Agenda
Predictive Analytics World for Business Las Vegas 2019
June 16-20, 2019 – Caesars Palace, Las Vegas
This page shows the agenda for PAW Business. Click here to view the full 7-track agenda for the five co-located conferences at Mega-PAW (PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World).
TOPICS – The sessions across this two-day, three-track conference are grouped into the following five topics:
Analytics operationalization & management
Track 1
Machine learning methods & advanced topics
Track 2, Day 1
Data strategies & data prep
Track 2, Day 2 (first half)
Predictive model deployment & integration
Track 2, Day 2 (second half)
Cross-industry business applications of machine learning
Track 3
TOPICS – The sessions across this two-day, three-track conference are grouped into the following four topics:
Analytics operationalization & management
Track 1
Machine learning methods & advanced topics
Track 2, Day 1
strategies & data prep
Track 2, Day 2 (first half)
Predictive model deployment & integration Track
2, Day 2 (second half)
Cross-industry business applications of machine learning
Track 3
Session Levels:
Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Pre-Conference Workshops - Sunday, June 16th, 2019
Full-day: 8:30am – 4:30pm
This one day workshop reviews major big data success stories that have transformed businesses and created new markets. Click workshop title above for the fully detailed description.
Two and a half hour afternoon workshop:
This 2.5 hour workshop launches your tenure as a user of R, the well-known open-source platform for data analysis. Click workshop title above for the fully detailed description.
Pre-Conference Workshops - Monday, June 17th, 2019
Full-day: 8:30am – 4:30pm:
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning). Click workshop title above for the fully detailed description.
Full-day: 8:30am – 4:30pm:
Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages. Click workshop title above for the fully detailed description.
Full-day: 8:30am – 4:30pm:
This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting. Click workshop title above for the fully detailed description.
Full-day: 8:30am – 4:30pm:
This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning. Click workshop title above for the fully detailed description.
Day 1 - Tuesday, June 18th, 2019
A veteran applying deep learning at the likes of Apple, Samsung, Bosch, GE, and Stanford, Mohammad Shokoohi-Yekta kicks off Mega-PAW 2019 by addressing these Big Questions about deep learning and where it's headed:
- Late-breaking developments applying deep learning in retail, financial services, healthcare, IoT, and autonomous and semi-autonomous vehicles
- Why time series data is The New Big Data and how deep learning leverages this booming, fundamental source of data
- What's coming next and whether deep learning is destined to replace traditional machine learning methods and render them outdated
In the United States, between 1500 and 3000 infants and children die due to abuse and neglect each year. Children age 0-3 years are at the greatest risk. The children who survive abuse, neglect and chronic adversity in early childhood often suffer a lifetime of well-documented physical, mental, educational, and social health problems. The cost of child maltreatment to American society is estimated at $124 - 585 billion annually.
A distinctive characteristic of the infants and young children most vulnerable to maltreatment is their lack of visibility to the professionals. Indeed, approximately half of infants and children who die from child maltreatment are not known to child protection agencies before their deaths occur.
Early detection and intervention may reduce the severity and frequency of outcomes associated with child maltreatment, including death.
In this talk, Dr. Daley will discuss the work of the nonprofit, Predict-Align-Prevent, which implements geospatial machine learning to predict the location of child maltreatment events, strategic planning to optimize the spatial allocation of prevention resources, and longitudinal measurements of population health and safety metrics to determine the effectiveness of prevention programming. Her goal is to discover the combination of prevention services, supports, and infrastructure that reliably prevents child abuse and neglect.
The research on the state of Big Data and Data Science can be truly alarming. According to a 2019 NewVantage survey, 77% of businesses report that "business adoption” of big data and AI initiatives are a challenge. A 2019 Gartner report showed that 80% of AI projects will “remain alchemy, run by wizards” through 2020. Gartner also said in 2018 that nearly 85% of big data projects fail. With all these reports of failure, how can a business truly gain insights from big data? How can you ensure your investment in data science and predictive analytics will yield a return? Join Dr. Ryohei Fujimaki, CEO and Founder of data science automation leader dotData, to see how Automation is set to change the world of data science and big data. In this keynote session, Dr. Fujimaki will discuss the impact of Artificial Intelligence and Machine Learning on the field of data science automation. Learn about the four pillars of data science automation: Acceleration, Democratization, Augmentation and Operationalization, and how you can leverage these to create impactful data science projects that yield results for your business units and provide measurable value from your data science investment.
