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

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 bfbusinesses 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 around nuanced tuning and subtle, yet complex, performance enhancements. Further, experts tend to make broad assumptions about the data that will be used in such models (e.g., consistent and clean data sources). With these arguments, I couldn’t agree more.

 

WHY IS THERE A MISSING LINK?

At Predikto, I have overseen many deployments of our automated predictive analytics software within many Industrial IoT (IIoT) verticals, including the Transportation industry. In many cases, our initial presence at a customer is in part due to limited short-term value gained from an internal (or consulting) human driven data science effort where the focus had been on just what Kalyan mentioned; a focus on the “model” rather than how to actually get business value from the results. Many companies aren’t seeing a return on their investment in human driven data science.

wallThere are many reasons why experts don’t cook business objectives into their analytics from the outset. This is largely due to a disjunction between academic expertise, habit, and operations management (not to mention the immense diversity of focus areas within the machine learning world, which is a separate topic altogether). This is particularly relevant for large industrial businesses striving to cut costs by preventing unplanned operational downtime. Unfortunately, the bulk of the effort in deploying machine learning solutions geared toward business value is that one of the most difficult aspects of this process is actually delivering and demonstrating value to customers.

WHAT IS THE MISSING LINK?

In the world of machine learning, over 80% of the work revolves around cleaning and preparing data for analysis, which comes before the sexy machine learning part (see this recent Forbes article for some survey results supporting this claim). The remaining 20% involves tuning and validating results from a machine learning model(s). Unfortunately, this calculation fails to account for the most important element of the process; extracting value from the model output.

In business, the goal is to gain value from predictive model accuracy (another subjective topic area worthy of its own dialog). We have found that this is the most difficult aspect of deploying predictive analytics for industrial equipment. In my experience, the breakdown of effort required from beginning (data prep) to end (demonstrating business value) is really more like:

40% Cleaning/Preparing the Data

10% Creating/Validating a well performing machine learning model/s

50% Demonstrating Business Value by operationalizing the output of the model

The latter 50% is something that is rarely discussed in machine learning conversations (with the aforementioned exception). Veeramachaneni is right. It makes a lot of sense to keep models simple if you can, cast a wide net to explore more problems, don’t assume you need all of the data, and automate as much as you can. Predikto is doing all of these things. But again, this is only half the battle. Once you have each of the above elements tackled, you still have to:

Provide an outlet for near-real-time performance auditing. In our market (heavy industry), customers want proof that the models work with their historical data, with their “not so perfect” data today, and with their data in the future. The right solution provides fully transparent and consistent access to detailed auditing data from top to bottom; from what data are used to how models are developed, and how the output is being used. This is not only about trust, but it’s about a continuous improvement process.

Provide an interface for users to tune output to fit operational needs and appetites. Tuning output (not the model) is everything. Users want to set their own thresholds for each output, respectively, and have the option to return to a previous setting on the fly, should operating conditions change. One person’s red-alert is not the same as another’s, and this all may be different tomorrow.

Provide a means for taking action from the model output (i.e., the predictions). Users of our predictive output are fleet managers and maintenance technicians. Even with highly precise, high coverage machine learning models, the first thing they all ask is What do I do with this information? They need an easy-to-use, configurable interface that allows them to take a prediction notification, originating from a predicted probability, to business action in a single click. For us, it is often the creation of an inspection work order in an effort to prevent a predicted equipment failure.

Predikto has learned by doing, and iterating. We understand how to get value from machine learning output, and it’s been a big challenge. This understanding led us to create the Predikto Enterprise Platform®, Predikto MAX® [patent pending], and the Predikto Maintain® user interface. We scale across many potential use cases automatically (regardless of the type of equipment), we test countless model specifications on the fly, we give some control to the customer in terms of interfacing with the predictive output, and we provide an outlet for them to take action from their predictions and show value.

As to the missing 50% discussed above, we tackle it directly with Predikto Maintain® and we believe this is why our customers are seeing value from our software.

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Robert Morris, Ph.D. is Co-founder and Chief Science/Technology Officer at Predikto, Inc. (and former Associate Professor at University of Texas at Dallas).

Context is King to Operationalize Predictive Analytics

contextisking

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” related to the analytics.

Once data has been tackled, the next step is to perform some kind of data crunching or analytics to derive insight and hopefully perform an “Action” that brings real value to the business. Predikto is laser focused on Predictive Analytics for Industrial Fleet Maintenance and Operations “Moving Unplanned to Planned”. We spend a lot of time figuring out what “Action” our customer will take from the configuration of the Predikto Enterprise platform software.  Up to now, I have not mentioned Context. So, why is Context King?

The reason is that once our platform and the power of Predictive Analytics is able to provide an actionable warning that a piece of equipment is at a high risk for failure, Context becomes the next huge hurdle.  The first reaction by a user of our software is “Why should I care about this Predikto warning?”, “Why is this relevant?”, “Why is this warning critical?”, “I don’t trust this warning from a machine learning algorithm?”, etc…  You get the point.

