Business Intelligence and the Capitalization of Data
Today’s disruption-filled digitized economy has transformed the Information Age, where data is the new currency. This has caused a tremendous upheaval in virtually all aspects of modern business organizations. New incubation areas are sprouting up overnight and quickly overtaking industry giants, creating a new business environment centered around an Idea Economy; where ideas are transformed into a digital commodity, and those fastest to execution are the winners. Information-rich content providers and delivery organizations have made data the most valued asset for an organization – and those companies that can harvest the greatest revenue from their data stockpiles can be the most successful in the long term.
This brings a host of challenging issues to the Intelligence professional around data acquisition, analytics, and insights generation and execution. The BI professional today has to transform from a batch-based analytics perspective to one that measures in real time and is structured within nimble and flexible organization that can effectively compete in anticipation of ongoing business disruptions.
McDonald's Journey to the Cloud
Interested in knowing more about how McD move their global data and analytics workloads from an on-premise environment to cloud? Please join us to learn more on the technology selection, challenges, lessons learned and journey ahead for McDonald's Global Data and Analytics platform in the cloud.
Power to the People - Search and AI Driven Analytics
2 billion people use search everyday to get the instant answers they need to book flights, trade stocks, apply for jobs and more. But when we come into work, we have to wait days or even weeks to get the data we need. ThoughtSpot is disrupting the business intelligence industry with search-driven analytics built for enterprise scale. Come hear from how some of the most successful Fortune 500 companies are giving front-line employees the ability to build their own reports and dashboards in seconds, with zero training.
Networking Coffee Break
Design Thinking in an Agile World
One of the top frameworks for managing software projects is agile scrum. One of the top frameworks for designing applications is Design Thinking. This seminar reviews how to adapt these frameworks for analytics and business intelligence deliverables. We will follow the PMI (Project Management Institute) framework of ITTO for each phase of Caterpillar’s Viz Review Process, which combines the best of Design Thinking with Agile Scrum to create analytics products quickly and efficiently.
How to Get Users Engaged with Business Intelligence
Organizations are drowning in data. BI teams are busily creating dashboards and reports for their business users, but most of this content goes unused. Users are overwhelmed with the amount of content they have access to, and with only a few minutes a day to focus on data, they don’t know what to take action on. Push Intelligence enables BI teams to engage their users with the right content at the right time by 1) Organizing content into a single portal 2) enriching data by uncovering exceptions and anomalies 3) distributing and alerting users to what’s important.
Ferrara Candy Company’s Sweet BI Experience, a Win for Everyone!
Ferrara Candy Company’s journey resulted with providing our business users full visibility and critical insight. The journey began by transforming the organization by retooling internal resources, establishing a corporate culture synergizing the user and developer communities, simplifying the BI governance, executing end to end solutions effectively, and creating smart partnerships with outsourcers. Here is the story of Team Awesome and how it impacted so many with such a sweet experience.
Integrating Data, Analytics, and Technology to Make Good Decisions
Decisions are everywhere. We make an estimated 70 conscious decisions every day. Have you ever wondered how businesses make decisions and how data, analytics, and technology can improve them? During this talk, I will share the impact you can make to the business by building data products and services that foundationally improve good decision-making.
Connecting IoT & BI to Transform Your Business
Organizations now believe that integrating IoT into their systems will eliminate inefficiencies in their business models and increase visibility into core business functions, while powering new revenue streams as well. But how valuable is this newfound network of interconnected objects, that can collect and exchange data using embedded sensors, without actionable insights?
This session will highlight how businesses are acting on the data generated by IoT to transform their business models, power modern decision-making and offer better insights than ever before.
Enterprise BI - Should it Die?
New, easy to use “self – service” data visualization desktop tools (Tableau, Power BI) have changed the BI landscape dramatically and irreversibly. Predictive analytics, dashboards and advanced graphs can be now produced in minutes. It seems, traditional enterprise BI model (SAP Business Objects, IBM Cognos) has aged and got left behind, so should we just kill it and switch everyone to the new tools? Are they really the future of BI? We will discuss both models and attempt to answer the question above.
Networking Coffee Break
What’s your Data Strategy? Data Analytics or Data Visualizations?
Having a data-management function is a start, but neither can be fully effective in the absence of a coherent strategy for organizing, governing, analyzing, and deploying an organization’s information assets. Without such strategic management many companies struggle to protect and leverage their data. We will walk through a new framework for building a robust data strategy that can be applied across industries and levels of data maturity. The strategy enables superior data management and analytics—essential capabilities that support managerial decision making and ultimately enhance financial performance.
Deep BI & Learning Insights Through Different Case Studies
In this talk, we will cover 4 case studies: Unlocking insights by deep learning from Brands, Face Images, Executive Posts and Medical Records. First case is extracting the impression strategies from corporate executives’ posts by using supervised machine learning. Second case is detecting and monitoring brand personality of the most popular brands from various industries. Third case is identifying tobacco users from selfie pictures; usage eon health/life insurance applications. And finally we do data mining to improve health outcomes, reduce information withdrawal and mitigate risk for providers by extracted incidental findings from medical records.
Find the Leaks in Your Profitability Fix Them
Chair's closing remarks
Networking Drinks Reception
Registration & Breakfast
DIY BI: Key Ingredients to Make it Work
We are in a new world of BI delivery. With so many mergers, acquisitions, and changing business needs, the role of traditional reporting has transformed. Business leaders have taken charge of analytics and have dedicated teams or build teams ‘On demand’ to support their rapidly changing needs.
