August 1st 2016

Wise Practitioner – Predictive Analytics Interview Series: Brian Reich, Former Director at The Hive

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, The Data Set You Can No Longer Ignore: Consumer Engagement with Social Issues, at Predictive Analytics World New York, October 23-27, 2016, we asked Brian Reich, former Director at The Hive, a Brian Reich IMAGEfew questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: We started by analyzing current donors. From our current donors, we created lookalike models that gave us an appreciation for who was already responding and taking action to support refugees – and, of course, the much larger universe of people who were not yet engaged. We also conducted a national survey to measure people’s knowledge and support of refugee aid, along with a host of related issues, and we used that information to build models that explored the population beyond the lookalikes – what they know about refugees, what they might need to understand better in order to be motivated to take action, and similar.

We started all this work when awareness and interest in the global refugee crisis was pretty limited – and certainly confined to only people who were already deeply knowledgeable on the subject. Then, at the end of last summer, that all changed. The number of refugees flooding out of Syria into Europe exploded. Heart-breaking pictures of a young refugee who drowned trying to make it to safety suddenly made headline news and took over social media channels. All of a sudden, the global refugee crisis was the top story all over the world. President Obama challenged Americans to step up and help. The Pope said it was our moral responsibility as a society to aid refugees. The attention and the interest in the refugee crisis was higher than ever before. And we were able to organize our response, both short-term and long-term using the data as a guide.

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: Everything is informed by the data, both directly and indirectly. We have an unprecedented level of sophistication that we can apply – who we target, how we position issues related to the refugee crisis, and the ways we work with partners. Beyond our specific efforts:

  • We have demonstrated that we aren’t a traditional non-profit organization, but rather a sophisticated, engagement-oriented organization that approaches engagement with a combination of political, consumer, media, and tech expertise that hasn’t been attempted before.
  • We have clear insights to our audience and can prioritize accordingly – not just look at who might engage based on their past behaviors, but look to engage people who are likely to engage if given the right opportunity.
  • We build our messages based on the rapid-message testing, so we don’t have to rely on what our gut tells us.
  • We know the best ways to reach individuals, whether digital ads or snail mail, through partners, or another method, so we don’t waste time or resources on audiences that aren’t likely to take action.

And with all of this, we’ve been able to improve our organizational capacity and shift the way we think about our challenges. We’re looking at individuals – human beings – not donors, or advocates. We can do a much better job tapping into what we know people are already doing, or comfortable doing, instead of trying to compel people to do what we think benefits our organization best. Our cause is important, but we need to think more about the people we are talking to – understanding who they are and what motivates them to ultimately engage them and support our cause.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: Prior to doing the modeling, we had been making assumptions based on our gut – and they were pretty good. While the data validated our assumptions, it also revealed a whole new set of opportunities. It uncovered some surprising untapped geographic markets, hotspots in the parts of the country that we had never thought of, groups of people who don’t fit the stereotype of those who would have been our targets. Over the past several months we have used the data to reach and engage hundreds of thousands of new people who would not have been targetable without the data.

Q: What surprising discovery or insight have you unearthed in your data?

A: Beyond target audiences, the data has expanded the ways we think about how to engage people. Instead of relying on the existing messages – the ways you commonly hear about the refugee crisis from a nonprofit or through media coverage – we now had messages that have been tested and verified by data science. Instead of speaking about the refugee crisis as an emergency situation, or telling stories of the horror refugees face or hope they retain against all odds, we can present issues in ways that Americans understand, connect with on a more personal level, or make sense of through an experience they can appreciate. All of these insights continue to show how data enables us to target more efficiently and effectively.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: We knew that the traditional methods and messages wouldn’t be enough to get new potential supporters engaged. It was clear that Americans cared about helping refugees, but we needed to know more so we could determine how to get people more involved, to take more meaningful actions. We used our data models, plus survey research and rapid-response message testing, to experiment with different efforts we thought could be most effective, and learn the most in the shortest period of time.  And it paid off in a big way.

