You’re going to need a bigger boat!

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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 years ago (but close), we did calculations by hand, on paper, with a calculator, often requiring engineering ‘iterations’ to get the right design flow (or whatever). Yes, we made mistakes (probably too many to worry about!) – then we had spreadsheets (to speed up iterations with less mistakes), then static process simulations to speed up the design and test alternatives, then dynamic process simulations to allow us to ‘predict’ the performance of the plant so as to help optimize the design – from there integrating with controls systems to develop control room operator training and from there to 3D Virtual Reality headsets that allow the User to ‘walk’ around the plant integrating control room and field operations etc….

And so into predictive analytics, where engineering meets data science…Predictive analytics has been around for quite some time and the ‘engineering’ approach has been to seek the answer to the question that everyone asks after there has been some kind of process or equipment failure, outage or worse still some kind of accident ‘Why couldn’t we see this problem coming?’ and usually, you can, but you have to be looking at all the variables, all the time and apply your engineering knowledge and skills to ‘spot the problem’ way in advance.

Clearly, you can apply lots of engineers and operators to look at all the data all of the time or you can use computer software to help. Early solutions involved looking at the ‘time series’ data only, as deviations from the ‘normal’ are relatively easy to spot and ‘trending’ solutions can alert and notify the host. But, as referring to my earlier engineering statements, users then want more – from spreadsheets to 3D VR – once you have your predictions based on time series analytics – then you want to add context, add CMMS and Maintenance data, operation logs, does the weather play a part, etc… and by adding each level of complexity, then the ‘problem’ to be solved gets much more complex and your ‘time series’ algorithm, even if it’s a really good one, just doesn’t stack up.

So you need to look at the problem from different aspects and try to find the best way to solve the ‘really big’ problem – which is – ‘How do I take all my relevant data – format it in a way that it can make sense and then contextualize it in a way that I can begin to make sense of it and then use all that data to ‘predict’ whats going to happen’. That’s a big problem that will need a big computer (‘gonna need a bigger computer’) and complex algorithm(s) to solve it.

Cloud computing has been around awhile, as has ETL (Extract Transform Load), and that is what impressed me so much to join Predikto, their ability to take all the data (as much as anyone would need or want), ETL it, put it in the Cloud, allow our ‘MAX’ Predictive engine to chew through it (just once, for 3-4 weeks), and then begin to apply every known algorithm that every Data Scientist might know (and a few of our own) AND, daily, optimize those algorithms for accuracy, applicability, and optimize for the types of features that help the predictions depending on the varying conditions of the process or asset (or weather etc) that are affecting it daily, hourly, etc….

Now that is really, REALLY clever stuff….and things that our customers have been asking for, for over 25 years in the business….

Big Data, IIoT, Industrie 4.0 and all the things that bring these together combined with what we are doing with Predikto – now that’s the future…..and I am honored to be part of Mario and Robert’s team – watch this space – stay on this track (sic), the future is in Predikto and is HERE!

By Paul Seccombe.

Paul joined Predikto in 2017 after his role as Solutions Leader at the GE Predix Oil & Gas for Europe and the Middle East. He was also at Smart Signal for many years. Paul holds a PhD in BioChemical Engineering from the University of Wales. He is based out of London.

Digital Transformations : From Analysis Paralysis to Execution Mode

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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:

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

So why am I so excited? We have seen a big shift in the past 12 months of organizations going from “analysis paralysis” to “let’s start to execute”. We love it when prospects get what we have built. This was not the case 3 and 4 yrs ago. It takes a sophisticated organization to be ready to capitalize on our technology. We look for:

Clear Strategy

A clear strategy with the Executive support that incorporates AI, Predictive Analytics, Analytics/Digital Transformations, and investments that will enable them to increase revenues or cut costs by leveraging their own data. A recent report by IDC found that 80% of senior executives said investing in digital transformation is critical to future success. Investments in digital transformation initiatives will reach USD2.2 trillion by 2019 which is 60% more than this year.

Organizational Readiness

Organizational readiness is another key aspect of prospects and customers who are ready for our technology. Most customers have hired a Chief Digital Officer who came from the outside to change the way they have tackled innovation and digital transformations. They have dedicated teams with the power and budgets to run multiple pilots with companies big and small to learn how new technology can bring tangible value to their organization. They are learning to move fast and fail fast. The best ones are learning from startups and aligning their key initiatives with true disruptors. If you are looking for Ginni to sell you IBM Watson to solve all your problems, you are going to have a rude awakening in 18 months. We actually look for prospects who have already hired IBM and failed. IBM, please send me your list of Pilot customers from the past 3 years for Predictive Maintenance projects.

Technical Transformation

Technical transformation means a lot of different things depending on the industry vertical. Some customers did not have access to their own equipment sensor data since the OEM would keep it. They had to invest in new hardware to tap into the sensor data inside trains. Others had data stored in siloed on-prem historians and it was a challenge to get their IT Security organization to push that data to the Cloud. Others are trying to figure out which cloud provider to go with? If you are still wondering, there are only two you should consider, AWS and Microsoft Azure.

We are finding that prospects that are moving and executing have figured out all three components of their Digital Transformation. IDC also states that by 2019, 40% of All Digital Transformation initiatives, and 100% of all effective IoT efforts, will be supported by Cognitive/AI capabilities. I am excited about our future and the AI / Machine Learning software we have built to bring value to large industrial transportation companies looking to move from Unplanned to Planned using a data approach to complement their engineering based condition monitoring approach.