This has driven Predikto to invest heavily in technologies and capabilities that help the maintenance expert or equipment engineer with “Context” as to why they should care or why this warning is important.  Users are able to easily drill through all of their maintenance, asset usage history, diagnostic codes, sensor data, and any other data which has been loaded into our platform.  The “Context” is King in order to empower the subject matter expert to confirm or validate prior to automatically performing the “Action” our software is recommending.

Next time you are rolling out a Big Data solution, focus on the key activities a user will take after you have tackled the data.  What automated action recommendation can I give experts and what Context can I provide to help them make a more informed and confident decision.

5 Reasons Why You Suck at Predictive Analytics

reasons why you may suck at Predictive Analytics

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 sound easy, yet the road to successful predictive analytics implementations seems to be littered with the corpses of many a well-intentioned executive.

Many of our customers come to us, after they have spun their wheels in the mud trying to implement big data projects on their own or with the help of the talking heads. Below is a list of what I believe to be the top reasons for failed projects with buzzwords omitted:

  1. Data Science and Engineering alignment Fail: Or… The fear that engineering will cannibalize data science. After all, “If I can automate Data Science, why do I need the Data Scientists?”, the reasoning goes. Aligning both camps is difficult in larger organizations, as turf-wars will erupt.  Analytics software should seek to include the data science day-to-day activities rather than exclude them.
  2. Your data sucks: Nothing can save you here. If your vendor/manufacturer is providing shoddy data, you won’t be able to predict or analyze anything. Any consultant that tells you otherwise, is selling fertilizer, not analytics. It is best to reach out to your data generator / vendor and work with them to fix the root of the problem.
  3. You hired IBM: Watson does well with Jeopardy questions, but sadly couldn’t even predict the most recent round of IBM layoffs.
  4. You build when you should buy: Predictive Analytics is really hard, and chances are that it’s not your core competency, so why are you bringing all of this in-house? The real short term costs of implementing and maintaining custom software, data science groups, engineering groups, and infrastructure costs, can easily eat away millions of dollars, and you’re placing really big bets on being able to hire the high-level talent to pull it off.
  5. Operations Misalignment: Predictions are useless unless there is a someone or a some-thing to act on the results. It’s important to make operations a partner in the initiative from the onset. Increasing operational efficiency is the goal here, so really… Operations is the customer. A tight feedback loop with ongoing implementation between both camps is a must.

And so that’s the gist of it – 5 bullets forced out of me at our marketing department’s insistence. As much as I enjoy mocking the hype-masters in the industry, these days I find myself extremely busy helping build a real startup, solving real-world problems, for the Fortune 500, for real dollars. 😉

A Software Industry Veteran’s Take on Predictive Analytics

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 event, whether that is a battery on a transit bus about to fail, or indications that a fuel injector on a locomotive diesel engine, while still operating, is doing so at a less than desired level of performance.   We predict these events in a time horizon that allows the customer to take action to rectify the issue before it creates a problem, in a way that minimizes disruptions to operations.  Our technology is cutting edge Open Source, leveraging Spark, Python and Elastic Search hosted by AWS.

The use cases we’re being asked to solve are fascinating and diverse.   Some companies are contacting us as part of an initiative to transform their business model from selling capital assets to selling a service, an approach popularized by Rolls Royce with their jet engines, the “power by the hour” approach and similar to the software industry’s transition from selling perpetual licenses with maintenance contracts, to selling Software as a Service (SaaS).  In order to sell capital assets like construction equipment and industrial printing equipment this way, our customers will offer service level agreements, with Predikto in place to allow them to proactively deal with issues likely to degrade their service commitment.  So while our tactical focus has been on helping clients maximize product “uptime”, the strategic driver is helping them transition to a new way of generating revenue while getting closer to the customers.  It’s been gratifying to realize the impactful role our offering is playing in facilitating these transitions.

Other organizations are complex, asset-intensive businesses, where an equipment failure can have a cascading effect on revenues and customer service.  For example in the work we are doing with railroads we’ve learned there are a multitude of areas where sub-optimal performance of equipment or outright failure, can have significant impact.  The North American railroad network in 2014 set new records for revenue-ton-miles, a key efficiency metric; this was accomplished over a rail network which is highly congested.   In this environment, a delay has huge ripple effects.  Any number of factors can lead to a delay, ranging from a rockslide blocking a section of track to a locomotive breaking down, to a wheel failure on a rail car, which can cause a derailment.   On top of this, in order to operate safely and comply with government regulations, railroads have invested heavily in signaling and equipment monitoring assets, as well as machinery to maintain the track and roadbeds, which must work reliably.  Our abilities to implement in weeks and generate actionable predictions regarding locomotive and rail car health, as well as monitoring other equipment and even the condition of the rails, are making a major difference in helping to facilitate efficient, safe rail operations.

 

Having a blast…more to come.

Kevin Baesler, VP of Sales

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

Predikto raises $4M from TechOperators, ATA to predict machine failure

By Urvaksh Karkaria
Staff Writer-Atlanta Business Chronicle

Predikto Raises $4m to predict machine failures

Atlanta software firm Predikto has raised nearly $4 million to help manufacturers predict product failures earlier.