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Don't miss Brian’s conference presentation, The Data Set You Can No Longer Ignore: Consumer Engagement with Social Issues, at Predictive Analytics World New York on Wednesday, October 26, 2016 from 3:30 to 3:50 pm.  Click here to register for attendance.  

By: Eric Siegel, Founder, Predictive Analytics World

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July 29th 2016

Wise Practitioner – Predictive Analytics Interview Series: Gary Neights at Elemica

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predicting Behavior in Chemical Industry Supply Chains, at Predictive Analytics World New York, October 23-27, 2016, we asked Gary Neights, Senior Director at Elemica, a few questions about his work in Gary Neights IMAGEpredictive analytics.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: Predictive models for pharma and retail are often used to influence consumer behavior or give direction to research efforts. These models yield results that are reviewed by experts before being acted upon.   The predictive system for manufacturing supply chains that I will discuss drive real-time manufacturing execution decisions by front-line employees.  Commitment of resources such as labor, manufacturing capacity, raw materials, and logistics capacity can occur in near real-time.   Accurate, real-time data flows from customers, distributors, suppliers, and carriers are required to develop a full picture of the situation and help provide the best level of decision support.

Q: In your work with predictive analytics, what behavior do your models predict?

A: One example is under supply or over supply conditions.  Over supplying finished goods may lead to price discounting while under supplying material to a downstream manufacturing process may shut down operations.  Another example is predicting which perishable materials in a complex supply chain network are nearing expiration so they can be expedited to an appropriate manufacturing facility.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: Supply chain decisions have financial impact and need to be taken in near real-time.   Product-by-product and plant-by-plant predictions can lead to information overload and indecision.   For example, if rail cars to a manufacturing site are predicted to be late do I dispatch trucks as a rush shipments… or dip into safety stock?   If trucks, how many?  Over the long-term data may be analyzed systematically and accounted for during periodic planning cycles or contract renegotiations.

Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?

A: A predictive Railcar KanBan system drove a working capital savings of greater than $400K / year for one product and inventory replenishment accuracy was increased from less than 55% accuracy to greater than 80%. This allowed a 20% reduction in safety stock levels and 40% reduction of leased railcars. 

Q: What surprising discovery have you unearthed in your data?

A: In one case a graphical review of time-series data indicated that a manual supply chain management process was systematically driving costly inventory swings.  The planner was not correctly accounting for transit times between locations, nor the operating hours for shipping and receiving operations. This was corrected by a correctly tuned predictive system.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: A common theme I hear is that the farther you are from the consumer the harder it is to get accurate demand data.  We will share one approach that supports manufacturers systematically aggregating demand to improve predictive accuracy.

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Don't miss Gary’s conference presentation, Predicting Behavior in Chemical Industry Supply Chains, and workshops at Predictive Analytics World New York on Tuesday, October 25, 2016 from 3:05 to 3:25 pm.  Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

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July 27th 2016

Wise Practitioner – Predictive Analytics Interview Series: Dr. Sarmila Basu at Microsoft Corporation

In anticipation of her upcoming conference presentation, Predictive & Prescriptive Analytics Helps Keep Kids in School at Predictive Analytics World London, October 12-13, 2016, we asked Dr. Sarmila Basu, Senior Director, Data & Decision Sciences Sarmila Basu IMAGEGroup at Microsoft Corporation, a few questions about her work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: We do predictive analytics for business groups within the company as well as for customers outside. For internal groups our predictive analytics involves things like:-

  1. Predictive models for customer attrition for a specific subscription based product.
  2. Models for antipiracy efforts to identify compromised license keys
  3. For some of our external customers we have built models for predictive maintenance to plan for potential failure of their machineries.

 

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: Predictive analytics has delivered impact through revenue enablement and cost savings. When we are able to identify pirated/compromised license keys, that helps with revenue recovery. In cases of customer attrition modeling, we were able to start proactive retention efforts.  

