Topics: Business Intelligence Innovation Summit 2014 Chicago: Day 2 Highlights
Tags: Analytics, Business Intelligence, Chicago-IL, Conference, Data Processing, IE Group, Innovation, Visualization
Highlights from the presentations by Business Intelligence leaders from Netflix, Hyatt, GE Capital and University of Texas on day 2 of Business Intelligence Innovation Summit 2014 in Chicago.
Business Intelligence (BI) is playing a key role in modern business and is considered central to identifying new opportunities within an organization. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability for a business. Using Business Intelligence theories, methodologies, processes & more is paramount to the success of such pursuits.
The
Business Intelligence Innovation Summit (May 21 & 22, 2014) organized by the Innovation Enterprise at Chicago, IL covered major challenges and opportunities being observed by BI leaders across industries. The summit featured an industry led speaker line-up consisting of 25+ leading business analysts, data scientists, intelligence developers, business researchers and business intelligence leaders. BI leaders shared their perspective on common challenges, best practices and real-life case studies.
We provide here a summary of selected talks along with the key takeaways.
Highlights from Day 1.
Here are highlights from Day 2 (Thursday, May 22, 2014):
Rajeev Guliani, Director, Data Science & Engineering, Netflix gave an insightful talk on "Analytics @Netflix: Fast, Iterative, and Insightful". Netflix collects data from a variety of sources including usage statistics, user ratings, set top boxes, user profiles, social networks, etc. All this data is used across business units for product design, content selection, marketing, customer experience, payments and finance. The data (greater than 7 peta-bytes) is stored on Amazon S3 (Simple Storage Service) and Teradata Cloud, where it observes around 100 billion events/transactions per day. Using cloud-based architecture enables Netflix to focus on its own core competencies and not worrying about hosting issues such as maintenance, capacity planning, scaling, etc.
Next, he explained the architecture of Netflix data infrastructure. Most of the data processing happens on Hadoop platform and then the aggregated data is pushed to Teradata for faster queries, interactive dashboards. MicroStrategy and Tableau are currently used for reporting and visualization. Visual insights is also being assessed as a future option. In order to minimize the ambiguity due to multiple visualization tools being used across the firm, the firm is moving more and more logic into data layer, and having less of logic in the reporting or visualization tools.
He explained how Netflix data platforms, tools and analytics teams are evolving to keep up with growing complexity and data volumes to drive optimization and decision-making. The BI process at Netflix is focused on collaboration, responsibility, alignment and information sharing. The process encourages light documentation, design guidance and self-service tools. In conclusion, he mentioned that there are four key factors that enable Netflix to move fast: culture & people, dedicated & co-located teams, analytics culture and flat organization.
Parthiv Sheth, Director, Business Intelligence, Hyatt delivered an interesting talk on "BI Innovation at Hyatt". He started his talk with describing why the technical issues are relatively easier to solve whereas the people issues (related to control, skills, incentives, etc.) are way harder to resolve. He explained data processing in the following five steps (in increasing order of difficulty):
In the new era of consumerization of analytics, we are all seeing that analytics is sexy again. However, there is a considerable data and statistics literacy gap, and thus, an analytics revolution has to precede in order to prepare the masses for analytics. It is very important to understand that Analytics is not the same as Reporting & Data Warehousing. Analytics is very different from typical IT projects. IT is often a cost center, lacks strategic influence; whereas Analytics needs more of scientists than engineers, and has significant strategic influence. People can be irrational. Instincts can be wrong. When that happens, Analytics is the only reliable way to identify and act on it. While applying Analytics, context and experience are invaluable.
Vijay Thiruvengadam, Director, Data Architect, University of Texas shared his story of setting up BI framework for UT Austin in his talk "Sizzle that Sells!! - Secrets of Building a Next Generation Business Intelligence Program". The major data problems included data not transparent, weak reporting tools for reliable transactional systems, and widespread discrepancies - data definitions, naming conventions, business logic, etc. In order to solve these problems, various challenges had to be overcome such as showing ROI, time-to-market, data stewards availability, etc.