Track Sponsored by
Multiple surveys show that operationalizing data science, advanced analytics and AI is a major barrier to data-driven decision-making in organizations. Getting even actionable insight across the "last mile" and into operations is hard. In 2018 McKinsey identified that leaders in advanced analytics not only focused on the last mile, they behaved differently. Specifically, they didn't start with the data, but with the decision-making they hoped to change.
In this session that kicks off the Operationalization track, James Taylor analyzes why the last mile is so hard, shares the research that shows how important to success this last mile is, and outlines a practical approach to working backwards to success with data science.
Track Sponsored by
Customer Lifetime Value (CLV) is considered one of the most useful measures for business to consumer (B2C) companies, and is usually considered more valuable than other measures like conversion rate, average order value, and purchase frequency. If an accurate measure of CLV can be obtained, companies can determine which customers to prioritize with marketing messages and discount offers.
Basic CLV is actually quite easy to compute. But more sophisticated analysts and statisticians use parametric models that take into account purchase frequency, purchase recency, churn risk, and even customer age. These models can provide value estimates 5, 8, and even more than 10 years into the future. However, most retailers, while interested in lifetime value, are especially interested in estimating near-term customer value so they can create effective marketing strategies now.
In this talk, SmarterHQ's founding Chief Data Scientist Dean Abbott describes non-parametric machine learning approaches to calculating customer value for retail that can accommodate additional measurements and features not typically used in CLV models. Model summaries and accuracy metrics for several retail clients will illustrate the effectiveness of this style of model.
Track Sponsored by
At Hopper, we predict airfare from a stream of 30 billion daily prices. In this session, we'll talk shop, covering our process for:
- Personalizing 30 million user conversations through push notifications
- Measuring user travel flexibility and recommending alternative flights and hotels
- Building trust with data
- Open problems
11:20 am - 11:40 am
Black box algorithms. Test data with test results. Predictions with possibilities only. All of these are reminders of analytics teams that have not yet plugged into the “business.” They appear to be making progress, or at least they are busy, but the results are not tangible. They have not yet created value that can be measured and replicated. Although predictive analytics is a “must” for nearly every business today, there are few companies really putting predictive analytics to work for them.
Why are so many organizations developing predictive capabilities, but haven’t put them to use with their sales, marketing or operations people? Are companies really getting value from statistical predictions? If they are, how are they measuring that value and showing it on their bottom line? If not, what are they doing to close the gap between data science and everyday business.
In this session, we’ll evaluate 3 areas that are most neglected and hardest to deal with when putting predictive analytics to work and show you how to get your predictions used.
- Trust. Getting the users to trust the prediction of an algorithm is fraught with biases. “That prediction can’t be right because the data is all wrong.” “I don’t believe that customer will default next month; I am best friends with the CIO and I haven’t heard a word. Trusting the outcomes of the predictions is the first barrier to overcome.
- Teaching. Teaching sales and operations people to use the predictions can be your secret to having successful deployments of systems that use your predictions. Helping users understand the context around the predictions is essential.
- Technology. Automation and self-service are the keys to use of predictive analytics. It must be easy. It must produce results. IT is the it!
Theresa Kushner, partner in Business Data Leadership, comes with over 20 years of experience in deploying predictive analytics at IBM, Cisco, VMware and Dell.
11:45 am - 12:05 pm
The success or failure of analytics and data science initiatives often hinges on whether those on the “front lines” of business actually use and follow them. In this talk the presenter will share ideas he has learned over the years that help maximize the chances of successful analytics deployment.
Marketing Predictive Models have seen significant growth in deployments over the past few years with many companies rolling them out for retailers. Marketing data provides many good examples of large robust datasets with clear target variables. It is a common step in model building to do dimensional reduction or variable selection of input fields in order to improve the quality of the models. At SmarterHQ, we have multiple clients with these models in production. Typically these have 100’s of input fields that have overlapping predictive power. To reduce this overlap, many different methods can be deployed including deviation limits, correlation thresholds, stepwise regression, etc. In this talk, we will discuss methods of input variable field selection and its impact on model quality on production data.
