Big Data Innovation Summit 2014 London: Highlights
Highlights from the presentations by Big Data technology practitioners from Sears Holdings, Microsoft, Ticketmaster during Big Data Innovation Summit 2014 in London.
With the Big Data Technology and Services market set to grow to $16.9 Billion in 2015 organizations are becoming more aware of the need to begin large scale investments in order to successfully adopt Big Data Strategies. With the focus within organizations on integrating more data sources and the ability to analyze larger volumes of data, the speed by which they can obtain these answers is vital. The theories underpinning current developments in Big Data don't necessarily pan out in the real world, particularly when examined in a global context.
Big Data Innovation Summit 2014 (May 14-15, 2014) was recently organized in London by the Innovation Enterprise. The summit helped businesses understand & utilize data-driven strategies and discover what disciplines will change because of the advent of data.
To help its readers succeed in their Analytics pursuits, KDnuggets provides concise summaries from selected talks at the summit. These concise, takeaway-oriented summaries are designed for both – people who attended the summit but would like to re-visit the key talks for a deeper understanding and people who could not attend the summit.
Here are highlights:
Ankur Gupta, Director, Big Data, Sears Holdings gave a talk on “Transforming Data Architecture Complexity at Sears”. He started his talk citing the following from a recent Wikibon survey:
Enterprise practioners believe the potential value of Big Data is significant. However, many are struggling to derive maximum value from their Big Data investments (45% have realized only partial value).
Wikibon reported that there are three major reasons behind this:
Focusing on the third reason, he stated that when considering implementing Hadoop into enterprise data architecture, it is very important to understand what Hadoop can and cannot do, specifically for your business. When implemented carefully, the Hadoop ecosystem helps in making smart business decisions quickly through faster processing on larder data sets for deep analytics.
Traditional data warehouses pose a variety of challenges such as high ETL (Extract-Transform-Load) costs, data latency and redundancy, batch window limits, etc. Ankur described how Sears uses Hadoop as a data hub to minimize data architecture complexity – resulting in a reduction of time to insight by 30-70% - and discover Big Data “quick wins” such as ETL modernization and mainframe MIPS reduction.
Based on his experience, he identified the following as key to achieve Big Data success:
He explained how Social Analytics with Hadoop can be leveraged for understanding product perception and brand sentiment analysis. By implementing Hadoop and Cassandra into a traditional data warehousing environment, Sears is able to provide more accurate and real-time inventory, pricing, sales and return data as well as predict ideal floor plans.
In the end, he mentioned that Hadoop can help answer questions that were difficult or cost prohibitive to answer preciously. However, it needs a clear strategy and long-term plan with proper attention to data governance.
Eduardo Ustaran, Author of “The Future of Privacy” delivered an insightful talk on “With Big Data comes Big Responsibility”. He emphasized that while we all are excited about the innovation opportunities through leveraging Big Data, we also need to be cognizant of the underlying Privacy concerns in order to stay compliant with privacy and data protection laws across the world.
Users are losing control of their data. Even worse, they are not even aware about how much of data about them is being recorded and used for what all purposes. Thus, as we make progress using Big Data technologies we need to understand the importance of getting the balance right between the progress of mankind and the thread to some of the very basic rights of all humans.
Getting consent from user before using every single piece of user’s information is cost-prohibitive and thus, we need more advanced technological solutions to protect privacy. He explained the privacy implications of a Big Data world, the challenges presented by the current legal framework and what compliance with the law will involve in the future. Nations are still debating how to future proof Big Data policy making? Should we regulate technology? Or rather regulate user behavior?
He recommended that we need to promote transparent use of data along with having privacy and security as default priorities in all business plans.
Carlo Zapponi, Data Visualization Specialist, Microsoft delivered a very interesting talk on how to explore data with a touch of fun. He shared his experience of turning raw consumer data into compelling lively stories, enriched with powerful visualizations by designing & developing easy-to-use, appealing yet insightful online tools to enable end-users to instantly understand the data in the right context, enabling them to instantly spot patterns, trends and relations in a self-service manner.
