From digital advertising and ecommerce to financial services and retail, companies are looking to grow profits by interacting with consumers in real-time based on a 360-degree picture of what they care about, where they are, and what they are doing now. This means developing applications that combine the historical analysis provided by Hadoop with real-time analysis through Storm and within NoSQL databases, themselves.
This talk will examine design considerations and development approaches for successfully delivering interactive applications based on real-time analysis using a combination of Hadoop, Storm and NoSQL.
In this session, Ash Kulkarni will provide an overview of the data integration challenges that are stymieing big data projects. He will then discuss ways in which proven data integration practices can be married to the inherent flexibility of Hadoop to overcome these challenges. Ash will provide perspective on how traditional problems like code reuse, data transparency and lineage, and data quality need to be viewed with a slightly different slant when applied to the types of problems Hadoop is being used for today. He will discuss how having a plan to address these issues head-on will allow forward-looking organizations to use their big data strategically and to make Hadoop a part of their enterprise architecture. By sharing his perspective about this next major step towards the maturity of Hadoop, Ash will discuss why Hadoop is poised to change the face of analytics and data management.
There’s plenty of buzz, and buzzwords, floating around about in-memory as the path to the future. How do you cut to the chase and determine whether you need an in-memory management solution, and if you do, how do you find the right way to integrate it into your business?
This panel will delve into the technological, business and social evolution of in-memory solutions and whether or not it will become a normal part of doing business. Where do you start when architecting a solution and what do successful use cases really look like? Moderated by Edd Dumbill, panelists will share their insight and knowledge of the industry around big fast data, in-memory data management and how it is changing the future.
Todd will talk about the opportunities and challenges facing the big data market. What is the potential for business of successfully implementing big data? What are the pitfalls that could cause the market to fizzle and fall to the wayside? And what will the industry response be that will ensure success. Also learn what a virtual data machine is and why is it critical to building successful data products and applications in the big data age. Modern day applications draw their power from the ability to collect and integrate large amounts of data from a variety of disparate systems including social data, sensor data, traditional relational data and legacy application data. In fact, it turns out that 80% of the work in a big data project involves data integration and data quality. Learn how the concepts behind a virtual data machine increase developer productivity up to 5 times and avoid some of the pitfalls in big data projects.
Big Data is the focus of many discussions in the C-suite, but there has been confusion about the business value it can deliver. This panel will discuss the impact of dramatically accelerating insight and analyses using big data technology. These senior executives —responsible for enterprise data and analytics in leading companies--will share their approaches to evaluateing, introducing and applying big data to real-world problems. Paul Barth will lead this panel to highlight Big Data best practices at large enterprises, including
The buzz of Big Data Analytics is everywhere, and companies have taken different approaches to tackle their Big Data Analytics needs. In this session, I will focus on R and Hadoop, and will try to answer the following:
1. What does Big Data Analytics mean to our enterprise clients?
2. What popular tools are available for an R programmer to handle Big Data?
- Open source R
- RHadoop packages
- Revolution R Enterprise
Gaining a competitive edge in acquiring and analyzing customer intelligence is no longer just a matter of acquiring or managing customer data but a process of integrating customer data sources together into "customer profiles". Sources may be the customers themselves, data from your enterprise, or 3rd party data services. On a practical level, how does updating a profile on your customer improve your ability to sell / market to them? How does adding social data sources or demographic data sources differ from adding historic transactions or credit risk data to their profiles? Is there one index or database for me to solicit multiple data sources on one company, or is the act of building a customer profile on a business a fragmented act of data gymnastics?
As organizations adapt their strategies to promising nascent capabilities to discover and curate massive amounts of new and highly dynamic data, challenges are emerging in the form of how to ask meaningful questions. Exploiting the opportunities and avoiding the pitfalls of the "v's" of large amounts of data (e.g. volume, variety, velocity, veracity) is occupying significant mindshare for data science professionals. In the business-to-business space, these challenges are creating both significant opportunity and ominous new types of risk. While the vast availability and dynamic nature of data are allowing business counterparties to find and do business with each other in new and exciting ways, there are also new bad behaviors, false assumptions, and wholly inappropriate methods of problem formulation that are driving great risk at alarmingly increasing rates. This session will address the phenomena impacting business-to-business decisions in the era of massively available data.
