newsletter. We see a lot of data and it is interesting how mundane and often overlooked data can contain meaning. Read how counting toilet flushes is helping to increase the uptime and reliability of bullet trains.  http://industrial-iot.com/2015/10/how-wc-flushes-relate-to-locomotive-reliability/

 

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1310/2015

Metro Atlanta CEO writes a piece about Predictive Analytics

By |October 13th, 2015|Categories: Industries, Predikto News / Events, Railroad|Tags: , |

Metro Atlanta CEO has an article in their October newsletter covering Predictive Analytics and some of the interesting use cases Predikto has in Transportation.

Predikto, a leader in Predictive Analytics solutions Transportation, has begun to deploy their machine learning / artificial intelligence software to help improve equipment reliability at global companies.

Click on the article to read about actual use cases and gain an understanding of this disruptive technology.

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209/2015

5 Reasons Why You Suck at Predictive Analytics

By |September 2nd, 2015|Categories: Predictive Analytics, Predictive Maintenance|Tags: , , |

We see it every day… Customer X embarked on a grand in-house initiative to implement Big Data analytics. $X million and Y years later, they can predict nothing, aside from the fact that their consultants seem to get fatter with every passing month, and the engineering guys keep asking for more time and bigger budgets. It’s understandable… These days, cutting through all the noise related to big data analytics can be difficult. The bloggers, analysts, and consultants certainly make it […]

1507/2015

Predikto: Making Waves in IoT!

By |July 15th, 2015|Categories: Internet Of Things (IoT), Predictive Analytics, Predikto News / Events, Railroad|Tags: , , |

The Internet of Things is generally defined as “Smart” + “Connected” + “Edge Devices” (Planes, Trains, Automobiles, Industrial & Farming Equipment, Medical Equipment, and Consumer Electronics)

Predikto focuses on putting the “smart” into managing smart connected devices, equipment and complex capital assets in order to forecast asset behavior/performance.

Industrial asset OEMs, operators and maintenance organizations are challenged by equipment performance degradation and failure as they impact uptime and efficiency. While reliability and condition-based solutions have been around […]

807/2015

A Software Industry Veteran’s Take on Predictive Analytics

By |July 8th, 2015|Categories: Industries, Internet Of Things (IoT), Predictive Analytics, Railroad|Tags: , , , , , |

I’m about 4 months into the job here at Predikto as VP, Sales.  The predictive analytics market is an exciting new market with predictably (pun intended) its share of hype.  Nevertheless, this is key niche of the Industrial Internet of Things sector. I’d like to share some observations on what I’ve learned thus far.

We focus on asset-intensive industries, helping organizations leverage the terabytes of data they have accumulated to anticipate the likelihood of an adverse […]

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 […]