In financial services, the top big data analytics use cases include customer analytics to understand customer journey using data from all customer interaction channels, predict and avoid customer churn, and fraud and compliance. The financial and corporate benefits of these use cases range from improving customer retention, to hundreds of millions of dollars in incremental revenue and protection of shareholder value.
In this webinar, learn from big data analytics experts:
- Top 3 use cases in financial services
- The importance of applying the appropriate technologies
- The data driven insights that will give companies a competitive edge
The biggest challenges that organizations face are to determine how to obtain value from big data, and how to decide where to start. Many organizations get stuck at the pilot stage because they don't tie the technology to business processes or concrete use cases.” (Gartner, 9/14)
This session will provide insight into how to build a roadmap and project charter for a big data solution. A solution that is both ready to address your first use case while serving as a platform for your future big data needs. A must for anyone looking to find out how to accelerate strategic initiatives and journey to big data maturity.
Through the analysis of real-life use cases, this webcast will demonstrate the top 3 big data use cases we have seen and how our customers answer these very questions.Read more >
Top 3 Use Cases In Telecommunications North AmericaRead more >
In this video, you’ll learn which use cases attract the most big data attention.
Data warehouse optimization was the leading use case followed closely by customer analysis. IoT uses cases are not on the radar for many organizations right now, but it’s likely that they will predominate big data analytics in the relatively near future. Watch the next video to learn what’s driving choices in big data infrastructure.
Content and Images Source: Dresner Advisory Services Big Data Analytics Market Study; Copyright 2017 -- Dresner Advisory Services
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
In this webcast, learn the top 5 IoT use cases of Datameer customersRead more >
How do you make sure your data is bit correct in the source and target systems? In this video, learn how the Big Data Compare feature in HVR enables you to make sure your data is correct and in sync.
VP of Field Engineering, Joe deBuzna, explains how the Big Data Compare function works in HVR, why it is important for your business, and how it can identify and mitigate errors.
Listen to our interview at Big Data LDN with Wael Elrifai, Director of Enterprise Solutions at Pentaho.
Wael will talk through some use cases for predictive maintenance and how Big Data has impacted these models.
He will also share some tips for people still struggling with Hadoop and will also go over the different ways to embark on an IoT strategy for your organisation.
In a recent survey, less than 20% of CEO's were very satisfied with the value they have recognised from investments in data and analytics. This holds true for Big Data as well. Many organisations have experimented with these technologies and invested in creating data lakes for analytics.
However, these technologies need to find operational use cases in order to drive value to the business. The good news is that the Internet of Things (IoT) is now defining these use cases and new opportunities.
This presentation will use multiple case studies and industry research to provide valuable information to attendees engaged in planning, or researching Big Data and IoT initiatives.
The explosion of data offers enterprises and start-ups alike virtually limitless possibilities for new, innovative data-driven applications. Too often, however, practitioners choose the technology to support new applications before the use cases are fully baked out resulting in poor performance and untapped potential. Join Wikibon Big Data Analyst Jeff Kelly, Applovin Founder and CTO John Krystynak and Aerospike co-Founder and CTO Brian Bulkowski in this hour-long webinar as they discuss why application use cases should drive technology decisions and how Applovin applies this approach to help its customers optimize the mobile experience.
John Krystynak, AppLovin CTO
John co-founded AppLovin in 2011, where he leads engineering and develops mobile advertising technology that enables brands to acquire and re-engage customers on mobile.
Jeff Kelly, Wikibon Principal Analyst
As Wikibon’s lead Big Data analyst, Jeff Kelly applies a critical eye to trends and developments in the Big Data and business analytics markets, with a strong focus on helping practitioners deliver business value.
Brian Bulkowski, Aerospike CTO
Brian has 20-plus years experience designing, developing and tuning networking systems and high-performance Web-scale infrastructures. He founded Aerospike after learning first hand, the scaling limitations of sharded MySQL systems and saw the need for a high performance database that would empower all developers.
Are you looking to improve speed, accuracy and depth of analysis for your expanding and accelerating internal/external data sources?
If so, you need to understand the 5 essential Big Data use cases that are applicable, regardless of your industry. These essential use cases drive the value for any Big Data investment.
Using specific examples we will highlight each of these 5 essential use cases and help you understand:
The truth about driving Big Data
IT challenges addressing Big Data
Real world applications and the value of being driven from Big Data
The German Cancer Research Center (DKFZ) uses self-service big data analytics to radically improve the genomic research process. Their new insights have allowed them to identify better treatment plans for cancer patients.
During this one-hour on-demand webinar, Dr. Fritz Schinkel, head of Fujitsu’s Big Data Competence Center and a Fujitsu Distinguished Engineer, discusses how the combined Datameer and Fujitsu platform helps the DKFZ:
--Perform deeper analysis on raw datasets representing millions of genomic positions without requiring data reduction techniques that can compromise results
--Dramatically reduce the time it takes to analyze raw genomic datasets for each patient to speed creating patient treatments
The newest buzzword after Big Data is AI. From Google search to Facebook messenger bots, AI is also everywhere.
Machine learning has gone mainstream. Organizations are trying to build competitive advantage with AI and Big Data.
But, what does it take to build Machine Learning applications? Beyond the unicorn data scientists and PhDs, how do you build on your big data architecture and apply Machine Learning to what you do?
This talk will discuss technical options to implement machine learning on big data architectures and how to move forward.
Learn how Kiva.org uses high-performance, on-demand cloud analytics to leverage the internet and a worldwide network of microfinance institutions to help individuals lend as little as $25 to create opportunity around the world. See how Kiva.org uses HPE Vertica OnDemand Data Warehouse as a Service to:
• Reduce capital costs
• Leverage the SaaS delivery model to adapt more quickly to changing business requirements
• Work with over a million lenders, hundreds of field partners and volunteers, across the world