Data scientists are busy analyzing the huge volume of structured and unstructured data – referred to as Big Data – to discover patterns and find solutions to existing problems. But, to propel the innovation engine of Big Data we need to make it accessible to the end consumers and make it highly intuitive to explore, understand and use. Data Visualization helps the end consumers participate in data exploration while staying away from technical details. In data visualization functionality and entertainment are deeply entangled. Carlos explained how the “fun layer” in data exploration helps the user to get familiar with and truly enjoy the journey through data. He compared Form vs Beauty vs Fun to describe the science of data visualization.
We need intuitive visualizations that will enable users to see patterns and connections that matter. In conclusion, he stated that if we make data analysis fun for end consumers, they will enjoy the process and will be able to make their own discoveries.
Sophie Crosby, Vice President, Insight at Ticketmaster delivered a talk on “Big Data to Drive Decision-Making at Ticketmaster”.
She started her talk citing the following trends:
Sophie quoted following words from Virginia Rometty, CEO, IBM: “Information is going to be the basis of competitive advantage for every company in the world. What you will see with rapid data and social sharing is the death of the average and the era of you. Businesses will be able to truly the individual”
Ticketmaster has a lot of Big Data business use cases, the most prominent ones being: managing risk & compliance, reducing cost, getting more tickets, and selling more tickets. The biggest challenge is to manage data ownership, consent and governance across multiple markets, multiple platforms and multiple clients.
Discussing about data she mentioned that Big Data is massive, messy and muddled. She talked about analysis showing immense need for powerful insights on demand and supercharged segmentation. Engagement scoring is also an important factor to consider. In action, they perform timely analysis, mass automation, and enable real-time match offers. She concluded the talk by stressing that data projects are the major drivers of change leading to a better world.
Related:
Big Data Innovation Summit 2014 (May 14-15, 2014) was recently organized in London by the Innovation Enterprise. The summit helped businesses understand & utilize data-driven strategies and discover what disciplines will change because of the advent of data.
To help its readers succeed in their Analytics pursuits, KDnuggets provides concise summaries from selected talks at the summit. These concise, takeaway-oriented summaries are designed for both – people who attended the summit but would like to re-visit the key talks for a deeper understanding and people who could not attend the summit.
Here are highlights:
Ankur Gupta, Director, Big Data, Sears Holdings gave a talk on “Transforming Data Architecture Complexity at Sears”. He started his talk citing the following from a recent Wikibon survey:
Enterprise practioners believe the potential value of Big Data is significant. However, many are struggling to derive maximum value from their Big Data investments (45% have realized only partial value).
Wikibon reported that there are three major reasons behind this:
- A lack of skilled Big Data practioners
- “Raw” and relatively immature technology
- A lack of compelling business use case
Focusing on the third reason, he stated that when considering implementing Hadoop into enterprise data architecture, it is very important to understand what Hadoop can and cannot do, specifically for your business. When implemented carefully, the Hadoop ecosystem helps in making smart business decisions quickly through faster processing on larder data sets for deep analytics.
Traditional data warehouses pose a variety of challenges such as high ETL (Extract-Transform-Load) costs, data latency and redundancy, batch window limits, etc. Ankur described how Sears uses Hadoop as a data hub to minimize data architecture complexity – resulting in a reduction of time to insight by 30-70% - and discover Big Data “quick wins” such as ETL modernization and mainframe MIPS reduction.
Based on his experience, he identified the following as key to achieve Big Data success:
- Bring IT and Business together
- Define realistic success criteria
- Ask “what are you really trying to accomplish?” i.e. discover your Big Data use case.
- Understand how Hadoop will fit into your environment
- See the end results first before you start your journey
He explained how Social Analytics with Hadoop can be leveraged for understanding product perception and brand sentiment analysis. By implementing Hadoop and Cassandra into a traditional data warehousing environment, Sears is able to provide more accurate and real-time inventory, pricing, sales and return data as well as predict ideal floor plans.
