2022 Great Lakes Data & Analytics Summit

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When you receive (or submit) a data request, does it include a defined value? Do these requests truly delineate the customers’ (or your) needs? These fundament aspects of data requests are often overlooked, especially in requests to measure something new, because people do not yet view data as a product with a lifecycle.

As businesses proceed through digital transformations, the demand to measure and evaluate activities increases. Unfortunately, many of these activities have never previously been measured. New measuring and reporting requests often miss important requirements; therefore, it is important to be prepared with knowledge on how to handle them professionally. Complicating matters, many organizations do not have data governance or intake processes in place to guarantee that these types of requests are comprehensive. How can you ensure success when a new measurement request like this comes to you?

This presentation begins by discussing the nature of new measurement requests. Data workers will learn how approach new measurement requests in the absence of an intake process. Data leaders will learn how to make requests more effective and efficient. All data people will learn how to avoid common hazards and what long-term solutions might look like. This presentation also includes plenty of true narratives from my experience navigating a world of expanding data.

Presented by: Chris Bawden, Jackson National Life Insurance

Chris is the Product Owner of the Data Science and Artificial Intelligence teams at Jackson. He was exposed to a wide variety of data early in his career at a few small technology firms in Lansing. For the past 15 years he has worked with data across many business disciplines including sales, marketing, web analytics, customer engagement, user experience, resourcing, process efficiency and more. In 2017, he earned a master’s degree in data science from Indiana University and also has bachelor’s degrees in Economics and Comparative Religion from Western Michigan University. Chris runs a disc golf website and in 2021 published a book on using data to become a better disc golfer.

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This session presents the ground-breaking benefits of AI and ML in automotive technology today, featuring a description and working examples of the top four use cases: Classification, Regression, Optimization, and Neural Networks and Deep Learning. Better understanding the technology will help over common challenges while avoiding potential points of failure. The session also offers best practices to drive the adoption of ML and scale projects across your organization, delivering important keys to success with AI and ML in automotive technology.

Presented by: David Corliss, OnStar Insurance-GM Financial

With a PhD in statistical astrophysics, David J. Corliss is a Data Science leader in the automotive industry with a focus on emerging technology and building high-performing analytic teams. He leads a team of data scientists at OnStar Insurance, part of General Motors Financial.

Corliss serves on the Data User Advisory Committee of the U.S. Bureau of Labor Statistics, chaired the 2022vConference on Statistical Practice from the American Statistical Association (ASA), and is the author of “Stats4Good,” a monthly column for the ASA’s Amstat News.

Dr. Corliss is also the founder of Peace-Work, a volunteer cooperative of statisticians and data scientists applying statistical methods to support community service organizations and data-driven advocacy.

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The modern-day potential for value-driven business strategies utilizing data is unprecedented. For many enterprises, unleashing data and analytics capabilities has become a mission-critical directive to drive business growth and create competitive differentiation. In this session, we will discuss a framework for building a modern data and analytics team, key considerations, and ways to start. Join us as we share our journey to develop a team to focus on the execution of data strategy, data governance, data management and data delivery. Learn what has worked and what challenges we have faced as we put the people, processes, technologies, and policies in place to shift our business culture towards a self-service analytical function.

Presented by Aaron Wilkerson & Kristin Schooley, Learning Care Group

Aaron is the Manager of Data Management at Learning Care Group. LCG is the second largest for-profit early childcare provider in North America. Headquartered in Novi, MI, LCG operates over 1,000 schools across 38 states, under ten unique brands, with 20,000+ employees and a capacity to serve 150,000 children.

Aaron has over 15 years of experience in data & analytics, having worked in various industries. He has specialized in building and driving data strategies that help companies leverage their data assets. He also has experience in building and leading teams to implement data solutions that enable data-driven problem solving. These experiences have provided measurable business value to the companies that have leveraged these solutions.

Kristin Schooley, Learning Care Group

Kristin is the Sr. Manager of Business Intelligence & Data Delivery at Learning Care Group. LCG is the second largest for-profit early childcare provider in North America. Headquartered in Novi, MI, LCG operates over 1,000 schools across 38 states, under ten unique brands, with 20,000+ employees and a capacity to serve 150,000 children.

With over ten years of experience in reporting and analytics, Kristin is passionate about her work to tell the organizations’ story through a unique approach to Business Intelligence. Alongside her team, they collaborate with every business unit to understand their reporting needs and identify how it fits into all of enterprise reporting.