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: Our antipiracy analytic effort has led to revenue recovery worth $130million last year.

Q: What surprising discovery or insight have you unearthed in your data?

A: Surprising discovery varies from project to project. We sometimes find unexpected information or insight by combining multiple data sources. People often complain about their data being inadequate for any meaningful conclusion, but it is more an issue of broken data than bad data. Data Scientists have to be able to stich data from multiple sources together and build a cohesive story.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: I will be talking about some of the use case scenario that has led not only to revenue impact for us but more importantly in terms of social good, it has delivered great impact.

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Don't miss Dr. Basu’s conference presentation, Predictive & Prescriptive Analytics Helps Keep Kids in School on Thursday, October 13, 2016 at 11:45 am at Predictive Analytics World London. Click here to register to attend.

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July 22nd 2016

Wise Practitioner – Predictive Analytics Interview Series: Dae Park and Vijay D’Souza at Government Accountability Office (GAO)

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

In anticipation of their upcoming conference co-presentation, Characteristics for Those Dae Park IMAGEClaiming Social Security Benefits Early, at Predictive Analytics World for Government, October 17-20, 2016, we asked Dae Park, Assistant Director at Government Accountability Office (GAO), and Vijay D’Souza, Director at Government Accountability Office (GAO), a few questions about their work in predictive analytics.

Q:  How would you characterize your agency's current and/or planned use of predictive analytics?  What is one specific way in which predictive Vijay D'Souza IMAGEanalytics actively drives decisions in your agency?

A: A core function of the Government Accountability Office (GAO) is to use data to objectively evaluate government programs and spending. We both develop our own models and evaluate agencies’ own models to evaluate the effectiveness of government programs. For example, we have completed numerous evaluations of Medicare payment policies to evaluate possible effects on quality and access to care.

Q:  Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: The biggest problem we face is acquiring and validating data from the agencies we audit. Data are provided in multiple formats and often have quality issues. These include duplicate, incomplete, or inaccurate entries, and inconsistencies among records. This challenge is compounded when we merge data from multiple agencies or multiple sources to conduct more sophisticated analyses.

Q:  Can you discuss any near term goals you have for improving your agency's use of predictive analytics?

A: There is an increasing awareness across our analyst community of the value of predictive analytics to measure the effectiveness of federal programs. This includes training and outreach efforts from our analytics experts to other agency staff. We are also taking greater advantage of open source tools for analytics and developing strategies to increase the amount of staff that can help with the initial stages of data acquisition, evaluation, and preparation.

Q:  Can you describe a successful result from the employment of predictive analytics in your agency, i.e., cost avoidance, funds recovered, improved efficiency, etc.

A: We analyzed TSA’s program to detect more risky air travel passengers and determined it was not based on sound evidence. In another case, we evaluated a Department of Transportation program that used a large amount of data to calculate safety scores for commercial trucking carriers and to ostensibly determine the likelihood of crashes. We found that violations used in the agency model either occurred too infrequently to be useful, or were not otherwise predictive of crashes.

Q:  Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Government.

A: To better understand the circumstances faced by those who claim early Social Security benefits, GAO examined demographic and occupational characteristics associated with early claiming. More specifically, we examined the characteristics and income of early claimers using data from the Health and Retirement Study. I look forward to discussing our findings in detail at the session.

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Don't miss Dae and Vijay’s conference co-presentation, Characteristics for Those  Claiming Social Security Benefits Early, on Monday, October 17, 2016 from 11:25 am to 12:10 pm at Predictive Analytics World for Government. Click here to register to attend.

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

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July 19th 2016

Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott of SmarterHQ

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, The Revolution in Retail Customer Intelligence, at Predictive Analytics World New York, October 23-27, 2016, we asked Dean Abbott, Co-Founder and Chief Data Scientist of SmarterHQ, a few questions Dean Abbott imageabout his work in predictive analytics.