With a gamut of new Self-Service tools in the market, it helps the BI & Analytics teams to efficiently guide/support the business and stay relevant by helping business users generate powerful insights, and good decision making. In this session I will be sharing the journey of how we engaged and empowered our users to develop Self-Service Analytics for quick ROI, creating a Win-Win for the BI Analytics Team and Business.
We will also see live examples of how the different departments and teams have leveraged DIY-BI to generate powerful insights via simple interactive stories and visualizations.
Fireside Chat: Building Agile Business Intelligence from the Ground Up
BI Transformation needs the agility from the ground up. It needs a well-developed business strategy and clearly defined objectives which use proven BI tools, appropriate data, and inputs to bring together the core business processes and IT functions to create a transformational BI framework. The Transformation to Agile BI requires the amalgamation of Organization, Process, Change Management, Data, Applications, and Infrastructure. The Agile BI framework should enable the business to improve its performance through information and should be able to adapt business intelligence quickly in response to changes in the business environment or operational changes on a daily basis.
Creating Corporate Cultures of Data and Analytics Readiness
Digital Transformation is no longer a choice – it is a prerequisite for competitiveness and growth. Executives across the board believe that big data and AI collectively have the highest potential for disruption but field skills shortages in data literacy throughout the ranks as obstacles to rapid and successful adoption. Consequently, new ways of learning on and off the job are being explored for the workforce to keep pace with the rapid rate of technological change. This talk will discuss digital and data literacy as fundamental 21st century skills and highlight a few approaches to develop effective corporate data cultures through data literacy in support of Digital Transformation.
Networking Coffee Break
Using Big Data: Insights to Action
The importance of Big Data is obvious. However, leveraging Big Data to draw insights and drive business strategies requires resources, technology, and vision.
As business intelligence professionals, how can you enable your employers or customers to make sense of big data and drive action? Credit data is Big Data. There are more than 250 million credit-active consumers in the United States, which translates to more than 450 billion records of data. In this session, we will showcase the importance of Big Data to drive business strategy by illustrating how FinTechs leverage market intelligence from credit data. We will highlight how insights driven from Big Data can position a business to drive smarter decisions – and to better serve consumers.
Understanding Data Science Development and Recruiting
Data science is currently one of the most sought-after fields for professional recruiting and yet one of the hardest to define. What is data science, and how can businesses use it most effectively? The fact is, nearly no one agrees on what data science is – the field is still too new and too diverse – we must first make some sense of the field as a whole. I will discuss the overall data science process and survey the full data science landscape which can be divided into four primary types of data science practitioners: Business Intelligence, Deep Technical, Broad Technical, and Well-Rounded. Each has a different vision of the field, a different set of skill emphases, and a different place in the data science ecosystem and in an effective data science team.
Once an organization understands this landscape and the varied skill sets that different kinds of data scientists bring to bear, they can bring data science more effectively into their work. This may involve developing a data science team within the organization, adding data scientists to the IT and business analysis teams, by consulting with external data scientists, or by developing hybrid data scientists in-house from business analysts and other professionals by training them in data science methods and skills. Takeaways include what to look for in data science hires and teammates and how to navigate the tradeoffs effectively when developing or enhancing data science efforts.
Introducing Power BI to Your Organization
How Power BI has helped Labelmaster with Pricing, Customer Segmentation, Item segmentation, (executive) reporting and data visualization, Marketing targeting improvement and with finding new insights.
Internally Labelmaster has better and faster decision-making capabilities where users might find answers in a few clicks (if the data is available and loaded into the Power BI model).
BI suddenly makes it easier to combine different data sources such as ERP Navision, Salesforce, other external fields, etc. and to make sense out of millions and millions of rows in a meaningful, refreshable and easy-to-digest way. Some case studies include:
Customer Segmentation clustering
Product Management has better visibility for outliers.
Sellers can explain cost-pass through impacts better to their customers.
Panel: Leveraging BI & Predictive Analytics in the Digital Age
The need for large-scale data analytics has become crucial for organizations to tackle head on. With the multitude of information and new technology in today’s Digital Age, the need to implement effective strategies leveraging Business Intelligence to Predictive Analytics has become more crucial than ever before. Join a panel of subject matter specialists to learn about today’s current trends & strategies in addressing today’s data overload.
Best Practices in Data-enabled Innovation That Have Stood the Test of Time Across Industries
Buzzwords and silos get in the way of identifying common themes of what works (or does not) in our profession. I recently collated about 45 examples of data enabled innovations spread over 15 years and a mix of industry verticals. Turns out there maybe some common and consistent themes after all. I am sharing my findings and inviting colleagues and peers to do the same. If any 10 leaders did this, we would quickly more reliable findings based on hundreds of case studies.
Networking Coffee Break
Building a Data-Driven Organization With Lean Resourcing
In a startup environment, resources are scarce and everyone must be scrappy. This seminar will dive into how we at Catch Co built our data and analytics organization from the ground up to facilitate a self service environment that allows us to push forward on sophisticated and impactful projects while ensuring our end users have access to the data they need, when and how they need it.
Creating Real Value in Real Estate Data
From energy savings to maximizing your real estate portfolio to enhanced human experiences, technology and analytics can generate significant value for an organization. Learn how your facility generates a mountain of data and how to leverage that data to enhance both employee productivity and the bottom line.