Predikto’s software engine — dubbed Max — allows manufacturers, railroad companies and other asset-intensive industries to predict equipment failure and warranty claims.

By detecting failures before they happen, companies can increase productivity, reduce downtime and tweak the manufacturing process to increase production volume, CEO Mario Montag said.

Max — an artificial intelligence and machine learning software robot built to design custom algorithms — uses real-time sensor data, historical maintenance records and past failure data to predict equipment breakdowns.

“Clients give us their data and (Max) spits out algorithms that can predict when a piece of equipment is going to fail,” Montag said

While predictive analytics software is widely used in business, manufacturing has been late to leverage Big Data to squeeze efficiencies.

Predikto is riding the wave of the industrial Internet of Things. Pumps, motors and other parts are being manufactured with on-board sensors that deliver tons of data on the health and performance of the devices.

“The industrial Internet of Things is creating a drive to do more with data,” Montag said. “The big data revolution of being able to do more and chew more information is sweeping through industrial manufacturing.”

Predikto raised the $3.6 million in a Series A round led by TechOperators, an Atlanta early stage venture firm managed by a quartet of serial entrepreneurs with billion-dollar exits under their belts.
Super angel groups Atlanta Technology Angels (ATA) and Angel Investor Management Group (AIM) also participated in the Predikto raise.

Predikto’s technology targeted to specific equipment and industry sectors, and its managed-service approach of delivering insight rather than tools, differentiates the startup, said Said Mohammadioun, partner at TechOperators.

“The market expects solutions rather than tools,” Mohammadioun said. “They don’t want to be in the business of figuring out how to use tools.”

The capital will be used for product development and sales and marketing — critical challenges for the startup.

Predikto must continue to innovate to stay ahead of the competition, while taking as much marketshare as possible, said Stephen Walden, a board member at Atlanta Technology Angels.

“That’s why this raise was so important for them to get into the market quickly,” Walden said. “Right now they’ve got, I won’t say a lock on the market, but a proprietary algorithm that nobody else can yet match.”

Launched in late 2012, Predikto is targeting a large addressable market. The industrial predictive analytics market in North America is estimated at more than $10 billion in annual sales, Montag said.

“Our sweet spots are continuous manufacturing facilities, such as steel plants, food & beverage plants and transportation companies with distributed assets — airplanes, trains and truck fleets,” Montag said. Siemens, for example, uses Predikto software to detect failures in train doors and diesel engines.

While the application of predictive analytics has so far been limited to the financial services and retail sector, the next market is industrial systems. Until recently, engineers typically followed a standardized maintenance schedule for industrial equipment, similar to annual maintenance schedules for automobiles.

Predikto relies on real-time data from sensors in the machinery, such as vibrations, electrical usage, and ambient temperature, to give engineers a more efficient predictive maintenance process. Rising temperatures, for instance, when combined with other things, can be a sign of inadequate lubrication, or an incorrectly fitting part that’s causing friction.

“The secret is in being able to run all these variables at once through a sophisticated algorithm to tell what is really going on,” Walden said.

Keeping maintenance downtime for critical equipment to the minimum required can save operators millions of dollars.

“If an engine that powers a steel mill costs $1 million for every hour it is shut down for maintenance, you’d rather not do (maintenance) every thousand hours, if you can do it every five thousand hours,” Walden said.

In the future, Predikto plans to target the booming oil and gas industry.

“The pain of asset downtime in that industry is significant,” Montag noted, adding it costs $500,000 for every day an oil rig is shutdown.

For Predikto, future success will depend on getting customers to fix things proactively, which requires a change in way of doing business, Mohammadioun said

“Companies know how to fix things that break — now, we are able to tell them when things are going to break,” he said. “That requires a change in culture.”

Via:: http://www.bizjournals.com/atlanta/blog/atlantech/2015/01/atlantas-predikto-raises-4m-from-techoperators-ata.html

 

Using Predictive Analytics in Maintenance

predictive analytics for maintenence
Predictive Maintenance is the best type of maintenance a company can undertake, but not all assets classes and applications justify Predictive Maintenance. That is why the best type of maintenance is the type that works for each client. The latest technology in Predictive Maintenance is the use of Predictive Analytics. In some cases Predictive Analytics is reaching accuracies above 95% to predict an asset failure. These results are much higher when compared to traditional predictive maintenance techniques like Lubrication Analysis, Infrared, and Vibration. These are all excellent techniques and companies should continue using them if they are seeing success reducing downtimes, extending the lifetime of equipment, and subsequently saving money.

In the MAPCITE blog, Eric Spiegel, CEO of Siemens U.S.A., consider that “while analytics were implemented widely in industries such as banking and communications initially, we view capital-goods organizations as a huge untapped opportunity, driven primarily by the “Internet of things” and the significant potential to optimize product development, supply chain and asset related services. One example is predictive maintenance – if we were able to better predict when critical and expensive equipment is most likely to fail, we could reduce downtimes, extend the lifetime of the equipment, and realize significant savings”. Read the entire story HERE.