Q: In your work with predictive analytics, what behavior do your models predict?

A: I’ve built models that predict a wide variety of behaviors and patterns. A short list is provided here:

  • Customer behavior: response, churn, product up-sell and cross-sell, best marketing creative, days to next purchase, days to next visit;
  • Signals (radar): tank, truck column of tanks; (sonar): man-made vs. biologic;
  • Financial: fraud or suspicion of fraud, debt repayment period, debt repayment amount, insurance claim repayment likelihood, claim amount of repayment.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A:  I’ll speak to SmarterHQ, the company I’m Co-Founder of and Chief Data Scientist. There is no one specific way our predictive models drive decisions, but they are involved in the decision-making process in several ways, all related to selecting customers to promote to, whether that be selecting customers to send an email to, show a display ad, or present content on a page that is of greater interest to the customer.

Consider our models that predict the likelihood that someone will purchase a product during a visit to the company’s web site within 3 days. Each visitor is scored while they browse on the website and at the end of their session. The company now wants to create a new campaign to increase sales of a particular product by emailing them a promotion code with a 20% discount. If the customer is likely to purchase a product on the web site within 3 days, the models will exclude these customers from the email list; why take away margin from sales that are likely to occur anyway. Or what if a customer was very likely to purchase within 7 days last week but is no longer likely this week? This is a form of churn (but based on expected behavior, not actual behavior), and these customers could be given incentives to visit again.

Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: It is difficult to describe most of the results my models have generated because they are considered sensitive information for the company or government agency. I’ve had models in use by organizations for 10 years before they were refreshed. I’ve had another model so successful that it was put on the “do not tell” list by the organization because it became a strategic initiative for the organization. I’ve had fraud models identify multi-million dollar cases to investigate that were clearly fraud but had previously eluded detection.

Q: What surprising discovery have you unearthed in your data?

A: Most surprising? There have been many surprises over the years, usually related to the data itself and patterns of behavior that we may overlook, but are important nevertheless. For example, with the days to next purchase models, one expects that visitors on a website who look at lots of hot products are more likely to purchase soon; these are engaged visitors. However, it turns out that some of the most likely purchasers are those who visit just one item. The vast majority of the time, one-item visitors are not engaged and therefore are unlikely to purchase. But, if these one-item visitors were previously highly engaged, it’s a different story; they are focused like a laser beam on one product only. So the surprise was that there is this subset of visitors who look awful but are actually fantastic!

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: The most important take-away in my talk is this: When you prepare data for modeling, think about how the algorithms interpret the data. Each algorithm has weaknesses that can result in strange or misleading behavior. It’s our job as predictive modelers to help the algorithms do the best job they can.

Q: In addition to keynoting, you will be teaching two one-day workshops at PAW New York, Supercharging Prediction with Ensemble Models and Advanced Methods Hands-on: Predictive Modeling Techniques. How would you advise attendees to choose between these workshops and would it even make sense to attend both?

A: There are many workshop options, and all of them are worthy of attending. I think of the Supercharging and Advanced Methods workshops as complementary to your Online Introduction to predictive and John Elder’s Modeling Methods, with the sequence being (1) Intro, (2) Modeling Methods, (3) Advanced Methods, and (4) Supercharging. The Modeling Methods can be taken the day before Advanced Methods in the same conference; Modeling Methods provides a framework for predictive modeling, and Advanced Methods lets you try it out on commercial software. Supercharging takes predictive modeling to the next level, introducing the methods that win modeling competitions and have provided me with extra accuracy has made the difference between successful models and very successful models in my consulting practice.