Analytics have become prevalent in professional sports, but is this the case for Olympic sports? Mike Heenan will demonstrate how their team uses analytics to support Canadian Olympic athletes. He will outline the unique public-private partnership between Own the Podium and Canadian Tire that has been in existence since 2015. The story of how this full-time analytics team started will be shared.

Mike will focus on his work with Curling Canada and show the full cycle of working with an Olympic sport. He will speak to the core components of his work: data collection, analytics and visualizations. Further discussion will highlight important insights found in the data, advanced metrics such as win probability, and a review of visualization tools used by analytics staff. The presentation will conclude with a discussion on the future of analytics in curling.

Presented by: Mike Heenan, Canadian Tire

Mike Heenan combines his passion for sports and numbers working in Sports Analytics at Canadian Tire in Oakville, Ontario. He holds a Bachelor’s degree in Math & Statistics from McMaster University. Mike joined Canadian Tire in 2015, and in his current role as a Senior Analyst, he helps various Canadian Olympic teams, including Curling, Short-Track Speed Skating and Alpine Skiing, to maximize their medal potential. He also focuses on the team’s global reporting projects, supporting the creation of podium predictions for all Olympic and Paralympic sports at Tokyo 2020 and Beijing 2022.

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In a utopian world, all data sources would natively integrate to tell the holistic story of a brand which would be concise and easily digestible by the end user. However, this is the real world. Join Chelsea Howard, Director of Business Intelligence and Category Management for 5-hour ENERGY® as she walks through a business case of creating a streamlined data platform to drive truly actionable insights and grow sales.

Presented by: Chelsea Howard, 5-Hour Energy

Chelsea Howard has spent 14 years driving actionable insights from data in the alcohol and non-alcohol drink space. She has played an integral role in large data migrations, including the launch and development of ERPS, WMS’, and BI Platforms. In her current role as Director of Business Intelligence and Category Management, Chelsea works with a team of accomplished individuals who are working to combine over 20 different data sources into one holistic picture of business for their company, 5-hour ENERGY®. Chelsea has been able to double the size of her team in a year by showing how data can drive sales and efficiencies. Chelsea lives in Columbus, OH and is proud to call the Midwest home, especially as we emerge as a hub for data savvy individuals.

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AI/ML/DL innovation through data discovery & augmentation

• Basics concepts of ML: Supervised and unsupervised techniques, different algorithms like linear regression, logistic regression, SVM, Neural Networks, K-means, KNN, DB Scan, Decision trees etc. and concepts like regularizations, validation, data pre-processing techniques

• Basic concepts of DL: Neural networks like DNN, CNN, RNN and LSTM.

• Tools: Pandas, CuPy, numpy and other encoding techniques

Coupling Recommender Systems with high-performance Data Science

• Recommender systems (RecSys) have become a key component in many online services, such as e-commerce, social media, news service, or online video streaming. However with the growth in importance, the growth in scale of industry datasets, and more sophisticated models, the bar has been raised for computational resources required for recommendation systems.

Presented by: Bill Veenhuis, NVIDIA

Bill Veenhuis, Senior Solutions Architect in the Enterprise Automotive Group at NVIDIA, focusing on integration of NVIDIA technologies for vehicle design, vehicle data analysis, engineering, safety over to self-driving vehicles. Motivated from a history of modeling and simulation of digital based automotive crash analysis in effort to dramatically reduce vehicle accident occurrences. Prior involvements included multi-touch panel software, graphics and animation and high performance computing all leveraging NVIDIA GPU acceleration technologies.

Bill serves on the Steering committee of Michigan Modeling, Simulation, Visualization and Immersive Technologies (MSV&I) in southeast Michigan and an active member in both SAE and AIAA.

Manufacturing remains a critical part of the world’s economic engine, but the roles it plays have shifted dramatically. Manufacturers can now discover new information and identify patterns that enable them to improve processes, increase supply chain efficiency and identify variables that affect production. They understand that data and analytics are required to compete successfully in a data-driven economy, and they are making investments in data integration and management assets to achieve digital transformation and gain a competitive edge.

In this session, you will learn:

• How you can leverage timely data to detect and respond to changes in, and disruptions to, the manufacturing process

• The role of technology enablers in handling large amounts of data at scale, speed and accuracy

• How to overcome systemic challenges in moving toward a data-centric culture

Presented by: Rajeev Kalamdani, Ford & Poonam Gulati, Informatica

Rajeev is a Data Science/Analytics Manager with experience in deploying AI/ML products in a global enterprise within the Manufacturing domain.