Twitter has amazing and unique content that is generated at an enormous velocity internationally. A constant challenge is how to find the relevant content for users so that they can engage in the conversation. Approaches span collaborative filtering and content based recommendation systems for different use cases. This talk gives insight into unique recommendation system challenges at Twitter's scale and what makes this a fun and challenging task.
As organizations invest more in predictive analytics, machine learning and AI, they are seeking proven ways to maximize their return on this investment. Many find they are lagging behind in capturing the full value of ML and AI because they can't embed potentially game-changing algorithms into their front line systems and workflows. Industry research has identified that these laggards under-invest in operationalizing their analytics and fail to focus their ML investments on the decision-making that matters most to their business strategy.In this session, James Taylor from Decision Management Solutions and Nathan Patrick Taylor from Datarobot, will discuss how leading organizations are successfully driving ML and AI algorithms into frontline systems and workflows. This session will walk through proven techniques for developing decision understanding to be clear what you need your algorithms to do; show how automated ML delivers powerful algorithms that can be rapidly deployed and continuously updated; and then show how you can get your models over the last mile by combining your algorithms with rules-based guiderails and constraints.Join us to see how you can deliver business value from analytics and turn machine learning into business learning.
Businesses are continuing to grow their investments in predictive analytics, ML and AI to enable faster and more accurate decision-making by line of business users. Often, however, the ROI of these investments comes under scrutiny as organizations struggle with building the best possible algorithms and deploying them in a time-to-market manner.
In this session, Aaron Cheng, VP of Data Science from dotData will discuss and demonstrate an innovation in data science automation using AI-powered feature engineering and automated machine learning. You will see a hands-on demonstration of a new platform that is integrated with PySpark on Jupyter Notebook that radically simplifies the end-to-end data science process through the use of a single line of code. This innovation creates incredible opportunities to accelerate and democratize data science in the enterprise, driving the highest value and providing the ROI modern businesses need to justify their investment in Predictive Analytics.
How many .edu addresses are in your inbox right now? As organizations pursue digital transformation strategies, challenges related to finding and retaining analytical talent, objectively assessing the relevance of new, and emerging technology and engaging in deep and meaningful innovation with eventual payback, are common to all sectors of the economy. Deep, collaborative partnerships with universities can help mitigate many of these challenges. Dr. Camm is an associate dean who oversees two masters programs in analytics at the Wake Forest University School of Business and is also leading the creation of a new Center for Analytics Impact at Wake Forest. Throughout his 35 years in academia, Professor Camm has always focused on real-world problems and has actively engaged with companies including among others, Procter and Gamble, Owens Corning, GE, Tyco, Ace Hardware, Boar's Head, Brooks Running Shoes and Kroger. He will discuss the ways that organizations should be thinking about working with universities, but typically don’t – including research, innovation, "externships," training options, recruitment, and other strategic relationships. After this session, you will never look at universities the same way again.
Predictive modeling using machine learning techniques is transforming every aspect of modern business. Traditional approaches to machine learning is a time-consuming, resource-intensive and highly error-prone process. Automated machine learning platforms can make the process of building highly accurate predictive models fast and efficient. In this session, we will show how Datarobot can collaborate with data scientists to quickly build hundreds of highly accurate predictive models in a transparent and flexible manner, generate deep insights and deliver immediate value to business with easy deployment options.
In a world where demand outpaces supply, finding and keeping analytics talent has become a real dilemma. Identifying the right mix of business skills and analytics skills can feel like an impossible search. With so many people looking for strong talent, it often becomes difficult to compete. How do you attract the right skills to your team to ensure a strong analytics capability? What types of levels, roles, and titles do you need? What are some of the ways to ensure you retain your analytics talent? This session will discuss different compositions of successful analytics teams, as well as titles, career paths, and tips to win at the salary game.
Today, with always more data at their fingertips, Machine Learning experts seem to have no shortage of opportunities to create always better models. Over and over again, research has proven that both the volume and quality of the training data is what differentiates good models from the highest performing ones.
But with an ever-increasing volume of data, and with the constant rise of data-greedy algorithms such as Deep Neural Networks, it is becoming challenging for data scientists to get the volume of labels they need at the speed they need, regardless of their budgetary and time constraints. To address this “Big Data labeling crisis”, most data labeling companies offer solutions based on semi-automation, where a machine learning algorithm predicts labels before this labeled data is sent to an annotator so that he/she can review the results and validate their accuracy.
