BI's Big Lie: The Distinction Between Visualization and Analysis
Business intelligence tools were born to query and report. But now analysts and business users don't just want dashboards, they want to dive deep into ad hoc analyses, to explore dozens of hypotheses in minutes. The BI industry is responding by tacking on better visualization and calling it ana
Business intelligence tools were born to query and report. But now analysts and business users don't just want dashboards, they want to dive deep into ad hoc analyses, to explore dozens of hypotheses in minutes. The BI industry is responding by tacking on better visualization and calling it analysis.
But visualization and analysis are very different. If they weren't, why do most analysts prefer to query data with BI tools, then do their actual analysis in Excel (or statistical tools)? Or why do Tableau's help documents literally suggest you pull out a calculator if you'd like to run a correlation?
For many companies, this misunderstood distinction is the final barrier to reaching the promised land of data for all. We'll explore the distinction, as well as the growing divide between exploratory analysis tools and predictive analysis tools. We'll also talk about the reasons that cloud-based analysis tools will leave the rest further and further behind.
RecordedMay 14 2014
Your place is confirmed, we'll send you email reminders
Credit & payment card fraud has mushroomed into massive challenges for consumers, financial institutions,regulators and law enforcement. As the accessibility and usage of credit cards increase, banks are losing billions in fraudulent transactions. A recent Nielsen report suggests 5 cents per one dollar is lost to fraud. Banks are increasingly turning to Hadoop & predictive analytics counter identity fraud real-time.
Join Dataguise and Hortonworks for this live webinar to learn how some of the biggest financial institutions are thwarting fraud while maintaining personal information security and compliance.
Moving to the cloud can be daunting; often organizations struggle understanding where to begin and the steps required to realize a migration. In this one hour webinar, Google's migration architect, Peter-Mark Verwoerd will walk through a framework on how to assess cloud migration. Though the course of this presentation, you will also become familiarized with the services offered by Google Cloud Platform.
Andy Kirk, Data Visualization specialist and Editor, VisualisingData.com
In this talk Andy Kirk will shine a light on some of the most discussed and debated aspects of data visualisation design. The aim of the talk is to expose some of the myths about data visualisation and reinforce some of the truths in order to offer practitioners, professionals and part-time enthusiasts alike greater clarity about this increasingly popular discipline.
Viewers will come away with a greater understanding of the rights and the wrongs in data visualisation as well as an awareness of the aspects of this activity that must remain tagged with the elusive notion of ‘it depends’. Along the way Andy will exhibit some of the best examples and techniques from across the field.
Annine Nordestgaard Bentzen (Hufsy), Jeremy Light (Accenture), Stefan Weiß (Fidor), Jan Sirich (Nordea)
A successful Application Programming Interface (API) strategy relies heavily on concepts of open infrastructure and open data. The adoption of Open APIs in banking is thus an idea that has been met with excitement and, understandably, concern as well.
Attend this summit where our experts will discuss:
-What’s in it for banks/fintechs?
-What are the pitfalls when it comes to opening up APIs for banks and integrating into open APIs for fintechs?
-PSD2 - will you be ready (mostly a consideration for banks)?
-How should we (fintechs and banks) operate until the PSD2 is rolled out?
The USP of Hadoop over traditional RDBMS is "Schema on Read".
While the flexibility of choices in data organization, storage, compression and formats in Hadoop makes it easy to process data, understanding the impact of these choices on search, performance and usability allows better design patterns.
Learning when and how to use schemas and data model evolution due to required changes is key to building data-driven applications.
This webinar will explore the various options available and their impact to allow better design choices for data processing and metadata management in Hadoop.
Natalino Busa, Head of Applied Data Science at Teradata
We are very well aware that companies like Facebook, Twitter, Whatsapp deal with datasets in the range of 100's of Petabytes and more. However not all datasets are that big. Did you know that all english pages of Wikipedia amount to just 49 GB uncompressed text data? Likewise, there are a large amount of datasets ranging from customers data to events and transactions which do not exceed the low Terabyte range.
