Machine learning (ML) is on the rise at businesses hungry for greater automation and intelligence with use cases spreading across industries. At the same time, most projects are still in the early phases. From selecting datasets and data platforms to architecting and optimizing data pipelines, there are many success factors to keep in mind. The advantages that ML offers organizations—the ability to automatically build models that can analyze huge volumes of data and deliver lightning-fast results—have also led to a growth in the availability of both commercial and open source frameworks, libraries and toolkits for engineers. Attend this workshop for a hands-on course in the enabling technologies, techniques, and applications you need to know to succeed in today’s environments.

DataOps has emerged as an agile methodology to improve the speed and accuracy of analytics through new data management practices and processes, from data quality and integration to model deployment and management. By leveraging automation, data democratization, and greater collaboration among data scientists, engineers, and other technologists, DataOps can help organizations improve the time-to-value of their data. Attend this workshop to hear about the key supporting technologies, real-world strategies, and success stories and how to get started on your DataOps journey.

Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends, may be the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. Extracting actionable knowledge from all your data to make decisions and predictions requires a number of skills, from statistics and programming to data visualization and business domain expertise. Attend this workshop for a deep dive into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.

A new era of cognitive computing is unfolding, and its impact is already being felt across industries, from preventative maintenance at manufacturing plants and patient diagnosis at hospitals to the rise of sophisticated chatbots ready to assist us across the connected world. The goal of cognitive computing is straightforward: to simulate human thought processes in a computerized model. However, building cognitive systems and applications that can perform specific, humanlike tasks in an intelligent way is far from easy. Attend this workshop to get a full understanding of how cognitive computing works, popular use cases, and best practices IT leaders and practitioners can apply today.

Stonebraker focuses on the current market for Big Data products, specifically those that deal with one or more of “the 3 V’s.” On the one hand, the Volume problem for business intelligence applications is pretty well solved by data warehouse vendors. However, upcoming data science tasks are poorly supported at present. On the other hand, there is rapid technological progress, so we need to stay tuned. In the Velocity arena, recent “new SQL” and stream processing products are doing a good job, albeit with some storm clouds on the horizon. The Variety space has a collection of mature products, along with considerable innovation from startups. He identifies opportunities, particularly those enabled by possible disruption from new technology. And then there’s that 800-pound gorilla in the corner.

While organizations have dramatically more data readily available, few are leveraging this data for true competitive advantage. McKinsey found that data-centric companies are driving a 9-fold increase in customer loyalty and an almost 20-fold increase in customer profitability. As data and analytics leaders, you have the opportunity to drive organizational focus on identifying, curating, and leveraging valuable data to support better strategic decision making. Levitt shares information management frameworks and anectotes that highlight the value of thinking outside box, discusses critical success factors, and recommends specific actions to improve your organization's competitive advantage.

Lynda Partner, VP Analytics at Pythian and her team of big data and analytics professionals work to solve the toughest data challenges for their clients. As veterans in the continually-evolving big data space, her team has been helping clients break down their data silos by bringing together data from disparate sources, and enabling use cases from BI to ML. When it comes to big data they’ve seen it all. In this session, you will hear how data professionals like you are dealing with the most challenging issues with data and keeping up with innovations. Learn through real-world examples how you too can be ready to address tomorrow’s opportunities with ever-advancing cloud analytics technologies and emerging practices, — in other words, what’s coming and how can you maneuver today to take advantage.

There is a conflict going in the IT industry pitting Big Data against “legacy” data architectures. However, traditional architecture and technologies and newer Big Data approaches each offer advantages.

Find out what you need to know regarding structured, unstructured, and semi-structured data; hybrid integration and data engineering; and different analytical uses. Learn about the technologies to use for different projects, including relational, columnar, and in-memory. Topics include the must-have underlying foundational concepts for every project; high-level architectural design to pull it all together; the best and pragmatic practices to ensure success; how to avoid deadly data and integration silos; and how to prevent data swamps, data shadow systems, and spreadmarts.

