Predictive Analytics


You’re going to need a bigger boat!

By |July 21st, 2017|Categories: Internet Of Things (IoT), Predictive Analytics|Tags: , , |

With the recent 42nd anniversary of ‘Jaws’ the film, everyone always comes up with the tagline above ‘you’re going to need a bigger boat’ and that led me recently to consider where we have come from regarding engineering, calculations (to solve problems), data, data science and now to Big Data, the Internet of Things (IoT) and its Industrial counterpart and the role of the engineer and the data scientist.

Back in my day, not quite 42 […]


Digital Transformations : From Analysis Paralysis to Execution Mode

By |July 13th, 2017|Categories: Industries, Internet Of Things (IoT), Predictive Analytics, Predictive Maintenance|Tags: , , , |

I have never been more excited about the future of Predikto. We started 4 yrs ago with the Vision of “Moving Unplanned to Planned”. We wanted to help large industrials “To harness the power of predictive analytics to optimize operational performance”. We are enabling this with:

Our software platform including Predikto MAX which automates machine learning algorithm generation at a massive scale
Our unique approach to data preparation optimized for Machine Learning

So why am I so excited? […]


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 […]


What about unplanned?

By |April 20th, 2016|Categories: Industries, Internet Of Things (IoT), Predictive Analytics, Predictive Maintenance, Uncategorized|

Everybody’s looking at process inefficiencies to improve maintenance but there’s lower hanging – and bigger – fruit to focus on first: unplanned events!
Maintenance has pretty simple goals; guarantee and increase equipment uptime and do so at the lowest possible cost. Let’s take a quick look at how unplanned events influence these three conditions.
Guarantee uptime
When production went through the evolutions of JIT (Just In Time), Lean,… and other optimisation schemes, schedules got ever tighter and deviations […]


Context is King to Operationalize Predictive Analytics

By |March 21st, 2016|Categories: Predictive Analytics|Tags: , |

Companies have invested significantly in Big Data solutions or capabilities. They usually start with adding more sensors on their equipment or perhaps bringing all of their historical data into a Big Data repository like Hadoop.  They have taken the first step towards a “Big Data” driven solution. The challenge is that “tackling” the data does not bring any tangible value.  This is why Predikto focuses so much of our R&D and technology in the “Action” […]


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 […]


Life Expectancy of an Algorithm

By |February 8th, 2016|Categories: Predictive Analytics|

In our field of predictive analytics (asset health and failures), and I presume the same goes for other fields, there is a major misconception about what an algorithm represents. Speaking with business leaders and, more worryingly, practitioners too, about PdA and in most cases the conversation will turn to “and then we build the algorithm that describes the behaviour of…” Here’s some bad news for these people; many, if not most, assets can’t be described […]


Why do we need so much data?

By |February 2nd, 2016|Categories: Predictive Analytics|

Most of us remember the double slit experiment from our physics classes. For those who don’t, here’s a (very) short reminder: a light is shone through two fine slits and the resulting image is proof of the dual nature of light.
If that experiment is done with an emitter that can send single particles of light and the receptor is a photo-sensitive surface, an interesting phenomenon occurs; ‘looking at’ the receptor too soon (if it’s a photo […]


What do you mean, no spare?

By |January 22nd, 2016|Categories: Predictive Analytics|

Forecasting is only useful if the result is applicable… and to the point! I may have perfectly forecasted the weather but if I’m going to spend the whole day inside, this has no impact on me. This is the same with industrial applications. Current interest in predictive analytics results in plenty of pilot projects of which most focus on how well the forecast performs. To the non-initiated this means; out of every 100 failures, did […]


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 […]