↓ Agenda Key
Visionary speaker presents to entire audience on key issues, challenges and business opportunities
Panel moderated by Master of Ceremonies and headed by four executives discussing critical business topics
Solution provider-led session giving high-level overview of opportunities
End user-led session in boardroom style, focusing on best practices
Interactive session led by a moderator, focused on industry issue
Pre-determined, one-on-one interaction revolving around solutions of interest
Discussion of business drivers within a particular industry area
Analyst Q&A Session
Moderator-led coverage of the latest industry research
Several brief, pointed overviews of the newest solutions and services
Overview of recent project successes and failures
Open Forum Luncheon
Informal discussions on pre-determined topics
Unique activities at once relaxing, enjoyable and productive
Were all in the depths of Big Data and the Analytics frenzy. It's one of the hottest areas of business strategy. As we all depend upon analytics to drive our businesses(and compete against others doing the same), hiring, developing, and retaining analytical and big data talent is becoming more critical to all of us. As we collect more information both inside and outside our corporations and begin to understand the potential value, this talent is the key to unlocking that potential. We will focus on first knowing who this talent is, and then breaking it into four types of talent (analytics, modelers, big data, and developers); all of these are referred to as Big Data experts or data scientists at times. Well discuss how to ensure you hire the right people, the often different types of development they prefer, and how to maximize the potential to retain your best talent.
It is common for companies embarking on their big data and analytics journey to stumble on the topic of ROI. This session will explore the opportunities and challenges for IT and Business alike in driving value and outcomes.
We will explore the CIO agenda and the Business leader agenda and share experiences on how both contingencies approach where to start and where to focus. We will also explore the topic of data discovery as a strategic approach for driving ROI. Finally, we will highlight several examples of where value and ROI is being driven.
Supporting big data analysis in the cloud is forcing many to confront various, albeit manageable, architectural hurdles. In this session, we will discuss the benefits of cloud based analytics and how it can improve productivity within the organization.
Given the difficulty in quantifying the true value of Big Data, how do companies implement successful Big Data projects? What are the key obstacles to navigate that ensure it can deliver on its promise? In this Big Data Kick"Off session, well describe the basic milestones of Big Data projects, proven approaches to delivering Big Data projects and introduce critical challenges and opportunities to expect along the way. Finally, well discuss ways to measure the value of a Big Data project so it can be used to obtaining funding for additional development phases.
Troy Christensen, Exploration and Production Data Director, Integrated Oil Services
Many companies are undertaking "Big Data" initiatives. How can we be certain that these initiatives are aligned with business opportunities and challenges? What are the appropriate processes to reinforce this alignment?
How - specifically - are other companies monetizing Big Data? What are the challenges and obstacles that companies are facing and how are they overcoming these obstacles?
Data can transform your business. In the new digital economy, organizations are demanding more and more analytics on all of their data. The operational ability to capture the right information, at the right time and act on it preemptively is todays sure driver of sustainable business value and competitive advantage.
This session will present the challenges of acting on big data in real-time as well as how you can expand core big data competencies, build data discovery capabilities, and put your insights into action.
As enterprises begin to get comfortable with Hadoop based architectures - some may wonder - what's next? As IT and line-of-business executives begin to operationalize Hadoop and MPP based batch big data analytics, it's time to begin to understand and prepare for the next wave of innovation in data processing, visualization and analytics - Analytics over real-time streaming data.
This session will provide an overview and discussion on the business value, use cases and architectural considerations of integrating real-time streaming analytics into your Enterprise Big Data roadmap.
Decisions establishing the underlying structural framework within which a predictive model is constructed can have a greater impact than the remaining process of model building and optimizing. Yet that central component of creating a model foundation is often not given a focus in proportion to its significance.
This session will discuss:
Due to the lack of qualified analytics professionals here in North America, many executive teams are looking offshore to see if they can solve the problem internationally. But is offshore analytics a competent strategy for your enterprise? Many factors need to be considered: the priority of analytics within the organization in addition to its function, the analytics leadership team, the maturity of data assets, and the presence of existing outsourcing relationships.
