May 23rd 2016

Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Data Driven Selling: Enabling a Direct Salesforce with Tools that Re-Enforce Predictive Selling Methods at Predictive Analytics World Chicago, June 20-23, 2016, we asked Lawrence Cowan, Partner at Cicero Lawrence_Cowan imageGroup, a few questions about his work in predictive analytics.

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

A: A good portion of the advanced analytics work we do at Cicero deals with consumer behavior across all stages of the customer lifecycle.  So this would span from acquisition through the development of “typing” tools for targeting and segmentation, to response and uplift modeling for existing customers, to attrition modeling and customized intervention strategies.

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 defines our organization.  With it, we would not have an offering.  As a full service data-driven strategy consulting firm, it is our job to provide the technical and analytical expertise to help our clients leverage data to make smarter decisions.  And in all engagements involving predictive analytics, our ultimate objectives are results and implementation – if our clients cannot actively use the models and insights to make decisions, we have failed.

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

A: In a recent effort to help a financial institution optimize its marketing campaigns, we developed a model that identified customers who were more likely to respond to a CD campaign, and who were more likely to bring “new money” to the CD (opposed to simply shifting money from another account at the financial institution).  The results were very impressive, including the following metrics: 14x higher response rate, 4x increase in average deposits, 60% in “new money” compared to just 3% “new money” in the control group.

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

A: I’m always surprised at the opportunities unearthed with predictive analytics – things that you would never expect if it were not for efforts in data mining.  For example, for a large grocery retailer, we were able to identify two critical customer behavior trends (made possible through loyalty data) that were significant predictors of customer profitability.  These two trends were counter to heuristic judgment at the executive level (executives have since changed their perception of the event after seeing the compelling evidence.

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

A: How to leverage secondary data (household level data) to drive more business value from your predictive models.

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Don't miss Lawrence’s conference presentation, Data Driven Selling: Enabling a Direct Salesforce with Tools that Re-Enforce Predictive Selling Methods on Tuesday, June 21, 2016 from 10:30 to 11:15 am at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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May 20th 2016

Wise Practitioner – Text Analytics Interview Series: Pengchu Zhang and John Herzer at Sandia National Laboratories

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

In anticipation of their upcoming conference co-presentation, Enhancing search results relevance using Word2Vec Language Models at Text Analytics World Chicago, June 21-22, 2016, we asked Pengchu Zhang, Computer Scientist at Sandia National Laboratories, and Pengchu Zhang imageJohn Herzer, Enterprise Search Project Lead at Sandia National Laboratories, 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: We use the Word2Vec Neural Network model in our search application to predict word John Herzer imageusage in our corpus for a particular context.  Word2Vec consists of two models, the Continuous Bag of Words (CBOW) model and the Skip-Gram model.  The CBOW model lets us predict a target word given the surrounding words and conversely, the Skip-Gram model lets us predict the surrounding words given a specific word.  We use this capability to enhance our queries with term expansion.

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

A: We’re able to increase the relevant content that we return in our search results by enhancing the customer’s query with related terms or synonyms.  This automated way of identifying synonym-like terms is much more cost effective than trying to build and maintain a corporate synonym dictionary.

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

A: For one particular acronym query, ‘SAR’, our use of Word2Vec to expand the query into its definition of ‘synthetic aperture radar’ resulted in a 432% increase in relevant documents returned by the search engine.

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

A: We discovered that there were many more references to “buckyballs”, the spherical carbon molecule in our corpus than we realized.  Use of the Word2Vec model resulted in our query being expanded to include the word ‘fullerenes’, a term more commonly used in scientific papers for this molecule.

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

A: Using the Word2Vec model for query enhancement can help your enterprise move towards conceptual search.

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Don't miss Pengchu and John’s conference co-presentation, Enhancing search results relevance using Word2Vec Language Models on Tuesday, June 21, 2016 from 3:35 to 4:20 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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May 17th 2016

Wise Practitioner – Text Analytics Interview Series: Emrah Budur at Garanti Technology

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

In anticipation of his upcoming conference presentation, Tips and Tricks on Developing High-performance Fuzzy Name Search Engine to Prevent Terrorism Financing at Text Analytics World Chicago, June 21-22, 2016, we asked Emrah Budur, Senior Software Emrah Budur imageEngineer at Garanti Technology, a few questions about his work in text analytics.