A BI initiative, called Project Information Quest (IQ) was undertaken to deliver accurate and flexible analytical tools and management information to support University leaders in making data-driven decisions. The project was launched with one-on-one and group interviews with business users to define and document requirements. Key interview questions were: What business questions they cannot currently answer?, What business questions take a long time to answer?, What they hate about current reports?
He shared the key factors (tips and techniques) that were effectively handled in deploying a successful enterprise Business Intelligence (BI) solution in a world class higher education institution. He discussed how to shorten time-to-market to exceed customer analytical needs and to delight customers. He mentioned that selling a project in current technology trend is the easy part but making it part of the daily routine is harder.
Talking about the project, he mentioned that collaboration was critical as the BI program was based on grassroots approach. The focus was on answering the business questions, while shortening time-to-market for analytical needs. Next, good governance helped in creating a shared, clear understanding about strategy, roles, resources and priorities. The success of the project provided a strong foundation to expand on, avoided duplication of similar efforts across campus, and positioned IQ as integral part of university information ecosystem. He stated the following seven golden rules that he learned from his experience:
Melanie Shanks, Business Intelligence Leader, Commercial Distribution Finance, General Electric talked about GE Capital's BI pursuits in her talk "Customer Facing Analytics". Significant advances in big data, predictive analytics and location services are redefining the role of IT. At GE Capital, Americas — the North American commercial lending and leasing arm of GE Capital — IT innovation pro-actively helps the business and its customers achieve their goals and objectives. IT has expanded boundaries and rapidly evolved from an important back-office function to an integral part of the overall P&L.
GE Capital, Americas is striving to better serve customers and provide a differentiated offering. In order to create disruption, one needs commercial intensity as well as market speed. She walked through the firm's focus areas for IT: Geo-spatial, User Experience, Predictive Analytics and Social. She talked about the GE Capital Social Media Command Center to offer free, value-added services to customers. The center provides real-time intuitive visualizations, sentiment analysis, and reputation management. Lastly, she mentioned the key take-aways from her experience as: focus on customer "stickiness", embrace new revenue models, continuously analyze success and establish right partnerships for innovation.
Related:
The
Business Intelligence Innovation Summit (May 21 & 22, 2014) organized by the Innovation Enterprise at Chicago, IL covered major challenges and opportunities being observed by BI leaders across industries. The summit featured an industry led speaker line-up consisting of 25+ leading business analysts, data scientists, intelligence developers, business researchers and business intelligence leaders. BI leaders shared their perspective on common challenges, best practices and real-life case studies.
We provide here a summary of selected talks along with the key takeaways.
Highlights from Day 1.
Here are highlights from Day 2 (Thursday, May 22, 2014):
Rajeev Guliani, Director, Data Science & Engineering, Netflix gave an insightful talk on "Analytics @Netflix: Fast, Iterative, and Insightful". Netflix collects data from a variety of sources including usage statistics, user ratings, set top boxes, user profiles, social networks, etc. All this data is used across business units for product design, content selection, marketing, customer experience, payments and finance. The data (greater than 7 peta-bytes) is stored on Amazon S3 (Simple Storage Service) and Teradata Cloud, where it observes around 100 billion events/transactions per day. Using cloud-based architecture enables Netflix to focus on its own core competencies and not worrying about hosting issues such as maintenance, capacity planning, scaling, etc.
Next, he explained the architecture of Netflix data infrastructure. Most of the data processing happens on Hadoop platform and then the aggregated data is pushed to Teradata for faster queries, interactive dashboards. MicroStrategy and Tableau are currently used for reporting and visualization. Visual insights is also being assessed as a future option. In order to minimize the ambiguity due to multiple visualization tools being used across the firm, the firm is moving more and more logic into data layer, and having less of logic in the reporting or visualization tools.
He explained how Netflix data platforms, tools and analytics teams are evolving to keep up with growing complexity and data volumes to drive optimization and decision-making. The BI process at Netflix is focused on collaboration, responsibility, alignment and information sharing. The process encourages light documentation, design guidance and self-service tools. In conclusion, he mentioned that there are four key factors that enable Netflix to move fast: culture & people, dedicated & co-located teams, analytics culture and flat organization.
