Predikto Engineer Wins TAG YP Technologist of the Year Award!


Update: Will McGinnis WON the TAG Young Professionals One In A Millennial Technologist Award.  Well done!

One of Predikto’s own engineers, Will McGinnis, has been selected as a finalist in the TAG (Technology Association of Georgia) Young Professionals’ One In A Millennial Awards in the Technologist of the Year category. The winners will be announced at the banquet on Wednesday, September 23rd, at General Assembly in Ponce City Market.

At the banquet, there will be a keynote speech by Sangram Vajre, cofounder and CMO of Terminus, and networking with the nominees from the 8 awards categories:

  1. Technologist of the Year
  2. Professional Service Leader
  3. Sales Leader
  4. Marketing and Social Media Leader
  5. Top Recruiter
  6. Best Personal Brand
  7. Top Newcomer
  8. STEM Education Leader


Join us at GA tomorrow night and find Will if you’d like to talk about how Predikto is using big data technology and machine learning to revolutionize maintenance practices in transportation and heavy industry.

Consumer IoT solutions are cool, but useless?

It seems everyone is doing something with the “Internet of Things”, just like everyone was in “Big Data” 3 years ago, now it’s all about the “IoT”. But what are we actually gaining from all of these connected devices? I now have a page full of apps on my smartphone for all these devices, and sure that’s cool, and man do they make a cool demo at the slue of now IoT trade shows but how are they actually helping us? Are we saving money, by knowing that our refrigerator was 2 degrees warmer while we were putting up our groceries? Sure maybe the smart house has made us more efficient, we save a few dollars because we can now turn our A/C off from our office, but how does this effect business and add real value? Now finally companies and innovators have realized that all of these cool gadgets and innovations make for a cool demo but have little economic value. Many IoT innovation companies have started to migrate from Consumer IoT products and move towards the huge Industrial IoT opportunity. Why? Because an IoT innovation in the Industrial sector can be measured with real ROI and the business impact figures can be staggering. At the IoT Solutions World Conference in Barcelona (going on this week), IBM stated that 70% of the IoT value will be created in B2B use cases. They also stated that by 2025 IoT will have an economic impact of $11.1 Trillion PER YEAR.

We participated in two large international IoT conferences this year. Predikto was voted best startup at one of these conferences in San Francisco. There is a lot of interest in our underlying technology, but our actual traction comes more from tangible use cases than cool demos. The small number of tangible use cases presented at these IoT conferences by participating companies appalled me. I was in a breakfast meeting with business leaders and analysts one morning. One of the analysts asked the group for examples of real Industrial IoT use cases and it was crickets. Predikto was likely the smallest firm in the large table of 20+ people, and we were the only ones who could speak to use cases in the Industrial sector with real scars from what is working and what is not. Many of the others were speaking in hypotheticals and regurgitating what they read in blogs and articles.

There are over 12 different IoT market size figures quoted by leading companies. I mentioned IBM figures above, but others include: Cisco $19 Trillion in net profits by 2022, Gartner $300 Billion in incremental revenue by 2020, GE $15 Trillion added to global GDP in 20 years, and the figures go on and on. If you look at connected devices, the chart below from Business Intelligence shows 34 Billion connected devices by 2019.


Where will all these new revenues come from? I believe the bulk will be from the Industrial side of IoT.

I am getting tired of all of the hype about IoT. The reality is that most of the consumer IoT solutions and use cases are losing traction after the cool factor wanders off. Wearables have a dirty little secret; A survey by Endeavour Technologies shows that 50% of wearable users lose interest after a few months. What real value is there in being able to monitor your refrigerator or toaster from your iPhone? I can see some value in my coffee machine automatically turning on 20 minutes earlier along with my alarm shifting forward by 20min because Waze is recognizing heavier traffic in the direction of my first meeting that morning. Sure, but how many people will actually be using that type of integration and how do you provide measurable ROI in some of these use cases. Cool? Yes. Measurable impact with clear ROI? A lot more challenging to justify.

At Predikto, we are focused on planes and trains at the moment. We see clear tangible impacts to safety, operations, and maintenance optimization through our ability to help clients deliver tangible value to their real business problems.

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

“…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 most that claim they are more than an over glorified calculator are actual use cases that can demonstrate value. Without calling out any names, the one thing that these offerings share in common is the fact that they require services (i.e., consulting) on top of the software itself, which is a hidden cost, before they are operational. There is nothing inherently unique about any of these packages; all of the features they tout can be carried out via open-source software and some programming prowess, but here lies the challenge. Some so-called solutions bank on training potential users (i.e., servicing) for the long-term. These packages differ in their look-and-feel and their operation/programming language and most seem to either require consulting, servicing, or a data science team. In each of these cases, a data scientist must choose a platform/s, learn its language and/or interface, and then become an expert in the data at hand in order to be successful. In the real world, the problem lies in the fact that data tends to differ for each use case (oftentimes dramatically) and even after data sources have been ingested and modified so they are amenable predictive analytics, many decisions must be made and there’s a lot of room to be wrong and even more room to overlook.