Q: What is your topic mainly about?

A: Financial institutions and US businesses working with partners overseas must follow strict government regulations to prevent them from doing business with terrorist organizations or other sanctioned entities. In particular, banks are required to precisely detect inappropriate transactions with sanctioned entities out of large amounts of legitimate money transfers in real time. However, detecting names of sanctioned entities is challenging due to the high number of variations in terms of misspellings.

In this session, we will explore the tips and tricks of developing a high precision & recall fuzzy name search engine model that detects the names of sanctioned entities with greater accuracy and precision than search engine market leaders.

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

A:  Our model detects the names of sanctioned entities out of free format input financial texts with a higher degree of accuracy than search engine market leaders. The final outcome is expected to be the names which exist fully or partially in the free format input text with exact or high similarity.

Q: Can you share a concrete example about the topic?

A:  Yes. When you search for “Barrak Obama” on Google (with the intentionally incorrect spelling) you will see a panel on the right hand side featuring some information related to Barrack Obama. This means Google applied fuzzy search on our query and figured out the exact identity we intended to seek. When you search instead for “Barrak Obama elections” on Google, the identity panel on the right hand side will disappear. Although the intended information that we seek is exactly the same, Google failed to show the identity panel on the second query. The banks are required to extract the intended identity included in both queries.

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

A: Text analytics is helpful in various decision making processes. For example, it can help you figure out the correlation between the number of false positive predictions and the level of similarity your model can tolerate, then make a data driven decision about how much similarity your model should tolerate to prevent excess number of false positives.

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

A: One of the most popular measures of success when evaluating search engines is the F1-Score (and also the F5-Score). F-Scores range from a scale of 0 to 1 (worst = 0, best = 1). We benchmarked our model with a hand-curated training dataset against the domain leaders. We achieved an F5-Score = 0.91 where the scores of domain leaders remained under .70.

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

A: We are surprised to find out some algorithms which are well known in some other domains are fairly applicable to search engine domain. For example, we were surprised to figure out that “the market basket analysis algorithm” can be applied to discover the frequent/infrequent co-occurrence of multiple stop words. For example, the terms “engineering”, “cooperation”, and “limited” can be considered to be stop words and may be filtered out from search queries because they return vast amounts of unnecessary information. And it may be possible that the statement “Cooperation Limited” is also frequent according to your dataset. However, the expression “Engineering Cooperation Limited” may refer to a unique organization. So, ignoring all of these stop words completely will lead you to a false negative match. On the other hand, identifying and unearthing the frequent co-occurrence of “Cooperation Limited” by means of market basket analysis will let you identify the importance of the term “Engineering” in this context hence come up with a true positive match instead.

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

A: Running name searches to prevent doing business with sanctioned entities in the global banking industry is a highly sophisticated and critical area in the field of text analytics. With the help of the tips and tricks that we will present in this session, a tailor-made solution can be implemented which provides more accurate and timely results than the solutions of leading search engine domains.

Q: Is it possible to share this case and comment or ask a question even before the session?

A: Yes! You are more than welcome to share your questions and comments on this Q&A platform and by #detectivenamesearch on Twitter.

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Don't miss Emrah’s conference presentation, Tips and Tricks on Developing High-performance Fuzzy Name Search Engine to Prevent Terrorism Financing on Wednesday, June 22, 2016 from 2:20 to 3:05 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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May 13th 2016

Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Using Predictive Analytics to Optimize Organizational KPI's: A Panel Market Research Case Study at Predictive Analytics World Chicago, June 20-23, 2016, we asked Thomas Schleicher, Sr. Director, Measurement Science at National Consumer Panel, a few questions about his work in predictive analytics.

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

A: Our models predict a number of behaviors and outcomes, including attrition, compliance and the number of recruits needed to replenish households lost through churn. We also used predictive analytics in combination with test-control comparisons to further optimize business decisions.