In this webinar we will discuss how to process data in this range both for interactive queries as well for batch processing. We will look at what tradeoffs can be made by tuning the architecture with SSD and RAM. And which distributed computing paradigm work best for this datasets and their typical workloads. We will revision the concepts of data locality, data replication and parallel computing for this specific class of datasets.
Shreyas Shah, Principal Data center Architect, Xilinx
In the cloud computing era, data growth is exponential. Every day billions of photos are shared and large amount of new data created in multiple formats. Within this cloud of data, the relevant data with real monetary value is small. To extract the valuable data, big data analytics frame works like SparK is used. This can run on top of a variety of file systems and data bases. To accelerate the SparK by 10-1000x, customers are creating solutions like log file accelerators, storage layer accelerators, MLLIB (One of the SparK library) accelerators, and SQL accelerators etc.
FPGAs (Field Programmable Gate Arrays) are the ideal fit for these type of accelerators where the workloads are constantly changing. For example, they can accelerate different algorithms on different data based on end users and the time of the day, but keep the same hardware.
This webinar will describe the role of FPGAs in SparK accelerators and give SparK accelerator use cases.
Adrian Whitehead, Specialist Systems Engineer, Isilon Storage Division, EMC ETD
Organisations are spoilt for choice when it comes to Big Data tools with current trends promoting Hadoop as a method of analysing vast amounts of stored unstructured data. Organisations are also increasingly looking towards tools which can monitor live feeds - e.g. Twitter - to perform actions in real time based on keywords. To perform this valuable analysis Spark has become the ecosystem of choice.
Join this session to uncover which tool to choose to improve the performance of your business.
Jay van Zyl (Innosect), Pedro Bizarro (Feedzai), Natalino Busa (Teradata), Matt Mills (Featurespace)
One of the main benefits of Machine Learning is being able to analyse a large amount of data at the speed and efficiency that would require a huge team of humans. This is something that has proven to be very necessary in the Financial Services industry, where insurance companies, banks, and lenders need actionable insights quickly.
Join this panel where we will discuss:
-Why is Machine Learning such a hot topic? What are the benefits/challenges?
-What is needed to do Machine Learning right?
-Case studies of how Machine Learning is helping financial institutions — better customer experience, faster actionable insights
-How ML is able to spot trends and patterns to mitigate risk
Ina Yulo (BrightTALK), Vamsi Chemitiganti (Hortonworks), Bob Savino (Moven), Jamie Donald (Moneyhub), Pedro Arellano (Birst)
Businesses around the world have recognised “data management and analytics” as one of the key areas where they are investing time and money. The demand for this push is largely due to new regulations as well as pressure from customers and investors.
From digital banks which visualise your spending habits, to predictive analytics helping understand consumers’ financial habits, and even to how Big Data can be used to fight fraud and reduce risk, join this panel where industry luminaries will tackle the different opportunities that analytics can unlock.
J.D. Power rates cars, Nielsen rates TV shows, Morningstar rates stocks, and the Data Model Scorecard® rates data models. The Data Model Scorecard® is the industry’s benchmark on data model quality. You will receive an overview to the Scorecard and learn how to incorporate it into your organization’s architecture review board.
Pete McCallum will discuss the impact of platform convergence in today’s storage and infrastructure marketplace. In this discussion, we will explore the evolution of distributed computing, and how the hardware-defined era has been a stagnating force. We will discuss the deep impact of the Software-Defined everything and how platforms like Containers, Software-Defined Storage, Openstack, and Analytics are shaping the new world of massive Data-Defined infrastructure.
Scott Masson, Head Of Technical at SUSO Digital; Moderator: Dallas Jessup, Content Marketing Manager at BrightTALK
Big data and data analytics provide invaluable insight for businesses, guiding and steering everything from the decisions made in board rooms to the way they market and provide goods and services to their customers.