“…a tall order for a human.”

Unfortunately, data scientists, by nature, are subjective (at least in the short term) and slow when good data science must be objectively contextual and quick to deploy since there are so many different ways to develop a solution. A good solution must be dynamic when there may be thousands of options. A good product will be objective, context driven, and be able to capitalize on new information stemming from a rapidly changing environment. This is a tall order for a human. In fairness, data science is manual and tough (there’s a tremendous amount grunt work involved) and in a world of many “not wrong” paths, the optimal solution may not be quickly obtained, if at all. That said, a data science team might not be an ideal end-to-end solution when the goal is for a long-term auto-dynamic solution that is adaptive and can to be deployed in an live environment rapidly and that can scale quickly across different use cases.typical solution

“…a good solution must be dynamic…”

End-to-end PdA platforms are available (Data Ingestion -> Data Cleansing/Modification -> Prediction -> Customer Interfacing). Predikto is one such platform where the difference is auto-dynamic scaleability that relieves much of the burden from a data science team. Predikto doesn’t require a manual data science team to ingest and modify data for a potential predictive analytics solution. This platform takes care of most of the grunt work in a very sophisticated way while capitalizing on detail from domain experts, ultimately providing customers with what they want very rapidly (accurate predictions) at a fraction of the cost of a data science team, particularly when the goal is to deploy predictive analytics solutions across a range of problem areas. This context-based solution also automatically adapts to feedback from operations regarding the response to the predictions themselves.

Predikto Solution Utilizing Predictive Analytics


Skeptical? Let us show you what Auto-Dynamic Predictive Analytics is all about and how it can reduce downtime in your organization. And by the way, it works… [patents pending]

Predikto Enterprise Platform

The Scalability of Data Science: Part 3 – Reality Check  

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.) for something that’s going to develop slowly and if it works at all, it may not work well. Have you made a wise investment?

This is the reality that most companies entering the realm of IoT and predictive analytics will face. Why? Most predictive analytic solutions can’t scale (i.e., can’t be rapidly applied across different problems). It’s too time consuming and too expensive and the value may be lost in a ramp-up attempt. A deployed predictive analytics solution must be scalable, fast, and affordable. A data scientist can be great (and many are), but they’re bound by the magnitude in which they can scale and the subjectivity of their respective approaches to the solution. There are many ways to approach data analysis that are correct, but there’s probably an alternative that is more valuable.

The next generation of predictive analytics solutions should be able to accomplish most, if not all, of the above automatically and rapidly with decreasing involvement from humans, and should perform as good or better than a good data science team; this is what Predikto has done (patents pending). We enable operations managers and data scientists by tackling the bulk of the grunt work.

I’m well aware that this may downplay the need for a large data science industry, but really, what’s an industry if it can’t scale? A fad perhaps. Data science is not just machine learning and some basic data manipulation skills. There’s much more to a deployed solution that will impact a customer’s bottom line. To make things worse, many of the key components of success are not things covered in textbooks or in an online course offering on data science.

It’s one thing to win a build the “best” predictive analytics solution (e.g., a Kaggle competition), but try repeating this  process of dozens times in a matter of weeks for predictions of different sorts. If any of these solutions are not correct, it costs real dollars. Realistically scaling in an applied predictive analytics environment should scare the pants off of any experienced data scientist who relies on manual development. Good data science is traditionally slow and manual. Does it have to be?

Rest assured, I’m not trying to undercut the value of a good data scientist; this is needed trade. The issue is simply that data science is difficult to scale in a business setting.

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

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 second or quicker and these data must be cleaned and aggregated with other data sources. It’s a big deal to get just this far. Next you have to become and expert in the data and begin cleaning and developing context-based feature data from the raw source data. This is where art comes into play and this part is difficult and time consuming for data scientists. Once a set of inputs has been established, then comes the easier part, applying an appropriate statistical model/s to predict something (e.g., event occurrence, time to failure, latent class, etc.) followed by validating and deploying the results. Oh yes, let’s not forget the oft unspoken reality of threshold settings for the customer (i.e., costs of TPs vs FPs, etc.). To this point, we’re assuming that the solution has value and it’s important to keep in mind that a data science team has probably never seen this sort of data ever before.

So on top of requiring computer programming skills, feature engineering prowess (which is art), understanding statistics/machine learning, and having good enough communication skills to both learn from the customer about their data and to be able to “sell” the solution, this must all be accomplished in a reasonable amount of time. We’re talking about 1 instance to this point, remember? And, we’re still not deployed. Do you have expertise in deploying data for the customer? Now repeat this situation ten times and you’re closer to reality. Your team may now just filled up the next 12 months of work and the utility of the solution is still unknown.