Parthiv Sheth, Director, Business Intelligence, Hyatt delivered an interesting talk on "BI Innovation at Hyatt". He started his talk with describing why the technical issues are relatively easier to solve whereas the people issues (related to control, skills, incentives, etc.) are way harder to resolve. He explained data processing in the following five steps (in increasing order of difficulty):
- Data - relatively easy to get assess and maintain quality
- Analysis - objective and rigorous analysis is difficult
- Insights - hard to find insights of true business value
- Action - acting on insights to obtain tangible business results is even harder
- Operations - operationalizing all of the above with consistency and continuous improvement is nearly impossible
In the new era of consumerization of analytics, we are all seeing that analytics is sexy again. However, there is a considerable data and statistics literacy gap, and thus, an analytics revolution has to precede in order to prepare the masses for analytics. It is very important to understand that Analytics is not the same as Reporting & Data Warehousing. Analytics is very different from typical IT projects. IT is often a cost center, lacks strategic influence; whereas Analytics needs more of scientists than engineers, and has significant strategic influence. People can be irrational. Instincts can be wrong. When that happens, Analytics is the only reliable way to identify and act on it. While applying Analytics, context and experience are invaluable.
Vijay Thiruvengadam, Director, Data Architect, University of Texas shared his story of setting up BI framework for UT Austin in his talk "Sizzle that Sells!! - Secrets of Building a Next Generation Business Intelligence Program". The major data problems included data not transparent, weak reporting tools for reliable transactional systems, and widespread discrepancies - data definitions, naming conventions, business logic, etc. In order to solve these problems, various challenges had to be overcome such as showing ROI, time-to-market, data stewards availability, etc.
A BI initiative, called Project Information Quest (IQ) was undertaken to deliver accurate and flexible analytical tools and management information to support University leaders in making data-driven decisions. The project was launched with one-on-one and group interviews with business users to define and document requirements. Key interview questions were: What business questions they cannot currently answer?, What business questions take a long time to answer?, What they hate about current reports?
He shared the key factors (tips and techniques) that were effectively handled in deploying a successful enterprise Business Intelligence (BI) solution in a world class higher education institution. He discussed how to shorten time-to-market to exceed customer analytical needs and to delight customers. He mentioned that selling a project in current technology trend is the easy part but making it part of the daily routine is harder.
Talking about the project, he mentioned that collaboration was critical as the BI program was based on grassroots approach. The focus was on answering the business questions, while shortening time-to-market for analytical needs. Next, good governance helped in creating a shared, clear understanding about strategy, roles, resources and priorities. The success of the project provided a strong foundation to expand on, avoided duplication of similar efforts across campus, and positioned IQ as integral part of university information ecosystem. He stated the following seven golden rules that he learned from his experience:
- Crawl, Walk and Run
- Under-promise and over-deliver
- Measure results and keep investment in line with results
- Leverage existing transactional systems
- Driven by business needs
- Accuracy more important than schedule
- Emphasis on data governance
Melanie Shanks, Business Intelligence Leader, Commercial Distribution Finance, General Electric talked about GE Capital's BI pursuits in her talk "Customer Facing Analytics". Significant advances in big data, predictive analytics and location services are redefining the role of IT. At GE Capital, Americas — the North American commercial lending and leasing arm of GE Capital — IT innovation pro-actively helps the business and its customers achieve their goals and objectives. IT has expanded boundaries and rapidly evolved from an important back-office function to an integral part of the overall P&L.
GE Capital, Americas is striving to better serve customers and provide a differentiated offering. In order to create disruption, one needs commercial intensity as well as market speed. She walked through the firm's focus areas for IT: Geo-spatial, User Experience, Predictive Analytics and Social. She talked about the GE Capital Social Media Command Center to offer free, value-added services to customers. The center provides real-time intuitive visualizations, sentiment analysis, and reputation management. Lastly, she mentioned the key take-aways from her experience as: focus on customer "stickiness", embrace new revenue models, continuously analyze success and establish right partnerships for innovation.
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