To support a modern data architecture and approach to analytics, data integration strategies now support on-prem, cloud, and hybrid deployments. Meanwhile, streaming architectures featuring change data capture (CDC) technology are rapidly being embraced to process data in motion. This session discusses the new requirements and best practices to be successful in enabling a real-time enterprise, whether in a data lake, via streaming technology, or in the cloud.

There are game-changing technologies emerging in data management. But to win at the new world of Big Data, you have to know the changing rules.

The world of data management and administration is rapidly changing as organizations digitally transform. Mullins examines how database management systems are changing and adapting to modern IT needs. Understanding the trends occurring now and on the horizon is critical to being prepared for the rapidly changing data landscape. This presentation looks at cloud, analytics, NoSQL, IoT, in-memory, and DevOps and examines what is happening with DBAs and their roles within modern organizations. Mullins backs up the trends with references and links where appropriate.

It was hard enough to manage IT infrastructures when everything was on-premise only. But today, with combined on-premise deployments, SaaS, and hybrid cloud scenarios, there is uncertainty about the proper way to license software in these very complex environments.

Keeping software in compliance is a more significant challenge today than ever before. Sorting through all the FUD (fear, uncertainty, doubt) and getting straight answers from the vendors on the proper way to license software in this complicated world is nearly impossible. Making matters worse is the fact that many software vendors have turned to software license audits as an easy way to generate additional revenues. This session covers current software licensing trends, important lessons learned from the real world, and the steps every organization should take now to avoid becoming a victim of a software license audit whose real purpose is to generate revenue.

Data is flowing into organizations from a previously unimaginable array of sources and at unprecedented speed and volume. This means that the challenges of cleaning, deduplicating, and integrating data are increasing.

A cloud-native data platform may be the best way for organizations to cost-effectively deliver on the promise of better insights and more intelligent systems through data. Danil Zburivsky covers how a cloud integration approach can lead to better data governance and more accurate analysis and ensure consistency of data across systems, as well as the best practices for cloud data integration and how a cloud data platform breaks down data silos within the organization. The presentation also looks at how one client successfully took its global sales data to the cloud to uncover new opportunities.

From the perspective of an experienced engineering thought leader, Paul Wolmering, VP Worldwide Sales Engineering, Actian Corporation, will deliver a deep dive into Actian’s newly launched Gen III cloud data warehouse. Learn about key considerations for building a fully managed, multi-cloud data warehouse with federated query capabilities, that’s built for hybrid data. Understand key success factors for migrating to columnar analytics to gain actionable insights from an operational data warehouse. Learn what it takes to deliver insights from real-time data economically and at scale with hybrid data regardless of location, in the cloud, on-premises or both.

There are significant benefits offered by IoT, but also new threats and dangers. Do we really understand the challenges posed by all these connected “things”?

With the Internet of Things (IoT), essentially everything becomes a computer. This means that everything can be hacked—including cars, home appliances, medical devices, and more. This presentation provides examples of IoT hacks and the consequences of not getting security right as we move forward in the world of smart and connected machines.

AI and Big Data offer seemingly unlimited potential for organizations to better understand their customers, make more informed decisions, and address challenges with greater agility. It’s important to understand the choices available to achieve the best outcomes.

The intersection of AI and Big Data provides the ability to deliver more targeted, timely, relevant insight in a pervasive and intuitive manner. However, delivering that simplicity requires an analytics and data ecosystem that is markedly more complicated than 10 years ago. To that end, effectively deploying analytics from BI to AI is a now an exercise in portfolio management—complete with discrete customer segments, diverse data environments, development methods, and a wide spectrum of deployment options. This session puts the diverse—and growing—landscape of analytics capabilities from BI to AI into context.

