Today’s customers expect personalized experiences and there are a plethora of available martech solutions that claim to have the tools to help. And while marketers are able to collect a mass amount of data, it’s becoming increasingly challenging to be able to act on that data, and have one, central repository to be able to make informed decisions and drive more meaningful customer experiences.
A new martech solution has recently appeared into the space that does just that - the Customer Data Platform (CDP). In this webcast, you can expect to learn:
- How these purpose-built products assemble and distribute customer data
- Why CDPs are faster, easier to use, cheaper and more flexible than other solutions
- What problems a CDP can help the marketer solve
- The key ways to using a CDP in driving a business’s digital transformation
David Raab, Founder of the Customer Data Platform Institute
Adam Corey, VP of Marketing at Tealium
In this keynote Ashish Thusoo, CEO of Qubole, discusses the gap that enterprises face today when activating their big data. He makes a case for the shift that organizations need to make towards a big data activation strategy in order to put their data assets to use for differentiating and achieving business objectives. The session also covers key elements of big data activation supported by usage trends of Qubole's cloud-native big data activation platform. He presents various ways that enterprises can use to measure their own activation readiness, and demonstrate why Qubole provides the right approach to big data activation.Read more >
Learn about the advantages that come with bringing your data onto one platform in this on-demand webinar, and get a glimpse into Periscope Data’s vision for a unified data platform.Read more >
As data is growing at an exponential rate, organizations are increasingly looking to leverage streaming data from mobile devices, wearable technology and sensors for real-time processing and analytics. Gartner estimates that, “By 2020, 70% of organizations will adopt data streaming to enable real-time analytics.” However, implementing real-time data ingest, processing and delivering insights at scale requires infrastructure with zero latency and easy access to information when it is required.
In the webinar, we’ll discuss:
- Adopting Modern Data Lake with the Hortonworks Data Platform (HDP)
- Accelerating real-time data analytics with Hortonworks Data Flow (HDFTM) and Attunity to build a data lake
- Solving challenges with real-time data ingest and managing data in motion workloads
Join subject matter experts from IBM and Hortonworks for a joint webcast to help you accelerate real-time data analytics and manage your data workloads efficiently.
With new technologies such as Hive LLAP or Spark SQL, do you still need a data warehouse or can you just put everything in a data lake and report off of that? No! In the presentation, James will discuss why you still need a relational data warehouse and how to use a data lake and an RDBMS data warehouse to get the best of both worlds.
James will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. He'll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution, and he will put it all together by showing common big data architectures.
The Under Armour built the world's most comprehensive health and wellness application: the Connected Fitness Data Platform. It consists of event streaming pipelines and processing using big data technologies like Hive, Presto and Spark to derive the insights needed to keep their users fit and healthy. Discover their step by step process.Read more >
Everyone is talking about cloud-based data platforms, but a lot of the talk is more questions than answers. What’s driving the move to the cloud? What are the implications for data management and analytics? How can I make a hybrid environment succeed? Who are the real players in the market and who should I pay attention to?
Join Matt Aslett, Research Director for the Data Platforms and Analytics Channel at 451 Research and Bruno Aziza, CMO at AtScale for this deep dive into everything* you need to know about data in the cloud.
Join this webinar to learn:
What technologies to support when deploying a hybrid environment.
About the implications on data management and how it affects your current big data environment.
About the implications of the separation of compute and storage for analytics workloads.
About the technology and business drivers to consider on this journey.
Every CEO aspires to create a data-driven culture that can activate 100s or 1000s of users and petabyte-scale data to continuously deliver true business value. This keynote panel explores the journey of 4 companies: Comcast, Qubole, Fanatics and MediaMath, that have chronicled their successes and challenges in two books by O’Reilly Media about Creating a Data-Driven Enterprise. The panelists talk not just about their technology strategy and choices but also how data-driven insights are powering their business and transforming the competitive dynamics of their industry.Read more >
From chat bots, to recommendation engines, to Google Voice and Apple Siri, AI has begun to permeate our lives. In this keynote, IBM's Sumit Gupta demystifies what AI is, presents the differences between machine learning and deep learning, explains why the huge interest now, shows some fun use cases and demos, and then discusses use cases of how deep learning based AI methods can be used to garner insights from data for enterprises. Sumit also talks about what IBM is doing to make deep learning and machine learning more accessible and useful to a broader set of data scientists.Read more >
There was a time when the Enterprise Data Warehouse (EDW) was the only way to provide a 360-degree analytical view of the business. In recent years many organizations have deployed disparate analytics alternatives to the EDW, including: cloud data warehouses, machine learning frameworks, graph databases, geospatial tools, and other technologies. Often these new deployments have resulted in the creation of analytical silos that are too complex to integrate, seriously limiting global insights and innovation.
Join guest speaker, 451 Research’s Jim Curtis and Pivotal’s Jacque Istok for an interactive discussion about some of the overarching trends affecting the data warehousing market, as well as how to build a next generation data platform to accelerate business innovation. During this webinar you will learn:
- The significance of a multi-cloud, infrastructure-agnostic analytics
- What is working and what isn’t, when it comes to analytics integration
- The importance of seamlessly integrating all your analytics in one platform
- How to innovate faster, taking advantage of open source and agile software
We look forward to you joining us.
The Pivotal Team
Today, big data is enabling the advanced analytics that companies have dreamed of for driving their business. And as forward-thinking companies take advantage of big data and advanced analytics to drive digital transformation initiatives, it is forcing the laggards to realize that they will have to do the same if they want to survive.
The generally accepted architectural model for harnessing big data is a data lake. But data lakes, if leveraged simply as cheap storage within which to dump data, will inevitably disappoint. As the saying goes, garbage in, garbage out. Data lakes present unique challenges that must be dealt with if that big data set is going to be turned into actionable information.