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

A: At NCP, the ideal set of households participating on our market research panel would be fully representative of the nation's households while also fully participating in the panel by scanning and submitting all of their purchases. Using predictive analytics can help to ensure that the most optimal set of households is selected and maintained with respect to these and other metrics.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative? Do you see a role for integrating more “qualitative” impacts into a quantitative model?

A: We regularly evaluate ROI when we test new ways of engaging with our panelists in efforts to encourage them to remain as contributing panelists for years. However, we also take into consideration the value improved participation has to our clients. Although we are making progress on putting numbers on this sometimes qualitative impact, it is possible to undervalue an initiative by only referencing items that are easily quantifiable.

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

A: As we continue to develop our valuation of panelist compliance, we are learning additional ways to benchmark difficult to measure variables. More to come…

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

A: Although it can be challenging (or even impossible in some situations) to achieve “perfect” scores on your organization’s KPI’s, predictive analytics can leverage your data and business acumen to drive optimal business decisions.

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Don't miss Thomas’ conference presentation, Using Predictive Analytics to Optimize Organizational KPI's: A Panel Market Research Case Study on Tuesday, June 21, 2016 from 11:20 to 11:40 am at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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May 11th 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Dr. Matteo Bellucci at General Electric

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

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference Matteo Bellucci imagekeynote presentation, Changing the Way we Make Things: The Brilliant Factory, we interviewed Dr. Matteo Bellucci, Manager, Process System Lab at General Electric. 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: In a manufacturing environment such as the ones at GE, were we make low volume high value products, Return of Investment (ROI), R&D funding allocation and cultural resistance are probably the highest barrier for translating the lessons of predictive analytics into manufacturing. Given the “low volumes” it takes longer to see the benefit of an improvement on the factory floor. R&D is mainly dominated by Engineering functions that do not always fully understand the challenges of supply chain teams, thus allocating limited funding into new manufacturing technologies. Finally we still live in a world where ”trial and error” are dominating the mindset of supply chain teams, and the power of analytics is not fully engrained in the culture of those organizations.

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

A: The work we are doing at GE Global Research spans many different areas from virtually commissioning new factories before they are even built, to predictive maintenance algorithms or real time factory bottleneck detections.  

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

A: There are 3 main benefits of predictive analytics – 1) reduce cost of our supply chain operations, thus making GE more competitive; 2) increase the speed at which we can implement innovation in our products & supply chains; 3) drive real time decisions on the factory floor. For example if a CNC goes down unexpectedly, or if we see a surge in demand for a specific part, or if the production is “disrupted” by some R&D prototype that needs to be built, the system will inform us in real time of how to best utilize the assets that we have available within our internal or external supply chain, to still deliver at cost, and on time.

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: Over the last couple of years, we have proven many use cases in many of our factories. In one of them, a real time scheduling system is helping them gain significant productivity by providing suggestion on how best optimize assets on a day by day basis. Another use case was about predictive maintenance – we have proven to be able to detect an issue days before it would have actually occurred. We use analytics to improve parts yield and reduce cost of quality. We have seen many success stories where analytics has helped solve problems and deliver a better product, faster. Overall we believe the company is going to become stronger through the use of Brilliant Factory technologies.

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

A: There is an incredible amount of capacity at both machine level and plant level that can be unlocked by smart analytics that can help operators focusing on what really matters. Just by visually monitoring a machine you can get a 20% uptime increase. Most importantly the data are helping drive a behavioral change at the shop floor level and this is helping to accelerate some of the R&D and implementation efforts inside GE.

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

A: “If you went to bed last night as an industrial company, you’re going to wake up this morning as a software and analytics company.” – Jeff Immelt, GE Chairman & CEO. The presentation will be around Jeff’s quote with specific focus on how we are using analytics to optimize our own factories.

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Don't miss Dr. Bellucci’s keynote conference presentation, Changing the Way we Make Things: The Brilliant Factory, at PAW Manufacturing, on Wednesday, June 22, 2016 from 9:10 to 10:00 am. 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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May 6th 2016

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

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

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Analytics in Manufacturing Supply Chains – Predicting Behavior In Chemical Industry Supply Chains, we interviewed Gary Neights, Senior Director at Elemica. View the Gary Neigh imageQ-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: Two things come to mind.  First is the number and variety of signals that need to be processed. Data from customers, distributors, suppliers, contract manufacturers, carriers, and third party warehouses may need to be analyzed to get the full picture.   Second is that predictive data may need to be acted upon quickly and decisions can commit resources such as manufacturing capacity, raw materials, or logistics capacity.  Decision support systems are critical.