In spite of the buzz around AI, organizations are struggling to build data science teams that deliver value on the ground. This talk presents the three distinctive phases of growth for data science teams, highlighting potential challenges and suggesting a standard framework of guidelines to successfully navigate this evolution. Vastly different approaches are needed in each stage of maturity to tackle aspects such as strategic direction, project framework, the mix of skills, hiring strategies, and fostering of a data culture.

Emerging technologies such as AI, IoT, and machine learning are changing what is knowable about customers. At the same time, the frequency of data misuse is leading government entities and individuals to demand higher standards of accountability.

This presentation explores the issues around modernizing security and governance, as well as what it means to deliver transparency and what users actually expect. It also covers the need to manage accountability within systems of multiple decision-makers; why it is necessary to build fairness into the system to overcome bias, discrimination, and enable diversity; and the need to address expectations of privacy and appropriate use of data.

Due to exponentially growing data stores, organizations today are facing slowdowns and bottlenecks at peak processing times, with queries taking hours or days. Some complex queries simply cannot be executed. Data often requires tedious and time-consuming preparation before queries can be run. This session will demonstrate how the power of GPUs can help conquer these challenges, enabling data professionals to rapidly analyze more data on more dimensions, for previously unobtainable business insights.

Organizations in all industries are under pressure to take advantage of Big Data and newer data sources for real-time decision making in mission-critical environments. New technologies provide opportunities to gain insight into the future.

How does an organization evolve from an application-centric to a data-driven enterprise? This presentation covers how Fannie Mae embarked on a major transformation journey to modernize its data infrastructure, transitioning from legacy data platforms to more integrated and scalable architecture to capitalize on the growing opportunities of the analytics economy and generate substantial business value, internally and externally.

As Big Data grows and evolves, your enterprise faces both challenges and market-disrupting opportunities to analyze and manage larger data volumes for business value. But with seemingly endless commerical, open source, and "as-a-service" offerings hitting the market each week. How do you choose the right mix of technologies and avoid creating an accidental architecture that will limit you from future innovaation? How are organizations actually achieving true bottom-line benefits from their Big Data initiatives? Learn how to adopt an effective and agile approach to Big Data analytics.

With the vast quantities of data flowing into organizations, the job of cleansing and validating data is only becoming more difficult. In order to gain the kind of insights and outcomes that organizations seek, new processes and technologies must be deployed.

The (IRS) Compliance Data Warehouse (CDW) is an analytical data warehouse used for research purposes. It empowers researchers to spend more time on analytics and less on data wrangling. To ensure all data is loaded properly, consistent, well-thought-out validation steps must be included in the ETL process. This presentation offers a case study of accomplishments and lessons learned (since FY 2016), including the data quality issues identified by CDW users (data stewards), and takeaways for attendees on how to improve decision making.

Best-selling author David Weinberger previews his new book on everyday chaos.

Ultimately, machine learning’s most important effect may not be in the benefits its use brings, but how it is implicitly transforming our understanding of how the world works and our most basic strategies for dealing with the future. From Newton on through the Computer Age, we have assumed that the universe is ruled by a relative handful of laws that are the same everywhere and that are simple enough for us to understand. But machine learning shows us a world of motes of data in networks so dense with connections and so delicately balanced, we sometimes can’t understand them. This sort of model of the world is changing not only our strategies, but our moral sense, our ideas about meaning, and even what makes humans special.

A new data platform approach is needed to extend the data warehouse and address the vast quantity and variety of data flowing into organizations, much of it unstructured.

The data warehouse is experiencing pressure from increasing data volumes, more users, and tight budgets—a triple threat to its ongoing existence and value. In addition, new data types are coming into play. This increased pressure means the old-school data warehouse may not be delivering insights at the speed of business. There are a number of alternatives to meet modern analytics infrastructure needs. This presentation outlines in detail why a modern data platform is required to deliver on new analytics demands.