Unfortunately, even this approach is not always realistic to implement, for example in the context of some industries, such as Healthcare, where obtaining even a single label can cost thousands of dollars.
There is a radically different approach to this problem which focuses on labeling “smarter” rather than labeling faster. Instead of labeling all of the data, it is usually possible to reach the same model accuracy by labeling just a fraction of the data, as long as the most informational rows are labeled. Active Learning allows data scientists to train their models and to build and label training sets simultaneously in order to guarantee the best results with the minimum number of labels. In this talk, I will cover both the promises and challenges of Active Learning, and explain why, all in one, Active Learning is a very promising approach to many industry problems.
Internally at SWA, predictive scores are delivered to the sales team via documents known as “Quick Start Guides.” The point of these guides is to take an analytics example and repeat it across different hypotheses about the business, 40 of them.
One example of this is a set of models we built to predict the trajectory of YoY growth for individual accounts to see if they will continue with the same YoY growth or go another direction.
While that information on it's own is a cool prediction it doesn't service the 'boots on the ground', so we built guides that help the users understand why an account has come to their attention and talking points for those influential attributes so the sales force can use them in client conversations.
Right now the projected gain is $15MM in incremental future revenue per year - just by focusing on educating the frontline sales force.
3:55 pm - 4:15 pm
In the government contracting world, executives default to using domain knowledge to answer strategic questions. Industry experts are skeptical about using predictive analytics. But to remain an industry leader, Humana Military needs a broader perspective to diversify and grow its business.
In this talk, hear about how one executive's curiosity about analytics led to a great partnership between executives and data scientists. Through predictive analytics, we discovered new federal opportunities, uncovered what it takes to win contracts, scored our chances of win, and discovered potential partnerships. The result: expanding our view of what is possible and co-creating our future together.
4:20 pm - 4:40 pm
Caesars Entertainment is the world's most geographically diversified casino-entertainment company with major revenue streams from restaurants, entertainment, and hotels in addition to gaming. Caesars' VP of Gaming, Data Science and Fraud Analytics will cover some of the predictive analytics questions Caesars faces and approaches used to address these questions. Topics covered include how Caesars is using deep learning to interpret visual data, predicting key marketing characteristics including future spending and profitability, using machine learning for fraud detection, applying predictive analytics to sportsbook decisions, and valuing entertainment's impact on other parts of the business.
Career rewards -- the long-term value of employment reflecting the trajectories of advancement and pay -- can be used strategically to motivate performance and improve retention. Too often, they are neglected by reward practitioners who focus on benchmarking, and are not the result of deliberate design. In this session, we'll use case studies to show how to measure both the strength and impact of career rewards to optimize the career component of total rewards. In addition, we will demonstrate a methodology that quantifies organization shape in a way that permits alignment with pay on an empirical basis. The session will demonstrate how advanced analytics can inform rewards strategy.
Subscription services have seen tremendous adoption and growth. FabFitFun and StitchFix are household names with valuations in the billions. One of the biggest keys to success in this exploding sector is AI-driven personalization. In this session, I’ll cover the most important ways predictive analytics is impacting subscription companies, from onboarding data to world-class recommenders and lots more. I’ll also walk you through how I helped a Fortune 200 subscription company: (1) Reduce the time required to deploy an AI model from months to hours, (2) Increase the team's throughput by more than 3x, and (3) Showcase data science know-how throughout the company (of thousands) .
4:45 pm - 5:05 pm
Pacific Life has made great strides recently in adoption of analytics across the enterprise. This talk will discuss how the organization took talented and separate analytics practices, built a unified vision, accelerated insights and enhanced adoption at all levels. Specific take-aways for the audience will be around driving stakeholder buy-in, building consensus of vision, getting demonstrable value, and tracking iterative wins. Specific frameworks, anecdotes and examples will be used to engage the audience and create actionable best practices.
5:10 pm - 5:30 pm
In the age of machine learning, when business stakeholders demand both high accuracy and transparency in predictive models, practitioners must adapt in terms of how they present findings. Evaluation must be applied at all stages in the machine learning workflow -- from the initial POC through the model deployed in production. Each stage places different demands on the metrics we choose, as well as how we communicate and interpret those metrics. This talk will explore this issue and help both developers and product managers navigate the machine learning evaluation landscape.