So what does it take to succeed with a data lake? Why do some organizations get real value out of big data, while others struggle?
In this webinar, Matt Aslett, Research Director of Data Platform and Analytics at 451 Research and Kelly Schupp, VP of Data-driven Marketing at Zaloni, will discuss ideal data lake use cases such as Customer 360 and IoT. They will also discuss Zaloni’s data lake maturity model with which the data-eager company can chart its ideal course and roadmap.
As big data has exploded, the ability for companies to easily leverage it has imploded. Organizations are drowning in their own information, unable to see the forest through the trees, while the big players consistently outperform in their ability to deliver a great customer experience, faster, cheaper…As a result, the vast majority of companies are scrambling to catch up and become more agile, data-driven, to use their data more effectively so they can attract and retain their elusive customers...
Join 451 Research and Treasure Data to learn how to enable your line of business team to own their own data (instead of relying on IT) to be able to:
- deliver a single, persistent view of your customer based on behavior data
- make that data accessible to the right people at the right time
- Increase organizational effectiveness by (finally) breaking down silos with data
- enable powerful marketing tools to enhance the customer experience
Today's enterprises need broader access to data for a wider array of use cases to derive more value from data and get to business insights faster. However, it is critical that companies also ensure the proper controls are in place to safeguard data privacy and comply with regulatory requirements.
What does this look like? What are best practices to create a modern, scalable data infrastructure that can support this business challenge?
Zaloni partnered with industry-leading insurance company AIG to implement a data lake to tackle this very problem successfully. During this webcast, AIG's VP of Global Data Platforms, Carlos Matos, and Zaloni CEO, Ben Sharma will share insights from their real-world experience and discuss:
- Best practices for architecture, technology, data management and governance to enable centralized data services
- How to address lineage, data quality and privacy and security, and data lifecycle management
- Strategies for developing an enterprise-wide data lake service for advanced analytics that can bridge the gaps between different lines of business, financial systems and drive shared data insights across the organization
This 1-hour webinar from GigaOm Research brings together leading minds in cloud data analytics, featuring GigaOm analyst Andrew Brust, joined by guests from cloud big data platform pioneer Qubole and cloud data warehouse juggernaut Snowflake Computing. The roundtable discussion will focus on enabling Enterprise ML and AI by bringing together data from different platforms, with efficiency and common sense.
In this 1-hour webinar, you will discover:
- How the elasticity and storage economics of the cloud have made AI, ML and data analytics on high-volume data feasible, using a variety of technologies.
- That the key to success in this new world of analytics is integrating platforms, so they can work together and share data
- How this enables building accurate, business-critical machine leaning models and produces the data-driven insights that customers need and the industry has promised
- How to make the lake, the warehouse, ML and AI technologies and the cloud work together, technically and strategically.
Register now to join GigaOm Research, Qubole and Snowflake for this free expert webinar.
The idea of the data lake was alluring; the means to having data at your fingertips, filtered, profiled, secure and business-ready for data consumers to rapidly derive higher-levels of business value. However, this widely adopted concept didn’t come with an instruction manual.
The past few years have been turbulent times for enterprise data and analytics. In this live discussion, the presenters look beyond the lake, discussing the combination of self-service data preparation and data management and governance as one; a truly functional data marketplace or “data bazaar”. In addition, they will touch on other key enablers fueling the adoption of the self-service marketplace, including;
•Data as a service,
•Smart data ingestion – validation/profiling,
•Smart data cataloging/search,
Join David Talby of Pacific AI as he overviews the security and compliance checklist when architecting a data science platform.Read more >
Paul Bruton discusses the move to a holistic approach to next gen data management. Looking at digital transformation strategies, he explains how Hitachi Vantara’s object storage can address common challenges - from cloud complexity to data governance and compliance - with its advanced custom metadata architecture to make data more intelligent.Read more >
If you feel like you don’t trust your data, there’s probably a good reason. It happens all the time; companies implement analytics, customize their solutions and don’t audit the implementation to ensure ongoing data accuracy. This leads to multiple inaccuracies, gaps in tracking, and — even worse — information that’s simply missing. Inaccurate data can send a brand down the wrong path, leading to bad decisions and additional costs for tools and resources that could have otherwise been avoided.
Join and learn:
- What data quality is and why it’s critical to an organization's overall success
- Why your data is crap and how to identify the warning signs of poor data quality
- Best practices to ensure clean and quality data and how to take back control
- And so much more!
The webinar will conclude with a Fireside Chat with live questions from the audience on all things data quality.
Every aspect of the Uber experience is powered by data - everything from in-app ETAs, menu recommendations, and map labeling to driver dispatch and customer support. In this talk, we will focus on the infrastructure and platforms that power data ingestion, storage, streaming/batch analytics, and machine learning for thousands of operators, data scientists, and engineers at Uber.Read more >
Data is the new currency for most organizations and data volumes are continuing to grow at an explosive rate. While the advantage of collection of such large volumes of data is obvious, protection of this data from cybercriminals and malicious actors is becoming increasingly difficult. Conventional security mechanisms are failing and large-scale security breaches, despite increasing security spends, are becoming commonplace. This, along with increasingly stringent regulatory requirements and privacy laws have brought “Data Security” – protection of the data itself whether in motion, in use or in transit into strong focus. While there has been an increasing focus on Data Centric Security, the solution landscape is fractured and enterprises are still struggling to identify and deploy long term solutions with minimal disruption to existing investments and processes. This talk will focus on the current state of Data security and offer pointers to how organizations can embark on a long-term Data Centric journey which truly adds business value.Read more >