In a data fabric, the data discovery and integration layer maps all enterprise data in its original business context so that users can find and blend data from diverse siloed sources into analytic-ready data sets on an on-demand basis. Join Cambridge Semantics CTO Sean Martin to hear how companies are using data discovery and integration solutions to exploit enterprise data fortransformational analytic and machine learning projects.

With the abundance of data stored in data lakes, finding the relevant information is increasingly challenging, particularly in light of the many formats in which the data apears.

With the realization of the power of data lakes, more and more organizational data in various formats and standards are being made available there. Given this plethora of information, it is becoming increasingly daunting for users to search for the data of interest to them with the use of conventional data analytical tools. A combination of Data Discovery tools, making use of semantic search and concept search, brings in the right blend of capability, enabling "comparison shopping" between seemingly similar datasets, and allowing end users to evaluate the best fit while facilitating the discovery and reuse of all available information and data assets, both internal and external.

As part of our deep dive into data lakes, our panel of experts contemplates success factors, failure avoidance, and new developments. Join us for an invigorating discussion.

Data lakes are highly appealing as they provide the capacity to support all types of data and maintain it in its original format for future purposes. Before diving in, it’s important to be aware of the components of a successful data lake implementation.

Marmaray, Uber’s general-purpose Apache Hadoop data ingestion and dispersal framework and library, was open-sourced in 2018. Marmaray was envisioned, designed, and ultimately released in late 2017 to fulfill the need for a flexible, universal dispersal platform that would complete the Hadoop ecosystem by providing the means to transfer Hadoop data out to any online data store. Before Marmaray, each team was building its own ad hoc dispersal systems, which resulted in duplicated efforts and an inefficient use of engineering resources.

Rafael shares how an ad-tech company moved from a DW to a fully functional data lake that processes over 400,000 events per second without writing one line of code.

New frameworks and platforms are enabling organizations to improve time-consuming processes and meet enterprise requirements for high performance.

Apache Gobblin is a distributed data integration framework for both streaming and batch data ecosystems. This presentation covers how Gobblin powers several data processing pipelines at LinkedIn and use cases such as ingestion of more than 300 billion events for thousands of Kafka topics on a daily basis, metadata and storage management for several petabytes of data on HDFS, and near real-time processing of thousands of enterprise customer jobs. It also looks at the key Gobblin features that help LinkedIn build and run these data pipelines at extreme scale.

Artificial intelligence (AI) had the potential to completely revolutionize how we do business and increasingly affects people’s daily lives.

Depending on who you talk to, AI will either enable massive productivity gains from your employees or replace them entirely. Hype aside, AI is coming, and companies need to understand how to harness it. Despite the promise of “plug and play” technology, real AI requires varying degrees of information architecture (IA), knowledge engineering, product and content architecture, and high-quality data sources to be effective. 

The power of machine learning is particularly evident when used to predict events in the real world.

Machine learning (ML) has become top of mind for many businesses. Wilde shares his experience and insights about ML in the real world.

The customer experience (CX) is prime territory for employing AI technologies.

Let’s take a look at enhanced customer experiences for hospitality consumers delivered via virtual concierge services that provide resort guests with personal and contextual hospitality services. Built upon mobile use and location data, bot services help customers with a variety of services, ranging from in-room amenities and dinner reservations to event tickets. Don Spaulding provides an overview of the technical solution and powerful results of this virtualized solution.

The combination of Big Data with AI technologies creates both challenges and opportunities. Identifying the factors that turn challenges to opportunities leads to success.

With edge computing becoming a thing, AI on-the-edge is quickly following suit. It unlocks a whole new world of possibilities, including predicting customer needs before they even know them. But edge AI seems like it’s only a game for the most cutting-edge companies like Apple, Amazon, or Tesla, to name a few. Traditional enterprises aren’t really embracing it out of fear it may cost too much or due to uncertainty about the potential ROI. To tap into this opportunity, organizations don’t need to choose a risky “all in” approach; a small iterative approach reduces the risk while ensuring your edge AI projects aligns with your overall business strategy. Join this session to learn how to apply a Minimal Viable Prediction (MVP) approach to your next edge AI project.