4:45 pm - 5:05 pm
We are living at the dawn of big social science. Just like physics has the particle accelerators and astronomy has orbital telescopes, social scientists can now harness big data and machine learning systems of immense complexity and cost to measure and predict what society is up. Dstillery had been building one such system for the last decade. In this session, I'll walk you through how the system evolved from it's roots in programmatic advertising, how we discovered we were at war with fraudulent data, and how we settled on our philosophy that making good decisions on hundreds of billions of individual pieces of data yields the best results, but at the cost of significant infrastructure and system complexity. Finally, we'll talk about how these shiny new systems don't replace traditional social science methodologies such as surveys, but instead supplement and reinforce them.
5:10 pm - 5:30 pm
At Publishers Clearing House, we create – and deploy in real-time – micro-clusters in order to provide our customers the most relevant and curated experience. In this session, you will learn to create and deploy models that lead to higher customer engagement and LTV.
4:45 pm - 5:05 pm
Attribution is about crediting touchpoints in customer interactions with their impact in the sale process, hence the core element of performance marketing. But today, the choice of the model is often driven by subjective belief and guessing, rather than data and analytics. This explains why to date we often find in place relatively basic models, like last-click or last-non-direct. In this session, we will discuss the different models seen in practice, analyze how they perform in different contexts, explore are their core ideas (from statistics, game theory, marketing science and machine learning), and cover their pros & cons. Finally, we will discuss how to turn descriptive attribution into successful predictive analytics.
5:10 pm - 5:30 pm
There’s a new sense of urgency from the C-Suite to capture more value from the company’s data. For many organizations, this means accelerating progress toward machine learning. But what does it take to go faster? And can you skip some of the steps in an otherwise steep learning curve?
Drawing on case studies in banking and utilities, this session will provide insights to:
- Recognize where a project fits in the data science lifecycle
- Avoid predictive analytics projects that waste time and money
- Create an action plan that helps you reap the benefits of machine learning
Day 2 - Wednesday, June 19th, 2019
Wait a minute! Comedy at a machine learning conference? Yes, indeed, PAW Business has added Yoram Bauman, PhD, “the world’s first and only stand-up economist,” to the roster. Predictive analytics and economics?
As Earl Wilson famously said, "An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen."
When Yoram said he wanted to be a stand-up economist, his father infamously said, "You can't do that – there's no demand." And yet, Yoram has made a splash on TV and the stage, not to mention pursuing a serious economics career at the same time.
Come experience Yoram's stand-up session, "Knock Knock. Who’s There? A.I."
We’re in a global analytics arms race, where yesterday’s strategic advantage can quickly become tomorrow’s industry standard. To stay competitive, companies must continue to invest and evolve at an ever increasing rate.
In this keynote session, Disney Sr. Vice President of Revenue Management and Analytics, Mark Shafer, will discuss his 30-year rags to riches analytical journey, including lessons learned from being on the receiving end of analytics at People Express Airlines to building a science-based analytical team at The Walt Disney Company.
During his 23 years at Disney, Mark led an analytical transformation, starting by implementing Walt Disney World's first resort revenue management model to currently leading an Internal consulting team of more than 150+ employees responsible for supporting analytics across The Walt Disney Company, including Parks and Resorts, Media Networks (ABC, ESPN, Disney Channel, A&E Networks etc.), Studio Entertainment (The Walt Disney Studios, Disney Theatrical).
Leave with deep insights and practical advice on how to steer a successful analytics journey at your company.
70% of digital transformation initiatives are not reaching their intended goals; that translates to $900 billion lost last year alone. The gap between generated insight and action within the last mile is where many businesses get stuck. diwo (Data In, Wisdom Out) was engineered from the ground up to tackle the decision-making process directly, beginning with the last mile in mind while acknowledging a business’s previous trends to visualize their next best move. The new platform’s scalable design seamlessly integrates with existing data, making implementation simple. diwo effortlessly meets business owners where they are, optimizing the decisions they need to make today
Track Sponsored by
10:05 am - 10:25 am
Concerns are constantly being raised about what data is appropriate to collect and how (or if) it should be analyzed. There are many ethical, privacy, and legal issues to consider and no clear standards exist in many cases as to is fair and what is foul. This means that organizations must consider their own principles and risk tolerance in order to implement the right policies. This talk will cover a range of ethical, privacy, and legal issues that surround analytics today. It will frame big questions to consider while providing some of the tradeoffs and ambiguities that must be addressed.
