Dallas, TX, USA
↓ 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
Is there a difference between a Chief Analytics Officer and a Chief Data Officer? Not everyone agrees, but many in the data discipline see the CDO as focusing on tactical data management while the CAO concentrates on the strategic deployment of analytics.
In this session, we will discuss the role of a CDO and a CAO and the functions of an organizations need for one or the other, or both.
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.
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 today's 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.
This session provides an overview of Experfy's (Harvard Innovation Lab) thought leadership, approach, methodology, and leading practices for big data and advance analytics. Step through a demonstration of Experfy's (Harvard Innovation Lab) Financial Predictive analytics solutions to better predict financial performances. Participants will learn the strategy and roadmap and the key steps that are required to implement advance analytics on big data using technologies such as HADOOP, SAP HANA, and R statistical computing. See how Experfy's (Harvard Innovation Lab) predictive model uses data mining as a technique for building effective predictive models where the data is visually explored and used to determine which predictive model best fits the data. Learn how Experfy's (Harvard Innovation Lab) big data and advanced analytics solution works across industry and business functions, and how the solution connects corporate strategy to measureable outcome metrics " becoming a business driver.
Building a new muscle to advance analytics - with a discussion of how data management is imperative, how it can be done most effectively, how new tools, technology, and advanced modeling can be brought in to automate the process, and how that in turns enables more advanced analysis.
Customer intimacy is an imperative for companies who are struggling with increasing commoditization of goods and services and an explosive growth in the channels of engagement. Digital organizations have a head start and have disrupted traditional customer interfaces to gain competitive advantage. As a result, organizations across industries are now exploring ways to energize the customer experience and fill the digital gap.
This session will present practical ways in which leaders in digital customer experience are leveraging Big Data to harvest customer insights, create new business applications and enable digital transformation within sales and marketing.
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:
These days it takes much more than a lucrative salary to build a top-notch analytics team. With more than 25 years of analytics experience, Joe DeCosmo, CAO of Enova International, has experienced the shift firsthand. But by forging a close partnership with Enova's Talent Management team, Joe and his team have figured out the right combination of innovative recruiting techniques and challenging work assignments and career growth opportunities to attract and retain the right people.
Hadoop is rapidly being adopted by enterprises as a cost effective way to to ingest, analyze and derive business value out of an ever increasing deluge of data. Data from traditional structured sources (OLTP, existing DB's of all kinds) and more so from new, unstructured sources (social media, video, etc). As business requires more 'real time' response to customers, how can enterprises re-envision their Hadoop architectures? EMC/Pivotal/VMware (the Federation) are exploring how Open Source projects like Spark/Shark, Tachyon and new in-memory platforms can be blended with existing virtualization and SDS systems to unlock further value.
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.
Traditional data warehousing is complex, slow work, relying on multiple brittle ETL steps. This session is a practitioner led case study and QA on the emerging paradigm of the Virtual Data Warehouse. Having implemented a traditional enterprise data warehouse, Compassions DW team quickly recognized the potential of data virtualization. Leaning heavily on trailblazing work at a Global 50 energy company and by the man who wrote the book on SQL, Compassion replaced its traditional DW with the Ministry Information Library, an industry award winning architecture producing great insight and happy customers. Come and hear Compassions story and ask your questions.
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.
Big data is everywhere. You cant avoid being exposed to discussions around big data, and the analysis of it, on a regular basis. The downside of this attention is that there is a lot of hype and misinformation in the marketplace. Many companies are confused about how to get started, what actions to take, and what pitfalls to avoid. Based on content from the popular books Taming The Big Data Tidal Wave and The Analytics Revolution, this talk will provide an overview of important themes to understand regarding big data. The talk will address technological, organizational, and cultural points that must be considered. Most importantly, the talk will aim to provide attendees a solid direction to take their big data initiatives.
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.
As growth continues with geo-location targeting and visitation attribution through mobile devices, MapQuest looks to take advantage of one of the most unique consumer behaviors it obtains from its userbase - where are they planning to go (aka routes). Whether its going to the new store at the mall or to a relative's new house, we understand where the start, where the plan to go, and where they go before they get there when they are using. This creates unique and dynamic opportunities in the marketplace to reach our valuable MapQuest mobile audience.
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.
Inaccessible or non-integrated data can derail, postpone or cancel BI & analytical efforts. This encourages siloed one-off work-around tactics leading to ungoverned use of data causing inaccuracies, inconsistencies, and incompleteness. We'll discuss how Data Virtualization provides immediate & performant access to diverse data sets to enable faster time-to-market and more focused utilization of data scientists and analysts.
Big Data initiatives have become a reality among almost every company today. However, what we have seen is lots of initiatives have become just science projects and did not deliver on early expectations.
This panel discussion will focus on how to use data and analytics to drive true business success and show some real examples of companies and individuals who made a difference.
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:
Data volume, data variety, and data velocity have all grown exponentially over the last few years, the so-called Big Data explosion. And while this increased organizational focus on data, the information it contains, and the insights that can be gleaned from it promises tremendous opportunity, that opportunity isnt achieved without overcoming significant challenges. Whether it be the increased need for better data quality (an issue unresolved from the small data days), more efficient and effective data management, answering questions around data ownership vs. stewardship, or even increased regulatory pressure as a result of data security and data privacy, this increased focus on data has created an increased need for Data Governance. Join our panelists as we discuss the thorny issue of Data Governance: what it is, how it works, why you need it, and who should be responsible for it.