Everyone’s talking about machine learning, but we hear much less about how to put it into practice.

Only 10 years ago, you needed access to extensive academic and computing resources to make use of machine learning (ML). Fast-forward to today, and we’ve seen revolutionary changes in the hardware and software that are making ML accessible for any developer or data scientist. Whether you’re completely new to ML or you’ve already trained and deployed your own model from scratch, Google Cloud Platform has a variety of tools to help you start using ML right now. Sara Robinson starts with the basics: how to use a pre-trained ML model with one REST API call. Then she explains how to use your own dataset to customize a pre-trained model with transfer learning, how to build your own model from scratch with TensorFlow, and how to train and serve it in the cloud with GCP.

Learn about next-generation cloud data warehousing and what it takes to deliver insights from real-time data economically and at scale with hybrid data regardless of location, in the cloud, on-premises or both, while you enjoy Boston's finest deep fried pastries!

Elsevier is a 130 year old company with a long legacy of providing quality content that serves professionals in their fields. Its digital transforming is aimed at transforming that content into actionable intelligence for users. However, digital transformation is not just a matter of updating legacy systems and workflows, it also requires the capacity to transform the entire business; from pricing models to company culture. In this talk, Deus explores Elsevier’s journey to transform its business so it is able to incorporate advanced technologies like machine learning and AI technology to better serve customers.

We find ourselves continuously copying, transforming, and aggregating data into various large scale, complex, proprietary systems for the purpose of Digital Transformation in order to gain some competitive edge. Unfortunately, this process ends up exacerbating the problem by creating more silos, complexity and making the process of extracting knowledge more difficult overall. Deyette reviews where we are, how we got here, and what must come next with regard to leveraging Big Data Analytics.

Today, there are more opportunities than ever for organizations to achieve new levels of success by capitalizing on data and analytics. Despite this fact, many organizations struggle to build, grow, and reinforce their analytics capabilities.

Understanding that no two organizations are the same, and there are no “one-size-fits-all” solutions, Booz Allen uses a framework known as ADAPT+C to address an organization’s most critical analytics problems with a “best-fit” approach. This presentation covers the six components to ADAPT+C and how this framework can be applied to organizations of all types and sizes to help guide them in their data-driven journeys.

The role of a Database Administrator has changed significantly over the past few years. The continued adoption of open source databases, cloud and/or hybrid architectures, and non-relational data sources have added significant complexity to the modern data center, all while organizations strive to become more agile through DevOps. Hall focuses on these trends, how they've impacted DBAs, and best of breed solutions being used by enterprises today.

Modern data architectures address the need of business agility required in the modern age. However, moving from a traditional data architecture to a modern architecture has many challenges.

This presentation helps to define a modern data architecture and takes an in-depth look at how to build an end-to-end modern data platform from the plethora of choices and tools available, including when and why to use cloud implementations. It also focuses on application architecture and discusses some real-world examples of click stream analysis, customer 360, fraud detection, and scoring, in addition to data platform optimizations for business applications.

We continue to hear that "data is the new oil." How are data driven enterprises capturing, managing, and using this data as a competitive advantage? What challenges do they face with legacy technologies that have not been optimized to handle these data management activities? Learn about these challenges, what the optimal solution would look like, the benefits a true data management platform can provide, and how Actifio can help.

There are new technologies that contribute to speed and scale of a modern data platform. But as data size and complexity increase with Big Data, data quality and data integration issues must still be addressed.

Similar to many other mission-critical data management situations, clinical trials are fraught with missteps and data quality issues. This presentation focuses on the architecture of a modern, cloud-based, real-time data integration and analytics platform that ingests any type of clinical data (structured, unstructured, binary, lab values, etc.) at scale from any data sources. Attention is paid to assembling the architectural building blocks of a modern data platform, including the benefits of a “serverless” data pipeline, cloud architecture and deployment, microservices, continuous integration and deployment pipeline, platform scalability, data governance, and master data management.