10:30 am - 10:50 am
From predicting which candidates will make great employees and which employees are likely to leave the organization, to forecasting diversity trends and achieving pay equity, employers are increasingly turning to data science to help streamline their employment processes. Despite great promise, using data science in workplace management can expose employers to a crippling degree of legal risk and potential liability, if the relevant legal and ethical issues are not carefully considered. Join us for this engaging workshop as a data scientist and a lawyer from preeminent workplace law firm Jackson Lewis demonstrate how employers can unlock the full potential of leveraging data science to manage the workplace and avoid the unintended consequences of doing so.
Track Sponsored by
10:05 am - 10:25 am
As the global volume of data increases, the challenge of monetizing data is only growing. In fact, data is projected to increase ten-fold by 2025, and 25% will be real-time in nature, requiring sophisticated systems and processes to capture and utilize effectively. One of the most common business questions overheard at companies is how to leverage the value of “dead data.” Data monetization is “the collection and packaging of data (or data insights) for delivering value-added services or creating revenue-generating products”. As the term “value” suggests, data monetization goes beyond just selling or transferring data assets. Instead, the best data monetization practices include both direct strategies and indirect strategies. An indirect strategy may involve using data to improve customer experience, drive cross-selling, or improve performance, and a direct strategy may involve creating new sources of revenue with outside partners.
As the volume of data explodes, companies are finding creative ways to exploit this information. During this discussion, Lawrence will talk through simple steps to start leveraging the value of your data, with a specific focus on analytics initiatives.
10:30 am - 10:50 am
This presentation provides insights on how to optimize marketing campaigns by predicting the responses. The original idea was implemented for a large insurance company’s marketing campaign. We modified and perfected the idea, iterated and perfected it for the internal marketing lead generation campaigns. In this case, we gained access to customers’ attributes from a 3rd party data provider and how responders’ responded to previous marketing campaigns. The attributes include: customer age, professions, preferred contact types, months, past campaigns, etc., the target variable was if the customer responded to a previous campaign and purchased an item. We developed multiple machine learning models such as Ensemble, Gradient Boost, etc. We selected the best model with the highest accuracy and finally created the appropriate label for the response. The process allowed us to gain access to a precise marketing list for the campaigns, improving the performance response 25%-30% from the previous campaigns.
Track Sponsored by
At Overstock.com, lack of data has never been an issue. We know everything from the color you search most, to which room you'll redesign next. We can see individuals transition from furnishing their first flat to building their dream home, but processing this data requires some serious firepower. It has fueled our focus on delivering real-time personalization through the unification of data and AI.
Tune in as Chris Robison and Ramsey Kail takes you through martech innovations in building a successful marketing technology infrastructure for instantaneous individualized marketing experiences.
11:20 am - 11:40 am
The age of extending consulting services to help firms find analytical insights in their data is coming to a close. As businesses and institutions become more savvy in mining their own data, the traditional insights generated from consulting services (both internal and external) is moving to a new paradigm - data science products. This talk explores this shift in the industry and what it means for analytics and data science professionals, including how rapid advances in machine learning and artificial intelligence technologies are necessitating changes in how we think about project management, professional services, and analytics delivery models.
11:45 am - 12:05 pm
Innovative and impactful data science work happens when there is a mix of talented data science professionals, challenging business problems and (most importantly) data. In order to build data science solutions at scale however, the data fueling the analytical work must be clean and easily accessible to the advanced algorithms that will be leveraging it. This presentation will cover how the critical tasks of data acquisition, cleaning, storage and pipeline development must be considered when designing and operationalizing large scale data science solutions.