In the end, he mentioned that Hadoop can help answer questions that were difficult or cost prohibitive to answer preciously. However, it needs a clear strategy and long-term plan with proper attention to data governance.
Eduardo Ustaran, Author of “The Future of Privacy” delivered an insightful talk on “With Big Data comes Big Responsibility”. He emphasized that while we all are excited about the innovation opportunities through leveraging Big Data, we also need to be cognizant of the underlying Privacy concerns in order to stay compliant with privacy and data protection laws across the world.
Users are losing control of their data. Even worse, they are not even aware about how much of data about them is being recorded and used for what all purposes. Thus, as we make progress using Big Data technologies we need to understand the importance of getting the balance right between the progress of mankind and the thread to some of the very basic rights of all humans.
Getting consent from user before using every single piece of user’s information is cost-prohibitive and thus, we need more advanced technological solutions to protect privacy. He explained the privacy implications of a Big Data world, the challenges presented by the current legal framework and what compliance with the law will involve in the future. Nations are still debating how to future proof Big Data policy making? Should we regulate technology? Or rather regulate user behavior?
He recommended that we need to promote transparent use of data along with having privacy and security as default priorities in all business plans.
Carlo Zapponi, Data Visualization Specialist, Microsoft delivered a very interesting talk on how to explore data with a touch of fun. He shared his experience of turning raw consumer data into compelling lively stories, enriched with powerful visualizations by designing & developing easy-to-use, appealing yet insightful online tools to enable end-users to instantly understand the data in the right context, enabling them to instantly spot patterns, trends and relations in a self-service manner.
Data scientists are busy analyzing the huge volume of structured and unstructured data – referred to as Big Data – to discover patterns and find solutions to existing problems. But, to propel the innovation engine of Big Data we need to make it accessible to the end consumers and make it highly intuitive to explore, understand and use. Data Visualization helps the end consumers participate in data exploration while staying away from technical details. In data visualization functionality and entertainment are deeply entangled. Carlos explained how the “fun layer” in data exploration helps the user to get familiar with and truly enjoy the journey through data. He compared Form vs Beauty vs Fun to describe the science of data visualization.
We need intuitive visualizations that will enable users to see patterns and connections that matter. In conclusion, he stated that if we make data analysis fun for end consumers, they will enjoy the process and will be able to make their own discoveries.
Sophie Crosby, Vice President, Insight at Ticketmaster delivered a talk on “Big Data to Drive Decision-Making at Ticketmaster”.
She started her talk citing the following trends:
- Consumer Mobility - Today's consumer lifestyles are undergoing revolutionary change; in the past two years, people are spending significantly more time on the go.
- Massive User Generated Content - With the number of people on social networks continuing to grow, the opportunity for brands to leverage content created by consumers in marketing efforts will continue to grow.
- Enabling Tech Trends – Cloud, Massive Parallel Processing, Columnar data have been there for a significant time. But due to recent advances in processing power and good quality open source software has made technology much more usable.
Sophie quoted following words from Virginia Rometty, CEO, IBM: “Information is going to be the basis of competitive advantage for every company in the world. What you will see with rapid data and social sharing is the death of the average and the era of you. Businesses will be able to truly the individual”
Ticketmaster has a lot of Big Data business use cases, the most prominent ones being: managing risk & compliance, reducing cost, getting more tickets, and selling more tickets. The biggest challenge is to manage data ownership, consent and governance across multiple markets, multiple platforms and multiple clients.
Discussing about data she mentioned that Big Data is massive, messy and muddled. She talked about analysis showing immense need for powerful insights on demand and supercharged segmentation. Engagement scoring is also an important factor to consider. In action, they perform timely analysis, mass automation, and enable real-time match offers. She concluded the talk by stressing that data projects are the major drivers of change leading to a better world.
Related:
