DATA INNOVATION SUMMIT

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The presentation is meant to share with the audience the experience I had during my data analytics career on the “To Dos” / “Not To Dos” in order to ensure BI and Operations work effectively as one team and projects meet the expectations. I will cover practical examples to demonstrate the effectiveness of certain approaches and the downfall of others.

Questions:

- How to interact with your customer/stakeholder
- The right mindset
- Assess if the environment is adequate for a successful deployment and usage of the BI solution
- Handle the unexpected

Ricardo Rodrigues
Data Analytics Manager
Opel

Studied Telecommunications Engineering with a Master degree on Network Security. When doing IT audits, within the banking industry, realized the relevance of data analytics in supporting business decisions. Using the engineering mindset and analytical skills, decided to change career course and started doing statistical models for Credit and Market Risk and quickly realized this was my passion. Currently within the Automotive industry and supporting all business functions with business intelligence support, came to realize that every data point is like a piece of a puzzle and when you put all pieces together, you see the big picture. Seen as a person that seeks perfection, behaves as a person who is never satisfied with this achievements, dreams in being the person

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Learn how the ALDO Group — a global fashion footwear company with over 2,000 stores in 95+ countries — deployed near real-time mobile analytics. Gain tips from its move from “traditional reporting” to making key business metrics available to those who need them in near real time, giving the organization an “always-current” view of business performance and allowing employees to make better data-informed decisions and react quickly.

Questions:

- The pre-Mobile Solution situation at ALDO, including key processes for accessing information, data freshness, and ease of use, all of which led to this project
- Key factors that contributed to the successful implementation, such as change management and platform interoperability, and how what was supposed to be limited to management quickly grew to include stores, as well
- Specific examples of big wins, including time management, data-informed decisions, access to information, and user adoption that resulted from the project

David Dadoun
Director – Business Intelligence and Data Governance
The Aldo Group

David Dadoun is currently the Aldo Group´s Director of Business Intelligence and Data Governance. Prior to joining the Aldo Group he was the Director of a BI practice for the Keyrus Group. Over the last 15 years David Dadoun has advised a broad number of companies in the selection and implementation of BI tools, strategies, and roadmaps. He has managed BI projects including upgrades and migrations, and multiple BI platform deployments at small, medium, and Fortune 500 companies. David holds a Bachelor of Commerce in International Business from the John Molson School of Business and an executive Masters of Business Administration from ESG Montreal and Paris-Dauphine in France.

Automated Machine Learning is a paradigm that allows both junior and experienced data scientists to increase productivity by automating mundane and repetitive parts of the machine learning workflow. Contrary to popular belief, these tools can be used by experienced practitioners as well as junior data scientists effectively. This talk will survey the landscape of automated machine learning and provide a case study of how it was used at Airbnb.

Questions:

- Find out what Automated Machine Learning is, and if it might be appropriate for you
- Learn how to leverage Automated Machine Learning to increase productivity
- Get an overview of the landscape of Automated Machine Learning tools including open source tools and proprietary tools

Hamel Husain
Senior Data Scientist
Airbnb

Hamel has over a decade of experience building data science teams and products from the ground up. During his tenure as a data scientist, Hamel has built world-class solutions for companies such as: Airbnb, DataRobot, SalesForce, AT&T, and MGM Resorts. Additionally, Hamel has led consulting engagements at for numerous companies in the retail, telecommunications, and restaurant industries at the consulting firms Accenture and AlixPartners.

Hamel's primary area of expertise is machine learning, and he has built a wide range of data products including: image classification, sales forecasting, churn prediction, customer lifetime value models and recommendation systems. In addition to his technical leadership, Hamel has built and led data science teams of up to 50 people..

Hamel currently serves as an advisor to several startups and is actively engaged as an organizer and speaker for the Open Data Science Conference. Furthermore, Hamel regularly volunteers in a Data Science capacity through DataKind for nonprofit organizations and charities. Hamel has attended The Georgia Institute of Technology, The University of Michigan, and Southern Methodist University where he obtained undergraduate and graduate degrees in the areas of mathematics, industrial engineering, computer science, and law. More information about Hamel can be found on his LinkedIn profile.

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Every Enterprise has data and knows there is value is using it somehow. Often the issue is how to leverage the data and once found what do you do with this. Turning findings into actionable items should be the goal.