The ability for knowledge graphs to amass information and relationships and connect facts is showing potential for a range of use cases.

For publishers, content is the most important data asset. However, journal articles (and other content) are expressed in formats ideally suited for transmittal and display—not as data for analysis. By leveraging the structure inherent in scholarly published XML content, we can create structured data in RDF for analysis. Further, we can link out to external resources as linked data to further enrich the content-as-data assets and provide a rich analytical environment.

The holy grail of marketers is to attain a 360-degree view of customers in order to increase brand loyalty and understand preferences and purchases for a more personalized approach. Technologies are available to help make that goal a reality.

Brand and marketing teams often have questions about how to segment and classify users based on various attributes. This is used to identify cohorts and consequently run campaigns with strategic plans. This presentation shows how a data analyst can collect relative data and give sense to analysis output.

If you take a step back, a 360-degree view begins with a successful integration strategy--a really good one! In this talk, Benedetti shares two different stories, one in B2B and another in B2C, on how CloverDX helped enable customers to achieve a 360-degree view without too much of an investment and left them empowered to bring in more data as they grow in the future.

The demand to become a data-driven business with a competitive edge in the rapidly changing economy is greater now than ever. With the constant explosion of data within the organizations and across the industry, companies are increasingly moving toward leveraging data for business decisions and analytics.

Wayfair, one of the world's leading home furnishing platforms, has undergone immense and rapid growth, analyzing data all along the way. The successful creation of a retail holiday pushed its systems to the limit. "Way Day," as it's affectionately known, was full of highs and lows, and showed the team it needed to transform not only to the cloud but also to a high performance, low latency database. Wayfair lives and breathes on data-driven decision making that impacts the entire customer experience. Data science has been the core driver of Wayfair's success. The Aerospke NoSQL database enables a highly scalable, fault tolerant, and performance driven environment for customer intelligence, product recommendations, and real-time marketing.

With the rise of Big Data, IoT, and AI, useful sources of data are emerging and new opportunities are being created.

Location data is critical to closing the gap between the online and offline world. By leveraging location data, marketers can create impactful moments to influence the behavior of the consumer. This session reveals how location data is the cookie for the real world and how it empowers marketers to run unique campaigns to drive real results in a highly measured way.

Blockchain, the distributed ledger technology, is expected to impact a diverse range of industries, from agriculture to accounting to healthcare. From guaranteeing the authenticity of products to safeguarding transactions, blockchain holds wide-ranging potential.

Blockchain technology has emerged as an innovative and easy-to-adopt approach to improve anti-counterfeit measures in different industries and delivers a significant positive social impact. It offers a transparent environment where it is impossible to duplicate products and there is no need to rely on trust alone. This presentation shows how to use a blockchain with smart contracts to track products at every step of the production and sales process and make this information available to anyone.

DataOps is an emerging set of practices, processes, and technologies for building and enhancing data and analytics pipelines to better meet the needs of the business.

Join this presentation to explore the five key steps necessary to be successful with DataOps, including the process and cultural shift required. The discussion also covers the benefits of enabling DataOps’ success, such as improved productivity, streamlined and automated processes, increased output, and higher collaboration across teams. Learn how to better manage data flow across the data lifecycle—from ingestion to provisioning to analytics; derive tips from use cases involving data lakes, cloud, and data warehousing for better business insights; and increase collaboration, productivity, and business value.

The list of failed Big Data projects is long. They leave end users, data analysts, and data scientists frustrated with long lead times for changes. Bergh illustrates how to make changes to Big Data, models, and visualizations quickly, with high quality, using the tools teams love. Synthesizing techniques from DevOps, Demming, and direct experience shows you how to succeed with DataOps today.