Big Data is certainly a hot topic. But as with any hyped technology, the topic of big data is starting to lose its initial luster. Part of the reason for this is that many firms that made big investments in big data are still struggling to find the promised value. Hiring more and more (expensive) data scientists is not a viable solution. The problem which was well stated by Tom Davenport is that Big Data is too important to be left to the quants. Innovative firms understand that innovation can come from anywhere not just a select few individuals. Therefore what is needed is a way to reduce the bottleneck that prevents all business users from gaining benefit from big data so that firms can unlock the true potential of this valuable resource.
This session will help you create the optimal organizational strategy for big data by understanding the scenarios in which they are successful. This strategy will embed big data into the fabric of the business to foster an analytic culture aligned with your organization. We will focus on why some top organizations are able to successfully extract insight from their big data with analytic development processes, and why many others struggle to put their valuable data to good use.
Topics to be discussed:
Organizations have lost millions due to poor data management practices, but remain unaware of the root causes of their losses. Unless IT professionals can monetize these lost opportunities and their related costs, gaining executive-level approval for basic data management investments will continue to be difficult. This sets up an unfortunate loop: executive management is focused on fixing symptoms, but cannot address the underlying problems. This workshop illustrates how to identify specific costs of poor data management practices using delegate supplied examples. As organizations understand poor data management practices as the root cause of many of their problems, they will be more than willing to make the required investments in our profession.
While "big data" is the hot topic these days, what is not being discussed is how to really create incremental value from big data assets. In this lively round table session, we will discuss the need for new analytical approaches to handle big data and how analytics can be used to uncover hidden insights that can transform businesses and even industries.
Big Data can tell you the right thing to do - the best offer to make, price to pay, risk to take, feature to change, or rate to set. Using advanced machine learning software algorithms you can scan the universe of social media, machine data, transaction records, web clicks, and other Big Data, to create an evidence-based model of the best choice to make. There's just one problem - traditional machine learning software was not designed to handle the scale or complexity of Big Data, making it very slow and not very accurate.
Most transactions are online. But, technology failures are still common, leading to brand damage and loss in market share. In this session, we will discuss the right data architecture for online transactions: disaster-proof and simple to operate, with search, analytics and security built in. Learn how the world has changed where business drives technology instead of the other way around.
Why are there so many conversations about data? The IT industry has spent the last few years trying to name it, number it and determine what to do with it. As with all things in life, there is good and bad that can come from it.
This session will discuss top data challenges, and how you and your company play an integral role in determining what to do with your data proactively. What if the data you possess could cure cancer? What if it you could determine how to use your data to assist your customers or local government in an emergency? On the other hand, how do you do those things and protect customer privacy and security? A discussion of how your company could unleash your data to benefit you and the world around you, benefit your customers, and protect them.
Open data holds the potential to transform not only the public sector, but the private sector as well. Many executives see proprietary constraints and an over-abundance of data sets as major obstacles in harnessing the power of Open Data. But Open Data is the same as Big Data, Small Data, or Smart Data: it means nothing if you lack understanding in your data goals.
Organizations that want to stay ahead of the curve have learnt to capitalize on the synergies of big data. However, harnessing the mammoth data to result in improved ROI goes beyond just data crunching. Analytics initiatives will need to be approached from a holistic perspective. This is where the convergence of interdisciplinary approaches of Math, Business, Technology, and Behavioral Sciences will need to happen - defining the decision sciences space. This session will cover the multiple dimensions on which an organization will need to progress in order to institutionalize Decision Sciences.
Big data has become a reality in the enterprise, and so have the potential benefits of its proper use. As information continues to pile up, effectively making it work for the business is feasible for organizations of all sizes, but it can be easy to lose ones way in the process. This session will discuss key guiding principles to identifying, harnessing and optimizing big data, offering a framework for short- and long-term success.