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Don't miss Dean Abbott’s conference presentation, The Revolution in Retail Customer Intelligence, and workshops at Predictive Analytics World New York on Tuesday, October 25, 2016 from 10:30-11:15 amClick here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

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June 16th 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Chris Labbe at Seagate Technology

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2016

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Building a Predictive Analytics Organization, we interviewed Chris Labbe, Chris Labbe HeadshotManaging Technologist at Seagate Technology. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: ​Manufacturing at a company like Seagate means volume.  Volume of parts produced is 10’s of millions per quarter and each of these devices generates many MB of data.  Daily we produce several TB of data from the drives themselves.  Then we have vertically integrated components generating a couple more TB per day and a massive number of machines that will benefit from advanced sensors.  This magnitude and velocity of data is well beyond the typical retail / marketing analytics challenge and stresses our IT systems to the limits.

Q: In your work with predictive analytics, what behavior do your models predict?

A: Although we built this team with a desire to help fix engineering problems, we found much stronger partners in the business side of the company.  Partially this is because engineering is pretty structured in data management and resistant to “advanced analytics” since they often feel they are using advanced techniques already.  Meanwhile the business teams know that they need help.  As such, not only are we working on better methods for quality management of the production system, but also ideas like customer ordering predictability, supply chain management, inventory reduction and build-ahead risk.  In a way, these are all manufacturing challenges since bad decisions in the business front lead to inefficiencies in production.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: Our business is pretty mature, which means we have a lot of people that have been using data effectively yet inefficiently.  Some of the most important projects we are engaging in are attempts to pull the company into machine learned multi-variate methods instead of human biased univariate system for quality control.​

Q: Is your company supportive of the work your team is doing?  And are they well prepared to execute on the models and systems you develop?

A: We are very fortunate to have the attention of the President of Engineering, Manufacturing & Sales.  This means a lot in terms of stability for the team while we develop some of the projects as it can take a while to demonstrate effectivity.  Meanwhile, we came into this effort fairly unprepared for how big the gaps are in Seagate’s data management pipeline.  To move Seagate to a World Class Data Science company is going to take a lot of time and a lot of money.​

Q: What surprising discovery have you unearthed in your data?

A: There are several projects that we have been pulled in to help with visualizations and data automation.  What we see behind the scenes is often pretty scary, though.  From manual data processes that push sensitive data through email, to data manipulation between source and decision and even weak statistical methods being applied to the data.  Turns out we can help the company in many more ways than just machine learning.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.

A: In building this new division at Seagate, there have been many lessons to learn.  Probably the most important of them all is how to invest efforts up front on data engineering and BI visualization tools.  This has given us the “keys to the castle” by allowing the team to fully understand the underlying math in a tool.  Once the target group is excited about the improvements in the tool, then we can begin a discussion about improving the model behind the scenes.​  When we have started a project with “we can make your model better,” the progress is slow at best.​

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Don't miss Chris’ conference presentation, Building a Predictive Analytics Organization, at PAW Manufacturing, on Tuesday, June 21, 2016, from 4:45 to 5:30 pm. Click here to register for attendance. 

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

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June 13th 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Peter Frankwicz at Elmet Technologies

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Improved Statistical Process Control of Mature Manufacturing Processes Using Multiple Available Data Streams, we interviewed Peter Frankwicz, Senior Process Engineer at Elmet Peter Frankwicz imageTechnologies. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: Most small business manufacturing companies are focused on relatively simple statistical process control (SPC) or end-of-line quality control.  The next step to predictive statistical process control is a major undertaking in both collection of relevant manufacturing process data and product & yield metrics.  The data-based “return on investment” in higher yield has to overcome management angst of higher risk of product scrap at the end of the manufacturing line.

Q: In your work with predictive analytics, what behavior do your models predict?

A: Predictive statistical process control analytics are in use to optimize powder metallurgical properties, such as tap density, and sintered ingot properties for thermomechanical processing to sheet and rod products.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: Many small business manufacturing companies have a “we have always made it this way” mentality.  Predictive analytics and statistical process control methods allows process engineering to deliver data-based and statistically significant understanding of manufacturing processes to management.  Predictive analytics drives several specialized [Elmet Technologies refractory metal product – manufacturing process] combinations to optimize yield and quality.

Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?

A: Predictive models were developed to understand refractory metal powder properties.  Use of these models to direct manufacturing production resulted in an over $20,000 monthly reduction in scrap product in the “downstream” sheet rolling department.

Q: What surprising discovery have you unearthed in your data?

A: Data mining and statistical analysis of available process data revealed surprising manufacturing process sensitives.  Many of these process sensitives were only known at the level of tribal knowledge on the manufacturing floor.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.

A: There is a high need in small business manufacturing for simple and robust predictive analytical methods.  Even starting the path with data mining and statistical analysis of available data streams can discover surprising and valuable manufacturing process insights and yield optimization strategies.

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Don’t miss Peter’s conference presentation, Improved Statistical Process Control of Mature Manufacturing Processes Using Multiple Available Data Streams , at PAW Manufacturing, on Tuesday, June 21, 2016, from 2:40 to 3:25 pm. Click here to register for attendance.

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

 

 

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June 10th 2016

Wise Practitioner – Text Analytics Interview Series: Dirk Van Hyfte at InterSystems Corporation

By: Steven Ramirez, Conference Co-Chair of Text Analytics World Chicago

In anticipation of his upcoming conference co-presentation, Personalized Medicine and Text Analytics at Text Analytics World Chicago, June 21-22, 2016, we asked Dirk Van Dirk Van Hyfte imageHyfte, Senior Advisor for Biomedical Informatics at InterSystems Corporation, a few questions about his work in text analytics.

Q: In your work with text analytics, what behavior or outcome do your models predict?

A: To support the shift from reactive to pro-active medicine we look for patients who are at risk to develop Sepsis, Hepatitis C and Delirium. In the area of Behavioral Health we support harm reduction projects.

Q: How does text analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: The ability to effectively harness the mountains of unstructured data in healthcare is a key strategic asset for any successful organization.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: One California IDN has demonstrated great success in reducing sepsis mortality, bringing it down from 55% at the start of their interventions to a current level of 35%.  In an effort to bring sepsis mortality down even further to 25%, this IDN is now piloting the use of Text Analytic Tools to add unstructured data analysis to its armamentarium.

Q: What surprising discovery or insight have you unearthed in your data?

A: There are huge gaps in structured data fields. At a Cancer Registry we identified a data gap of 22% where HER-2 testing had been ordered but no definite outcome was recorded. The organization was able to identify a systematic shortfall in the availability of results and was able to investigate this.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Text Analytics World.

A: Numerous case studies, a few of which have been discussed above, have demonstrated the shortcomings of structured data.  Important information, such as disease risk factors, might not exist within structured data at all, and even when the appropriate structured data field exists within a data model, the data can be missing or unavailable.  Unstructured data, which comprises perhaps 80% of all healthcare data, has great potential to replace that missing structured data and/or complement what’s already there. 

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Don't miss Dirk’s conference co-presentation, Personalized Medicine and Text Analytics on Wednesday, June 22, 2016 from 3:30 to 4:00 pm at Text Analytics World Chicago. Click here to register to attend.

By: Steven Ramirez, CEO at Beyond the Arc, and Co-Chair of Text Analytics World

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June 6th 2016

Wise Practitioner – Text Analytics Interview Series: Michael Dessauer and Justin Kauhl at The Dow Chemical Company

By: Steven Ramirez, Conference Co-Chair, Text Analytics World Chicago

In anticipation of their upcoming conference co-presentation, Understanding our Customers' Customers' Customers' Needs – Text Analytics for B-to-B Businesses at Text Michael DessauerAnalytics World Chicago, June 21-22, 2016, we asked Michael Dessauer, Data Scientist at The Dow Chemical Company and Justin Kauhl, Computational Linguistics Expert at The Dow Chemical Company, a few questions about their work in text analytics.