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 shutdown 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: Product-by-product and plant-by-plant predictions can lead to information overload and indecision.   Supply chain decisions have financial impact and need to be taken in near real-time.  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: In one example the predictive system drove inventory replenishment accuracy from less than 55% accuracy to greater than 80%.    This allowed a 20% reduction in safety stock levels.  The number of leased railcars was reduced by 40%.  This drove a working capital savings of greater than $400K / year for one product.

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

A: The bullwhip effect can occur at a micro level.  In one case a supply chain planner was tightly controlling resupply – on the phone every day to make sure they never ran out of stock.  A change in today’s demand drove new truck shipment.  A graphical review made obviously that the manual planning process was systematically driving large inventory swings.  It did not correctly account for lead times as well as shipping, manufacturing, and receiving calendars.  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 for Manufacturing.

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, Analytics in Manufacturing Supply Chains – Predicting Behavior In Chemical Industry Supply Chains, at PAW Manufacturing, on Wednesday, June 22, 2016 from 2:15 to 3:00 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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May 2nd 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Jeffrey Banks at The Applied Research Laboratory at The Pennsylvania State University

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

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Using Vehicle Digital Bus Data for Predicting Failure of Line Haul Trucks, we Jeffrey Banks imageinterviewed Jeffrey Banks, Department Head, Complex Systems Engineering & Monitoring at The Applied Research Laboratory at The Pennsylvania State University. View the Q-and-A below and glimpse what’s in store for the PAW Manufacturing conference.

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

A: The availability of high quality data is the key component of conducting useful predictive analytics.

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

A: Our work focuses on predicting electrical and mechanical component and system failures to reduce unscheduled maintenance and increase operational availability of critical assets.

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

A: We are helping the Department of Defense to implement predictive analytic solutions to enable them to transition to a data driven decision making organization.

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: We are still in the development phase of our work, so we do not have any impact results yet.

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

A: The big surprise is that though we are limited to using low sample rate time series data (~1 sample/second) we are able to demonstrate preliminary predictive results.

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

A: We will show preliminary predictive results for the work that we are doing for component and system failure for military ground vehicles.

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Don't miss Jeffrey’s conference presentation, Using Vehicle Digital Bus Data for Predicting Failure of Line Haul Trucks, at PAW Manufacturing, on Tuesday, June 21, 2016 from 11:20 am to 12:05 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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April 28th 2016

Wise Practitioner – Text Analytics Interview Series: Frederick Guillot at Co-operators General Insurance Company

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

In anticipation of his upcoming conference presentation, Leveraging Hands on Approaches to Identify Actionable Topics in Property Insurance at Text Analytics World Chicago, June 21-22, 2016, we asked Frédérick Guillot, Manager, Research and Innovation at Co-operators Frederick Guillot ImageGeneral Insurance Company, 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: First of all, I’m working in the insurance world. As of now, our main focus had been to identify actionable topics in the free format text fields where our exclusive agents can write notes on everything they want about our clients. For instance, the most useful information we try to identify is the “life events” (newborn, wedding, new car, new home, new job, etc.) of our clients.

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

A: We are at a very early stage of using text analytics in the organization. For this moment, only research prototypes had been delivered. By the end of 2016, we will use text analytics to prioritize which clients to contact first, mostly for cross sell opportunities.

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

A: It is really difficult to articulate for this moment, but early tests in a recent marketing campaign showed an improvement by 15% on our cross sell acceptance ratio compared to a control group.

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

A: Hey, this was something we were really not looking for, but we discovered accidentally that a lot of our clients are annoyed by the fact that we have really strict underwriting rules for oil tanks older than 5 years. We are losing a lot of clients because of this rule, while 5 years of age is not necessarily too old for an oil tank.  

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

A: I will try to demonstrate that hands on efforts can have a great value while doing text analytics, especially in an industry heavily relaying in specialized terminology.  