As our businesses continue to expand its use and dependency on big data, it is also evident that we need to extend the reach of data governance to ensure the data is properly managed and leveraged within the confines of regulations and associated business controls. The extension of a data governance program is a critical ingredient toward the success of any analytic program. The challenge is when and how to establish a data governance foundation; and what are the main priorities on managing data in this new horizon. Through the attendance of this session, attendees will learn:
What can Retail learn from Finance? What can Finance learn from Healthcare? What can Healthcare learn from Retail? Lots can be learned from industry to industry. In order for organizations and industries to make the most out of Big Data, it is vital that success stories and views from across the Enterprise are shared and discussed to enable Chief Executives to devise Big Data strategies accordingly.
This CIO Executive Vision panel discussion will bring together executives from different industries to share best practice strategies, successes and failures and discuss how industries can work together and learn from each other in order to get ahead in the Big Data world.
It is common for companies embarking on their big data and analytics journey to stumble on the topic of ROI. This session will explore the opportunities and challenges for IT and Business alike in driving value and outcomes. We will explore the CIO agenda and the Business leader agenda and share experiences on how both contingencies approach where to start and where to focus. We will also explore the topic of data discovery as a strategic approach for driving ROI. Finally, we will highlight several examples of where value and ROI is being driven.
Enterprises have made major investments in analytics over the years, and these investments are largely paying off. Today however, data is exploding - new sources, more volume, more complexity. Business leaders suspect there is hidden value in this data but today's approaches often constrain their ability to find and exploit this value. The power of discovery should not be constrained. Analysts should be free to act on a hunch, uninhibited by rigid data structures and traditional approaches.
We have moved from an information-poor to an information-rich society. Practically-unlimited availability of data, computing, networking, and socio-mobile connectivity are fundamentally altering our world. In particular, they are enabling businesses to become more effective and efficient by using big data analytics - collecting all relevant data and automating their processing to drive decision-making. This represents a fundamental shift from traditional business analytics where limited amount of structured data is batch-processed to produce standard Business Intelligence reports. We will assess the current state of big data analytics, technology and business trends, and their enormous implications to the future of all businesses.
The session will dicuss the challenges with raw data stored in file-based NoSQL solutions, and will propose best practices for businesses dealing with big data. You cannot possibly make good business decisions when you do not understand the interpretation and underlying assumptions of raw text. To get this right, we must add context to the data.
Transformations such as interpretation, editing, standardization, acronym resolution, etc., are needed to disambiguate the data and make it useful for analytics.
We are producing more data than ever before in the consumer and the corporate world. The big question to ask yourself is, Where do you want to begin? Brand sentiment , Predictive maintenance, Insider threats, Network optimization, Product recommendation and Fraud detection are just some of the big insights you can get from your big data.
Most transactions are online. But, technology failures are still common, leading to brand damage and loss in market share. Learn how the world has changed where business drives technology instead of the other way around.
Big Data, the combination of high volume, velocity, variety, and validity, is requiring new forms of data acquisition, management, and integration to enable enhanced decision making, insights and opportunity.
The Ethics of Big Data Issues around the use of volumes of data being collected in todays world is an undeniably important topic and concern among both business and consumers. As companies seek to capture data about consumer habits, privacy concerns have flared. When it comes to the analysis and use of this data, organizations must consider the ethical implications at every stage. In this session, we will tackle important areas of consideration including:
What's the return on Investment (ROI) on Big Data? More importantly, what does the future hold? Data is the new currency and like any currency, how it's managed determines its true value. Big Data monetization is not about turning data into money. Instead, it's about taking information and turning it into opportunity. It's about the need to understand the real meaning of data in order to extract value from it. And it's about achieving this objective through a partnership with business and technology.
During this session, the panellists will discuss and showcase how to understand and place tangible value on data and how the true value of data can be realized for the future.