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DTE Energy chose Neudesic to migrate legacy Hadoop workloads and accelerate their move to Microsoft Azure and Azure Data Services when its existing data platform failed to meet expectations for actionable insights. Now it’s making big gains in energy security, customer satisfaction and operational growth. In this sessions, learn more about DTE Energy’s journey in deploying a next generation data platform.

Presented by Uma Poduturu, DTE Energy, David Bess, Neudesic & Rocky Regan, Databricks

Uma is responsible for the management of DTE Energy’s Enterprise Data & Analytics (EDA) Solution Design and Delivery for Customer Service Portfolio. She is responsible for EDA’s Architecture, Data Engineering, and Operation functions. Additional responsibilities include implementing a Data Governance program, creating Technology Platform Plans strategies, and road maps for the expansion of DTE’s Big Data Platform. Uma has close to 20 years of industry experience leading and implementing large-scale Reporting and Data Analytic programs with key focus around enterprise architecture, business transformation and solution delivery.

Presented by: David Bess, Neudesic

David is the Director of Solutions - Utilities at Neudesic and his professional career has included a diverse set of leadership roles spanning industry and consulting, and leading initiatives in digital transformation, cloud modernization, data and analytics, and consulting practice leadership (vertical and horizontal). Earlier in his career, David was a contributor to Apache projects including Hadoop, Lucene, and Spark.

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Organizations leading in digital transformation have taken a completely different approach to how they manage data access, analytics platforms, and data-driven decision making. But true analytic transformation can’t happen for an organization just in a few select pockets or silos. Unless everyone is ready to go on the journey together, the dream to become data-driven remains just that—a dream. That’s why the Alteryx platform is designed to be approachable and to meet every user wherever they are in terms of analytic and technical sophistication.

In this session, learn more about how the "Analytics for All" approach allows everyone - not matter your job role, skill level, department, or industry - to participate in the analytics process.

Presented by Ryan French, Alteryx

Currently serving as sales leader for the Midwest enterprise sales team at Alteryx, Ryan has spent the last 15 years focused on applying his engineering and sales leadership experience to solving customer problems as a global account executive (technology), technical sales leader (Fortune 200) and automotive engineer.

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Automated machine learning (AutoML) is changing the way organizations approach machine learning and data science. Minnesota companies, regardless of size and industry have the ability to become AI-driven enterprises with automated machine learning and no code.

During this session, Kyle Jourdan will dive into AI, and machine learning:

• Common automated machine learning use cases

• How automated machine learning enables more employees to take part in AI initiatives while making existing data science teams more productive

• Concrete action items to evaluate your organization’s readiness

Presented by: Kyle Jourdan, Qlik

With roots starting in the finance and accounting world, Kyle Jourdan found himself called back to his true passion: using structured languages to create solutions that revolutionize the way we do business. Today, he is at the forefront of improving the way that data and business analysts, developers, and engineers adopt AI and machine learning in their day-to-day business practices.

Kyle applies the fundamentals of asking the right question, collecting and preparing the right data, or meaningfully applying the insights to day-to-day operations as he finds himself broadening the scope of machine learning applications every day!

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Data gone wild? Alation can help you tame it with the combination of machine learning and human insight. We'll show you how Alation drives data search and discovery, data governance, data stewardship, analytics, and cloud transformation.

Presented by: Bill Ferkat, Alation

Bill Fekrat is a Sales Engineer with Alation who has spent this career helping organizations realize the value of their investments in people and technology. He has 25+ years of experience with a focus on data management, data governance, and advanced analytics.

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Vertica and Chata's revolutionary "text-to-sql" natural language analytics offering facilitates "In-the-Moment" Decision Intelligence, making data accessible to everyone in the organization, no matter their technical prowess. With Vertica's Machine Learning capabilities powering the backend data-driven workflow, and Chata's low-touch AutoQL democratizing the frontend insights through its natural language Interface, non-technical users can interact with the Vertica database without having to write a single line of code.

This transformative Vertica-Chata natural language analytics solution places the decision-making power in the hands of those who need it, from the executives to management, to individuals ordering stock on the warehouse floor. Employees across all industries can quickly access real-time data without restrictions — enabling them to do their jobs more effectively and make informed strategic decisions. Vertica and Chata’s new natural language analytics offering empowers everyone in the company to get the answers they need with ease, by simply asking a question in their own words.

Presented by: Theresa Melvin, Vertica

Theresa Melvin, J.D., is the Chief Data Scientist for Vertica Americas where she develops strategic AI solutions predicated on an economical amalgamation of commercial software, open technologies, and cloud computing. Ms. Melvin’s AI solution development strives to balance the economics of growth against accelerated innovation.

A Full Stack Data Science Pipeline Developer by trade, Ms. Melvin’s nearly 25 years of High-Tech sector experience has centered around architecting and incubating custom, end-to-end, AI solutions. This niche development work has facilitated the needs of the hyperscale and extreme-scale communities for the past decade.

Businesses today are faced with complex challenges and opportunities that require more flexible and intelligent approaches. To accomplish their goals, organizations are exploring the results of graph algorithms and using predictive features for further analysis, machine learning, or support for AI systems. Learn how global automotive OEMs and other manufacturers are using graph technology and a connected data approach to solve real-world problems including:

• Product Data Management

• Supply Chain Management

• Digital Twin / Software-Defined Vehicle Analytics

• Knowledge Graphs & NLP

• Operations and Process Improvements

Presented by: Mark Quinsland, Neo4j

Mark Quinsland is a Senior Field Engineer for Neo4j and is based in the SF Bay Area. In a prior life, he worked with the U.S., Asian, and European auto OEMs to develop global standards for the data used to develop and test vehicles. He has continued working behind the scenes with several OEMs and Neo4j partners to apply Neo4j to manufacturing use cases.

From accelerating time-to-value from integrations, to dramatically reducing effort, to automating enterprise business processes, SnapLogic delivers the business value you need to succeed. Join senior solutions engineer, Hugh Kaplan, to learn how Snaplogic’s unique architecture combines a broad set of intelligent data and application integration capabilities that powers enterprise integration and automation for IT and business.

Presented by: Hugh Kaplan, SnapLogic

Hugh has been helping corporations and state governments solve their business challenges using computer technology for over 19 years. Mr. Kaplan started his IT career working for Rockwell International writing applications using Pascal and HP/GL to help build the B1 bomber more efficiently. Mr. Kaplan went to work for Information Builders as a Technical Sales Engineer and found his calling in pre-sales. From Information Builders Hugh went on to work for several other software vendors including Prism Solution, TIBCO, Broadbase, IBM and Informatica providing various solutions such as Business Intelligence, Enterprise Application Integration, Enterprise Data Integration and Big Data across Cloud/On-Prem/Hybrid platforms.

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You invest so much in your data infrastructure – you simply can’t afford to settle for broken pipelines and stale dashboards. At Monte Carlo, we believe in a world where you sleep soundly at night knowing you can trust your data.

In this live product demo, you will see exactly how our product delivers end-to-end data observability across your data pipelines, from ingestion in the warehouse or lake to ETL and analytics.

Not only will you get a ‘look under the hood’ of our product, we’ll share how Monte Carlo can help you detect data problems in minutes, not days!

During this demo, we’ll show how Monte Carlo can help you to:

• Know when data breaks using ML-generated and custom rules

• Conduct root cause analysis on common data quality problems

• Reduce time to detect and resolve data issues with end-to-end lineage

Presented by: Ethan Post, Monte Carlo Data

Ethan Post is a Sales Engineer at Monte Carlo, a data reliability company, where he helps data teams achieve more trustworthy data with end-to-end data observability. Ethan began his career as BI implementation consultant, building end-to-end data solutions for some of the most recognizable brands in the world. Most recently, he led the PreSales Center of Enablement for an analytics startup where he witnessed firsthand the true cost of poor data quality and observability. He received his MS in Information Systems from Indiana University. In his spare time, he loves cooking, woodworking, and spending time with his wife and two young children.

An interactive virtual event to better understand Dataiku’s benefits and capabilities around data prep, autoML, and self-service analytics for analysts and non-coders.

In this workshop, you’ll gain intimate, firsthand experience with Dataiku’s end-to-end data science platform. Dive deep into the Dataiku DSS Design Node to prepare data interactively, analyze it, create insights, and augment your analysis using machine learning without coding.

Why should you attend?

• Beyond showing you the vast capabilities of Dataiku's data science and machine learning platform, this 90 min. workshop will:

• Demonstrate the importance of cross-functional collaboration in data initiatives

• Identify areas in the data pipeline where your organization can make efficiency gains

• Expand your advanced analytics potential

• Solve critical pain points and help your organization become more agile