Q: In your work with text analytics, what behavior or outcome do your models predict?

A:  Our team, Dow’s Advanced Analytics Group, service company-wide needs from any Justin Kauhlof our businesses or functions so our project outcomes are very diverse. In general, we get many requests for market insight which boils down to consumer needs, so we try to categorize product features and sentiment around those features.

Q: How does text analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: The delivered value statements have come from businesses using our market listening insights to secure value pricing on new product introductions. It’s easier to sell when we can point to overwhelming consumer needs that our analysis uncovers.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: In one case Dow was able to value price a product by 10X and our insights were given credit for contributing to that increase.

Q: What surprising discovery or insight have you unearthed in your data?

A: We have had success using external unstructured data to improve short-term forecasting. It was a bit unexpected but very helpful for some of our existing modeling efforts.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Text Analytics World.

A: We intend to share our experiences, both successful and unsuccessful, in developing effective text analytics projects with internal clients. We will share our process, technologies employed, and our best practices learned over the past several years working on text analytics projects.

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Don't miss Michael and Justin’s conference co-presentation, Understanding our Customers' Customers' Customers' Needs – Text Analytics for B-to-B Businesses on Tuesday, June 21, 2016, from 1:30 to 2:15 pm, at Text Analytics World Chicago. Click here to register to attend.

By: Steven Ramirez, CEO at Beyond the Arc, and Co-Chair of Text Analytics World

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June 3rd 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Edward Crowley at The Photizo Group, Inc.

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2016

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference Edward Crowley imagepresentation, Predictive Analytics – What is 2% Worth, we interviewed Edward Crowley, CEO at The Photizo Group, Inc. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: The biggest challenge in any predictive analytics deployment is to have the understanding of the business processes, business models, and workflows required to build predictive analytics models that have a meaningful impact on the business. Clearly, these processes and models can be unique to each vertical. The bigger issue is, in my mind, that much of predictive analytics has been focused on marketing and customer facing applications; however, there are tremendous opportunities for business process and operational related predictive analytics solutions which bring immediate savings to your organization or your customer’s organization and which are often overlooked when the focus is on customer facing solutions.

Q: In your work with predictive analytics, what behavior do your models predict?

A: Today our models predict device failures for either A) very expensive capital equipment items which have significant costs associated with lost production capacity due to unexpected failures, or B) mass fleets (as high as several million units) of devices which benefit from predicting failure in order to reduce service costs, improve productivity, and early identification of mass failures.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: By understanding when a printer or copier will actually be out of toner (versus when it is ‘low’ on toner), the amount of toner left in the cartridge when it is replaced can be reduced from up to 35% of total capacity to less than 10% of capacity. In this example, our model accurately predicts when toners will be empty, shipping the toner ‘just in time’ before the printer runs out versus shipping the toner when the printer initiates a toner low alert.

Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?

A: Our first model, a toner delivery optimization model, reduces ‘lost toner’ and excess shipping costs by over 50% per device. This translates into over $100 per year, per device savings – or from $5M to $50M for each of our OEM customers. The ROI for the model is immediate since we deliver this in a ‘as a Service’ model where the client just has to sign up; we have already developed the solution.

Q: What surprising discovery have you unearthed in your data?

A: Initially, we thought that the wasted toner from throwing away toner cartridges early was around 15% of the total cartridge capacity – but a validation phase of our project where we actually measured the amount of toner in a large volume of returned cartridges identified that the average waste is closer to 32% per cartridge.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.

A: There are, in my view, three key building blocks to PA – industry knowledge, PA technology, and a knowledgeable team which can turn technology into a solution. It’s not just about the software – it’s about the industry knowledge!

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Don't miss Edward’s conference presentation, Predictive Analytics – What is 2% Worth, at PAW Manufacturing, on Wednesday, June 22, 2016 from 3:30 to 4:15 pm. Click here to register for attendance. 

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

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