In today’s buyer-driven marketplace, effective marketing and sales requires a much deeper understanding of customer needs. The proliferation of touch points across social, mobile, websites, sales and more makes the process of delivering the right message to the right contact at the right time even more difficult. How can I add more buyer intelligence to the leads I generate? How do I more effectively share customer insights across channels? How can I more effectively pinpoint the right buyers and how can I better understand their needs? Fortunately, there is a whole new set of technologies that can deliver the insight you need. Hear from Denis Pombriant, CEO of Beagle Research, who will share best practices on how companies have utilized data and analytics combined with technology to drive better sales and marketing results.
Business executives are facing an unprecedented amount of complexity in the global operating environment. With an overwhelming amount of information to monitor and more pressure to drive better business results, these business leaders have taken a much stronger interest in gaining better insight in order to improve the quality and speed of decision-making. Unfortunately, many of them do not understand how to utilize data and analytics to enhance their business strategies. At the same time, experts in data, analytics and technology don’t always have a seat at the table when strategic decisions are made. Hear how leading companies are utilizing data and analytics to improve cash flow and prioritize investments, optimize their supply chain, and improve sales and marketing effectiveness to drive better business results.
The rise of big data is an exciting — if in some cases scary — development for business. Together with the complementary technology forces of social, mobile, the cloud, and unified communications, big data brings countless new opportunities for learning about customers and their wants and needs. It also brings the potential for disruption, and realignment. Organizations that truly embrace big data can create new opportunities for strategic differentiation in this era of engagement. Those that don't fully engage, or that misunderstand the opportunities, can lose out. Learn how business model disruption will begin with big data business models and what you can do to disrupt business before you are disrupted.
The dynamic transformation of industries created by market forces and new business models are driving business leaders to increasingly rely on sophisticated vertical data and analytics techniques. To meet these needs, the future of "data markets" - or places to buy or acquire data - will continue to focus on building an ecosystem of vertical-specific sources. What makes each industry vertical so unique regarding data use cases, infrastructure, or data standardization / aggregation? If the challenge facing data-as-a-service is resisting "commoditization of data" then how do industry vertical data markets solve this problem? How have veterans of industry-specific data-as-a-service markets shaped their products to meet their unique customers’ demands?
They say that with great power comes great responsibility. As data scientists, with big data comes big obligations. But beware – we must act now. As “big data” prepares to enter the “trough of disillusionment” in the Gartner hype curve, we shoulder the burden of driving the industry forward and delivering on the promise of data, now. We will be moving from a time of great investment to great skepticism and it is our responsibility to our customers, companies and industry to prove that big data isn’t just a fad. To do this, we must help our organizations identify and focus on the metrics that truly matter, help translate business questions into data questions and data findings into business strategy, and build data products that drive value for our end users and organizations. Above all, we must continuously tout not just the innovative work that we do, but more importantly, how our work significantly impacts our company’s bottom line.
Advertising technology data companies are proving now more than ever that they can be good actors with respect to consumer privacy online and on mobile. In this panel session, Marc Groman, Executive Director for the Network Advertising Initiative, Noga Rosenthal, General Counsel for the Network Advertising Initiative and Omar Tawakol, CEO of BlueKai, will spark an open dialogue about issues and solutions that move the privacy ball forward and share some of the benefits that self-regulation brings to the entire online data ecosystem. The panel will take a deep dive into the issues of online privacy and how self-regulation has encouraged third-party advertising companies to be more engaged than ever with raising standards for the industry. It will address consumers’ misconceptions about the ad tech industry that has caused third party data companies to be criticized in the privacy debate as well as debunk several of the misconceptions floating around about online privacy. Effective self-regulation includes high standards that must evolve over time, ongoing compliance, and enforcement. The panel will also detail how self-regulation is promoting the overall health of the online advertising ecosystem and a win win win for consumers, industry, and regulators.