Questions:

- Being and acting like a startup within an existing Enterprise
- Changing attitudes towards data
- Getting over the “I knew that”
- Beyond the technical to being trusted

Alun Jones
Data Scientist
Konecranes Global Oy

Geographer by education, programmer by trade.Experience in education, retail, manufacturing,distribution and service industries. Worked for Konecranes since 1992 in a number of roles. Joined the Data 2 Knowledge cluster in January 2016 as Data Scientist.

This talk presents a high-level trend of the hottest ongoing projects in Silicon Valley and emphasizes how data science/machine learning can not only save companies millions of dollars, but more importantly how it saves thousands of lives. At the end of this talk, the audience will hopefully respond “data science” to the following question: Who saves more lives, data science or doctors?

Questions:

- What is Data Science all about?
- Malaria and Zika detection by using predictive analytics
- Creating a better life for people with disabilities
- Making it happen: A novel way to make predictions

Mohammad Shokoohi-Yekta
Senior Data Scientist
Apple

Mohammad is currently a Senior Data Scientist at Apple and a Lecturer at Stanford University. Prior to joining Apple, he worked for Samsung, Bosch, General Electric Research and UCLA on predictive modeling projects. He received a PhD in Computer Science from the University of California, Riverside and B.Sc. from University of Tehran. Mohammad is also the author of the book, "Applications of Mining Massive Time Series Data.”

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Having a dynamic enterprise system looks to many organizations as a far-fetched target. Owing to the vast amounts of factors that need to be controlled across the implementation lifecycle; especially during usage and maintenance phases. On the other hand, advanced analytics techniques, machine learning and data mining, have been proofing strong presence through academic as well as industrial arenas through robust classification and prediction. Correspondingly, this session is set out to address a methodological approach that works on tackling the implementation lifecycle challenges by employing advanced analytics techniques in order to detect business process problems, find and recommend a solution to it, and confirm the solution. The objective is to make enterprise systems smarter & self-moderated by reducing the reliance on vendor support replacing it with intelligent built-in support. The session will profile an advanced analytics engine architecture fitted on top of an enterprise system to demonstrate the approach.

Questions:

- Employing deep learning algorithms together with the enabling components on typical analytics architectures have the potential to support post-live enterprise systems lifecycle
- Correspondingly, this would command for the development of Artificially Intelligent agents that works on analyzing enterprise systems data to prevent them from failure
- The advanced analytics engine will enable enterprises to rely less on external support and hence enhance their ability to sustain their core systems

Ahmed Elragal
Associate Professor of Information Systems
Luleå University of Technology

Prof. Ahmed Elragal (PhD, MBA, BSc) is an associate professor of information systems at Luleå University of Technology in Sweden. He has obtained his PhD from the University of Plymouth in the UK in 2001. Prof. Elragal has over fifty research papers and articles published at international conferences and journals, such as the Journal of Big Data, ECIS, AMCIS, and HICSS, etc. He is the winner of the 2010’s international case study competition on “Business Intelligence”, a prestigious international award. He has over 15 years of consulting experience, focused mainly on enterprise systems and business intelligence. He has helped different regional as well as multinationals organizations [including SAP, Teradata, Hyperone & Egypt Census Bureau] in the areas of enterprise systems, business intelligence, data mining, big data, analytics. In 2017, he has contributed to the formulation of Norrbotten’s Big Data Strategy.

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Organisations everywhere look to analytics to help them become/remain competitive in an ever more challenging business landscape. But what good is Business Intelligence when most can’t even trust the data they are using?

Questions:

- What is Data Governance (in the real world)
- How to Market your initiative
- How to create a Data Governance Charter

David Dadoun
Director – Business Intelligence and Data Governance
The Aldo Group

David Dadoun is currently the Aldo Group's Director of Business Intelligence and Data Governance. Prior to joining the Aldo Group he was the Director of a BI practice for the Keyrus Group. Over the last 15 years David Dadoun has advised a broad number of companies in the selection and implementation of BI tools, strategies, and roadmaps. He has managed BI projects including upgrades and migrations, and multiple BI platform deployments at small, medium, and Fortune 500 companies.

David often presents at global BI events regarding the challenges and opportunities managers face when it comes to Business Intelligence and Data Governance. He also teaches Business Intelligence and Analytics at Montreal's prestigious HEC Business School.

David holds a Bachelor of Commerce in International Business from the John Molson School of Business and an executive Masters of Business Administration from ESG Montreal and Paris-Dauphine in France.

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A few years after Google announced that their knowledge graph allowed searching for things, not strings, knowledge graphs have been gaining momentum in the world's leading organisations as a means to integrate, share and exploit data and knowledge that they need in order to stay competitive. In this talk, we discuss both the technical and business/organizational dimensions and challenges of Knowledge Graph initiatives, and share relevant best practices and lessons, derived from building our own knowledge graph about the Labour Market domain.

Questions:

- What is a Knowledge Graph and in what ways can it be used in an organizational setting?
- What are the main dimensions and tasks that both Information Architects and Business Executives need to consider when launching a Knowledge Graph Initiative?
- What are some key challenges related to Knowledge Graph development, exploitation and evolution, and how can they be addressed?

Panos Alexopoulos
Head of Ontology
Textkernel BV

Dr. Panos Alexopoulos is Head of Ontology at Textkernel BV, leading a team responsible for the development, management and evolution of the company's knowledge graph for the Recruiting and Labour Market domain. Born and raised in Athens, Greece, Panos holds a PhD in Data and Knowledge Engineering from National Technical University of Athens, and he has more than 10 years of combined academic and industrial experience in the areas of semantic technologies and knowledge engineering. He has published more than 50 articles at international conferences, journals and books, while the output of his research has been applied in several commercial and industrial projects.

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One challenge often faced by organizations is how to recognize which problems would benefit from machine learning techniques, and which ones would not. In this presentation, I will go over some characteristics that indicate potential machine learning applications, and describe a few examples from my own experience.

Questions:

- Machine learning is often most powerful when there is an existing context of human expertise
- To do machine learning in the future, you must start logging now
- Combining passive and active learning can be more effective than just using the fanciest algorithms

Shani Offen
Machine Learning Lead
Spotify

Shani got her PhD in neuroscience from NYU in 2008, with a focus on computational vision. At Spotify, she leads a machine learning team in the quest to make audio ads interactive, relevant, and measurably effective. This effort spans many areas of ML and AI, from algorithms to infrastructure. Her favorite algorithm is division, she is amused by superstitious pigeons, and she recommends fiction as a good way to expand your priors.

Jim Aldon D'Souza
Research Engineer
TomTom

Jim is a research engineer working with autonomous driving systems at TomTom. His primary interests lie in the perception, localization, and mapping aspects of self-driving technology.

Jim has a master's in electrical engineering from the Technical University of Denmark, and a bachelor's from National Institute of Technology Karnataka, India. His master's thesis addresses the localization issues faced by a planetary rover when navigating in a symmetrical and featureless environment such as the one on the surface of Mars. It was concluded in 2016 at the German Research Center for Artificial Intelligence in Bremen, Germany.

Many organizations lose time when several people are looking at data visualizations and dashboards for changes in their temporal data. Much of this decision making could be machine based. Session shows an open source solution to arrange meetings only when there is something interesting in the data.

Questions:

- Benefits of combining stats and machine learning for time series modeling
- Understanding anomalous behavior, impact estimation and simulation of optimal outcomes

Markus Virtanen
Chief Data Scientist
Valio

Build an AI team in a startup is no big deal (alright, literally), but in a well established enterprise? It could be a civil war…Let’s talk about the challenges and how enterprise could adapt with this new paradigm shift.

Questions:

- The Geocentric World
- The Revolution
- The Interpreter
- The Paradigm Shift

Jesse Chao
Product Head, Data Center Automation
Ericsson AB

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There are many challenges with getting started with deep learning and the latest AI techniques, especially in industrial settings with noisy data, immature tooling, and lacking supporting infrastructure. However, the potential has never been higher than right now and we are seeing an increasing number of companies making use of AI across their product lines and throughout the organization. Peltarion has the single focus of democratization of AI, helping companies transition into an AI-First future.

Questions:

- Why is the potential of AI higher now than ever before?
- Are there any real-world industrial example of what AI can be used for?
- What are the challenges with getting started with AI for your business?
- How should companies prepare and take the first step making use of state-of- the-art techniques?

Luka Crnkovic-Friis
Co-Founder Peltarion
Peltarion

There is not the perfect RPA tool. It depends on the process to be automated. There are processes, which are easier to automate compared to other processes. How Merck Business Services implements RPA.

Questions:

- How to identify the best RPA solution for a process?
- How to identify processes to be automated?

Bjoern Ebeling
Innovation Manager
Merck KGaA

The Digital Platform Team for Merck Business Services combines competencies in user experience, future analytics, cloud integration and digital efficiency. My role is to coordinate the team and to relate business needs with technology.

In today’s data driven world it’s extremely important to make sure that our data infrastructure is fast, efficient and quickly adapts to the changing business needs. At Uber, we use machine learning and artificial intelligence in all aspects of infrastructure life-cycle, starting from capacity planning to design data models and optimizing resource usage. This session will focus on few ways Uber is making Infrastructure smart and dynamic.

Questions:

- It’s not data but the ability to quickly mine meaningful information from data is key to a business’s success
- Machine learning and AI is can be effectively utilized to make infrastructure intelligent

Ritesh Agrawal
Senior Data Scientist
Uber

A leading data scientist for optimizing infrastructure, Ritesh Agrawal at Uber leads the Infrastructure Analytics and Science (IAS) team. The team focuses on scaling data infrastructure for Uber’s growing business needs now and foreseeable in the future. Before Uber, Ritesh specialized in predictive and ranking models at Netflix, AT&T Labs and Yellow Pages where he built scalable machine learning infrastructure with technologies such as Docker, Hadoop, Spark, and more. A Ph.D. scholar from Pennsylvania State University (State College) in Environmental Earth Science, Ritesh’s thesis focused on computational tools and technologies such as concept map ontologies.

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Organizations are collecting more data while they are missing the operating model that allows them turning data into action. I will share this operating model, and what I learned in implementing it.

Questions:

- Generating outcomes from data is not a capex investment – it is a way of working
- Put data, analytics, experts, change management together
- Manage the transformation instead of managing just the data
- Drive it from the business side and not from technology side

Richard Windischhofer
SVP Collaborative Operations
ABB Industrial Automation Division

Richard is running building a dozen of digital hubs that will be the key in ABB’s new digital operating model that combines equipment, control, software, cloud, and data gathering with analytics, optimization, and technical services. The centres will be staffed with OT and IT personnel and will be able to integrate with Industrial customer’s operating models.

Prior to that, Richard led the digital transformation of ABB Marine and its operating model, which is now one of the digital leaders in its field. He is based in Helsinki with his wife and three children and in his free time he is operating a new micro cidery with his friends.

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Smart Flows use measurements from over 15000 district heating meters to create real-time production planning forecasts.

Questions:

- How to derive as much information as possible from a very limited set of input and build a flexible, yet solid architecture
- How to use user insights in targeting and realtime bidding and have a direct impact on user level ROI
- How to use user insights in personalization and recommendations even on users we have never seen before

Henrik Wickström
Project Manager
Mälarenergi AB

Henrik Wickström is the sports guy! He is also that cheerful, inspiring and one who creates a good climate around him. But Henrik Wickström is also innovative! He has gone from being a statistical mathematician and economist to today creating state-of- the-art research projects and innovations.

Henrik believes that AI and the digitalisation that is happening right now is almost as exciting as the penalty shootout between Sweden and Romania in football world cup 1994.

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While other industries are just embarking on the journey towards autonomous vehicles Sandvik has been delivering 30t autonomous trucks and loaders moving material in extreme conditions 3000m underground. Here you’ll learn about key success factors and hard numbers on customer and business value.

Questions:

- Identify specific customer KPIs to move
- Don’t focus too much on technology – build capacity in front line
- Be specific and clear on terminology and definitions internally
- Balance your innovation portfolio – don’t focus only on disruption (most companies are not Uber)

Jakob Rutqvist
Business Development Manager
Sandvik

Sweden and after studies in economics at Harvard University went on to work as a lobbyist. After lobbying policymakers to regulate business, Jakob decided to switch tables and joined Accenture Strategy as an advisor to senior business leaders. Six years later left consulting for business development at Sandvik. Now deeply involved with the digitalization of the global mining and construction industries. Jakob is also the author of an Amazon best seller about business models and technologies for a circular economy (Waste to Wealth).

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