The use of containers—which enable applications, data, dependencies, and runtimes to be housed within a portable environment to support greater flexibility—is becoming more widespread.

Container usage is now being adopted by organizations of all sizes, from small startups to companies with huge, established microservices platforms. This presentation is aimed at helping practitioners navigate the minefield of database containerization and avoid some of the major pitfalls that can occur. It covers considerations such as container configuration and homogeneous versus heterogeneous node types; data resilience, resources, and storage; cluster upscale, downscale, and upgrade; and data locality and networking.

We’re at a unique point in time when fundamental changes in data management in the enterprise, the emergence of new technologies, and the volume and value of enterprise data combine into a massive opportunity to create next-generation data engineering pathways. Marinelli highlights the converging factors that affect why DataOps is gaining traction in the marketplace today, how it’s different from earlier generations of data curation, and what the key components are to implementing DataOps.

Data wrangling skills and capabilities are critical and a true business imperative. Organizations that are unable to derive true business insights from data will be left behind in the ever-changing and fast-moving digital economy.

All the information that is needed to find and stop bad actors from entering our financial system already exists and is available to you today; it’s just buried in terabits of messy, unstructured data all over the internet. For those performing investigations and evaluating risk, this needle in a haystack-of-needles problem is huge and growing: Unstructured data already dominates the web (growing exponentially year over year), and the traditional technology these departments use cannot keep up. Recent developments in natural language processing technology (NLP), the field of AI that focuses on human language, have, for the first time, made it possible for automated systems to find and deliver identity-relevant intelligence hidden in unstructured textual data. These innovations unlock a new world of actionable insight, providing much-needed ammunition in the fight against fraud, money-laundering, financial crime, and terrorism.

There are many ways to implement ML projects, but not all of them work out as planned.

EnCata’s work is organized according to lean principles, which mean reducing delivery time and eliminating time and money losses, connected with knowledge transfer and materials logistics. We used machine learning to create a neural network as a means of monitoring the working tool in production.

Deep learning brings many opportunities that businesses can use to augment current processes.

Using ML-based deep neural networks for prescriptive analytics via recommendations, followed by predictive analytics using the results of recommendation is the focus of Lakshman Bulusu’s presentation. He explains the pragmatics of using R and provides an industry use case as a proof of concept about how business can leverage this to augment BI at an enterprise level. He includes a demonstration of how AI Meets BI via integration with a BI platform using Oracle/Tableau.

Think big, start small, and just get going is an excellent approach to take full advantage of AI’s transformational technologies.

AI is already having a significant impact for the U.S. government, including Defense and Intelligence Community use cases, and is also providing game-changing capabilities for global enterprises in a range of industries, including financial services, life sciences, and technology. Guarino provides real-world examples of how AI is driving measurable benefits in a range of industry sectors. She discusses the importance of Explainable AI to regulated industries like financial services and healthcare, where being able to justify the reasoning behind algorithmic decisions is essential.

As we move into a new computing era, technology has outstripped society in its ability to upend labor markets and industries, as well as personal lives.  This panel discussion by researchers and industry experts, and moderated by the Cognitive Computing Consortium, presents a number of ethical and legal issues that computing advances have raised.  Areas covered include: security, privacy, decision making by humans vs. machines, self driving vehicles, effects on labor markets and industries.  Presenters will avoid hype and discuss practical approaches to these issues.

Companies are not lacking in technology options; in most cases, more advanced technologies exist than can be absorbed into the organization all at once. As data and analytics leaders, you need to determine how to make the biggest business impacts with advances in AI and ML for analytics, enable self-service with governance, and support BI and data engineering processes with the data lake. To establish this solid enterprise foundation, it is essential to recommit to data management principles and prioritize platform technologies and ecosystems accordingly. In his closing keynote, O’Brien shares a clear path forward based on the evolving concepts in data and analytics within the context of an enterprise-scale data strategy.