One of the biggest challenges in corporations is the training of new data scientists to build the most predictive models possible with a given data set and modeling algorithm. Following the approach he's developed teaching this critical topic area after more than 20 years of industry practice, Bob Nisbet will demonstrate the effectiveness in preliminary models of using a progressive series of common data preparation steps -- each on the same data set (KDD-Cup 1998 data set) -- including:
- Filling of missing values
- Derivation of "dummy variables"
- Feature selection
- Deriving custom variables, based on business insights, which become powerful predictors
- Showing how to incorporate time-series data as predictors of system response with a given prediction horizon
- Showing how different data conditioning operations (e.g. balancing and standardization) can generate very different predictive outcomes
11:20 am - 11:40 am
In the event industry, use of machine learning is not commonplace. This talk is on how UBM/Informa uses automated machine learning (AML) technology to improve their sales and marketing processes. This includes application areas such as identifying the most suitable marketing plan to maximize ROI, and forecasting the number of event pre-registrants. We employed an AML platform employed to build and deploy accurate machine learning models quickly. Informa is a leading business intelligence, academic publishing, knowledge and events business.
11:45 am - 12:05 pm
Every year, corporations spend more than $250B on litigation in the US. The critical decisions - whether to litigate or settle or where to file suit - are often made the same way they were 100 years ago. To gain insight that companies could use to make informed decisions on legal proceedings, we built a predictive analytics engine. The approach, combining minimal viable prediction with data from thousands of patent appeal cases over 10 years, was developed to predict outcomes in future patent appeal cases. We think of it like Moneyball, but for a market 20x the size of Majors.
Are you curious about how companies address the gaps in their employees’ data and analytics skills? As the Corporate Training lead at Metis, I’ve worked with a wide range of organizations – from blue chip financial services to boutique tech startups – to develop training programs that help build the competencies needed to successfully apply data science that leads to growth, innovation, and better decision making. In this talk, I’ll share some of these real-world scenarios and answer your questions about the ways training can help your company achieve its goals. Participants will learn:
Which data science and analytics skills are most in demand
How certain skills are evolving to meet market demand
How companies use training to solve common problems and achieve strategic goals
The core Bayesian idea, when learning from data, is to inject information — however slight — from outside the data. In real-world applications, meta-information is clearly needed — such as domain knowledge about the problem being addressed, what to optimize, what variables mean, their valid ranges, etc. But even when estimating basic features (such as rates of rare events), even vague prior information can be very valuable. This key idea has been re-discovered in many fields, from the James-Stein estimator in mathematics and Ridge or Lasso Regression in machine learning, to Shrinkage in bio-statistics and “Optimal Brain Surgery” in neural networks. It’s so effective — as I’ll illustrate for a simple technique useful for wide data, such as in text mining — that the Bayesian tribe has grown from being the oppressed minority to where we just may all be Bayesians now.
Altair Knowledge Works enables individuals and organizations to incorporate more data, unite more minds and engender more trust in analytics and data science. This solution helps organizations get more from internal, external, and enterprise-wide sources of data. Knowledge Works makes more data usable, maximizing the breadth, integrity, value, and insight from your analytics, no matter the origin, format or narrative of the data. Eliminate self-service siloes, duplications, and versioning errors that create dubious analytics. Knowledge Works is a secure, unified data management platform that ensures data integrity, user lineage, and control. The result is greater confidence, bolder insights, and smarter outcomes. It’s a platform for teams with different skill sets enabling them to combine heterogeneous strengths to collaborate on the machine learning, predictive models, and automated decision making with their peers with precision, efficiency and agility. From data wrangling to intelligence, creation of models and model results visualization.
The companies getting the most value from advanced analytics spend much more of their time and money embedding analytics into their core workflows than others. The most successful, in fact, spend more than half their analytics budget not to build analytics, but to deploy and operationalize it. Companies that don’t complete this last mile, those that stop once they have completed the core analytics, see their analytic investments go to waste. Join this expert panel to hear what you can do to make sure you can embed analytics in your front line and maximize the return on your analytics investment.
3:30 pm - 3:50 pm
Every company will live and die by the decisions they make, but none more than High Growth Start-ups. While start-ups are in high growth mode they have to make quick, meaningful decisions that have impact today and to ensure success they have to be insightful data informed decisions. This talk discusses the process of enabling everyone in a company with the ability and access to analytics. Discussing how you get non-technical users engaging with data and at the same time getting your technical data folks well versed in understanding the business beyond the numbers. With this data informed ecosystem any company can make efficient, informed decisions to drive the business.