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Don't miss Frédérick’s conference presentation, Leveraging Hands on Approaches to Identify Actionable Topics in Property Insurance on Wednesday June 22, 2016 at 9:20 to 9:40 am 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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April 25th 2016

Wise Practitioner – Predictive Analytics Interview Series: Alice Chung at Genentech

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference co-presentation, Utilizing Advanced Analytics to Generate Insights at Predictive Analytics World Chicago, June 20-23, 2016, we asked Alice Alice Chung imageChung, Senior Manager at Genentech, 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 are trying to understand correlations between certain attributes that drive behavior changes of our study population. Understanding these relationships will help us address business questions and develop appropriate action plans to work effectively with our customers.

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 allows us to perform scenario planning to answer specific questions for our business. In both decision making and operational activities, we will be able to answer questions precisely and to anticipate outcomes based on market dynamics and business needs.

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

A: We are still working on collecting results and characterizing our predictive output. This is a work in progress for us at the moment.

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

A: It’s all about using and synthesizing data to answer specific questions for decision making. I have heard people hypothetically discuss various scenarios to utilize various predictive models yet there’s no data to use. I have also heard people spending time reviewing/analyzing data without business context or knowing what questions to address. Both approaches, in my mind, don’t yield meaningful insights.

For my team, we understand what and how our data can be used and the level of insights it can bring to support predictive models. The most critical component of our analysis and recommendations is to understand its limitations to answer business questions and identify methods to overcome them.

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

A: We will be discussing high level approach of our experiments and have dialogues with the audience to gather input and feedback based on their experiences!

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Don't miss Alice’s conference co-presentation, Utilizing Advanced Analytics to Generate Insights on Tuesday, June 21, 2016 from 11:45 am to 12:05 pm at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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April 22nd 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Carlos Cunha at Robert Bosch, LLC

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

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Manufacturing Analytics at Scale:  Data Mining and Machine Learning inside Bosch, we interviewed Carlos Cunha, Senior Data Scientist at Robert Bosch, LLC. View the Carlos Cunha imageQ-and-A below to see how Carlos Cunha has incorporated predictive analytics into manufacturing at Robert Bosch, LLC. Also, glimpse what’s in store for the PAW Manufacturing conference.

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

A: One of the biggest challenges is in level of accuracy needed. In e-commerce and social networking applications, 80% accuracy might be very good and the consequences of errors are limited compared to that in manufacturing. Particularly, in manufacturing of safety critical applications, the accuracy requirements are stringent. Consequently, lot more complexity and sophistication goes into the provision of analytic solutions.

In root-cause analysis tasks, the challenge is to go from the correlations identified by the models to actual causation. The final proof can only be obtained by direct testing at the line the top potential factors.  But those tests can be time consuming and expensive for the plants, particularly if the plant has not yet fully embraced data mining methods.

Finally, verification and validation in manufacturing is an open challenge, under active research.

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

A: The target applications of our predictive models include the entire variety of business functions: manufacturing, supply chain and logistics, engineering, and Internet of Things and Services.    

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

A: Predictive analytics delivers value at all verticals of our organization; logistics, engineering, production, demand forecasting, etc. It guides our company, helping to decide what we build, how we build it, how to distribute our products and how and who to sell them to.

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: In some projects we have obtained up to a 65% reduction in scrap and up to 45% reduction in the time required for testing parts.

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

A: The best discoveries are the ones that are completely unexpected. It is very satisfying when our team discovers issues in the production line that are initially dismissed by the plant engineers as implausible based on their knowledge of engineering principles, only to be later confirmed to have been correct due to secondary and non-linear effects that their physical models did not take into consideration.

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

A:  It is much easier to cook a good meal if you have good ingredients. Knowing what data to collect and how to collect it makes all the difference in the world. However, even with incomplete and noisy data, it is possible to extract useful insights, as long as you account for the limitations of the inferences that can be deduced from such a data. With a tough piece of meat you can’t grill a nice steak, but you can still make a great stew.

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Don't miss Carlos’ conference presentation, Manufacturing Analytics at Scale:  Data Mining and Machine Learning inside Bosch, at PAW Manufacturing, on Wednesday, June 22, 2016, from 4:20 to 5:05 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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