3:55 pm - 4:15 pm
Analytics has become extremely valuable as it enables businesses to analyze their data and drive data driven decisions by uncovering insights and predicting outcomes. In this talk, I will share my personal story on how to hire, build and maintain world-class analytics teams.
3:30 pm - 3:50 pm
Preeminent consultant, author and instructor Dean Abbott, along with Rexer Analytics president Karl Rexer, field questions from an audience of predictive analytics practitioners about their work, best practices, and other tips and pointers.
3:55 pm - 4:15 pm
Today's organizations have billions of dollars riding on the accuracy and performance integrity of analytical models. With model performance becoming a strategic enabler and a potential source of liability, organizations need to manage the risks associated with analytics.
To manage these risks effectively and move beyond simple financial model or spreadsheet auditing, organizations need a system of controls around analytic model development. These analytics controls provide checks and balances around model selection, validation, implementation, and maintenance.
British Prime Minister Benjamin Disraeli once said, "There are three kinds of lies: lies, damned lies, and statistics." Hollywood is gradually coming around to data-driven decision-making, but some skepticism towards quantitative analysis still lingers on. This presentation will provide an overview on movie-related metadata and how data silos are starting to break down at studios. Additionally, AI/machine learning entertainment industry examples will be shared to show how an improving analytics culture is providing actionable insights to: (1) mitigate risk when green-lighting movies, (2) improve box office predictions by building better statistical models, and (3) drive profits with targeted marketing campaigns.
4:20 pm - 4:40 pm
In the future, there will not be a shortage of doctors, lawyers, teachers, and accountants, there will be a shortage of people in those fields that can speak to technology. From cloud computing to mobile and social media, there is an explosion of data from technology and there is value trapped in siloed organizations where only a hand full of specialized people are empowered with the necessary skills to realize the full potential of data. The solution is to use customized and compelling, case studies to foster a practical understanding of data analytics. This talk will provide practical steps on how to build data science skills across different functions and disciplines in your organization.
4:45 pm - 5:05 pm
Success in a data-driven world means empowering teams with science to improve decision making through confident, replicable and trainable programs that can engage an entire organization. Analytics teams that use a scientific approach to answer business questions will accelerate actionable insights and improve user experiences.
Peter and Martin will discuss their experience driving value in organizations including Lyft, Citrix, Alibaba and Bell where data science methods for growth and insights are at the forefront of the business. Data science is a team sport, the people in the business closest to the data often are in a position to know it best. Fostering an analytic mindset throughout the organization and training teams in a scientific approach to attack the problems they encounter will produce a needed competitive advantage.
Gain speed and agility in modeling solutions to the questions in your organization for a deeper understanding of the business landscape.
4:20 pm - 4:40 pm
There is a lot of information and best practices available so data scientists can build analytic models, but much less about how analytic models can best be integrated into a company's products, services or operations, which we call analytic operations. We describe three frameworks so that a company or organization can improve its analytic operations and explain the frameworks using case studies.
4:45 pm - 5:05 pm
Many organizations utilize predictive models to make decisions but what happens when those models fail to deliver, or worse, are totally off? Having had to audit numerous models across diverse industries as an advanced analytics management consultant, Stephen Chen shares personal WTF experiences and distills the perils inherent in predictive modeling which are typically glossed over in data science courses and texts.
Using real world datasets to illustrate these issues, this session aims to help stakeholders better assess the suitability of models for decision-making, as well as helping practitioners think through their datasets and processes to build more robust models.
The restaurant industry in America is closing on $800b in annual revenue. We have more than a million locations, and we employ more than 14.7m employees*. But for all of that, 9 out of 10 of our managers started at the entry level. Ex-dishwashers, busboys, and hosts, now helping us to run an $800b a year business.
Post-Conference Workshops - Thursday, June 20th, 2019
Full-day: 8:30am – 4:30pm:
This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models. Click workshop title above for the fully detailed description.
Full-day: 8:30am – 4:30pm:
Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists. Click workshop title above for the fully detailed description.
Full-day: 8:30am – 4:30pm:
During this workshop, you will gain hands-on experience deploying deep learning on Google’s TPUs (Tensor Processing Units) – held the day immediately after the Deep Learning World and Predictive Analytics World two-day conferences. Click workshop title above for the fully detailed description.