As more-and-more businesses are becoming aware of the agility and competitive edge big data gives them, it’s getting harder for the agencies and start-ups that actually provide big data services to rise above the herd and get found by the businesses looking for them.
In this webinar, we look at how big data and data analytics companies can shape SEO to their own niche in order to find businesses looking for their exact services and expertise. The webinar covers:
• Identifying the search terms potential customers are using to find big data companies
• How to identify niches to get the highest return on the least SEO investment
• How to use your own big data resources as link building assets and lead generation
• Key elements of technical and on-site SEO
What’s the point of data modelling?
We don’t need models as we use packages
We’re an agile shop, no need for models.
We don’t build custom DBMS’s so don’t need Data models.
Ever hear any of these? Unfortunately, these and other similar comments are heard across organisations worldwide.
In part the problem is the way in which Data modelling has been taught with its focus on the development of technical solutions.
This webinar with describe why Data modelling is NOT just for use in DBMS design, in fact it hasn’t been for a long time. Also how the techniques we learned in the 70’s and 80’s for the pre-relational era are useful again now, and why data models are essential for COTS package implementation.
As organizations increasingly leverage data and machine learning methods, people throughout those organizations need to build a basic "data literacy" in those topics.
In this session, data scientist and instructor Brian Lange provides simple, visual, and equation free explanations for a variety of classification algorithms, geared towards helping anyone understand how they work.
Shannon Quinn, Assistant Professor at University of Georgia; and Nanda Vijaydev, Director of Solutions Management at BlueData
Join this webinar to learn how the University of Georgia (UGA) uses Apache Spark and other tools for Big Data analytics and data science research.
UGA needs to give its students and faculty the ability to do hands-on data analysis, with instant access to their own Spark clusters and other Big Data applications.
So how do they provide on-demand Big Data infrastructure and applications for a wide range of data science use cases? How do they give their users the flexibility to try different tools without excessive overhead or cost?
In this webinar, you’ll learn how to:
- Spin up new Spark and Hadoop clusters within minutes, and quickly upgrade to new versions
- Make it easy for users to build and tinker with their own end-to-end data science environments
- Deploy cost-effective, on-premises elastic infrastructure for Big Data analytics and research
Data exploration is the first step in data analysis and typically involves summarizing the main characteristics of a dataset. It is commonly conducted using visual analytics tools.
Before a formal data analysis can be conducted, the analyst must know how many cases are in the dataset, what variables are included, how many missing observations there are and what general hypotheses the data is likely to support. An initial exploration of the dataset helps answer these questions by familiarizing analysts about the data with which they are working.
Join Noam Engelberg as he walks you through the 5 best practices for Data Exploration in preparation for Data Modeling.
1.5 TB of data per day? No problem! Learn how Ask.com turned to Snowflake’s cloud-native data warehouse combined with Tableau’s data visualization solution to address their challenges.
Ask.com and its parent family of premium websites operate in an extremely competitive environment. To stand out in the crowd, the huge amounts of data generated by these websites needs to be analyzed to understand and monetize a wide variety of site traffic.
Ask.com’s previous solution of Hadoop + a traditional data warehouse was limiting their analysts’ ability to bring together and analyze their data.
- Significant amounts of custom processing to bring together data
- Performance issues for data users due to concurrency and contention challenges
- Several hours to incorporate new data into analytics.
Join Ask.com, Snowflake Computing, and Tableau for an informative webinar where you’ll learn:
- How Ask.com simplified their data infrastructure by eliminating the need for Hadoop + a traditional data warehouse
- Why Ask.com’s analysts are able to explore and analyze data without the frustration of poor, inconsistent performance
- How Ask.com’s widely distributed team of analysts can now access a single comprehensive view of data for better insights
The office environment for working has been left almost unaltered for 150 years, even though the tools in the office have evolved immensely from quills, pens and ink, via typewriters and mainframes, to laptops, tablets and smartphones. Even within our computing environments, a revolution has been taking place with the advent of internet and cloud applications. With all this innovation in office tools, why does the office itself still look the same?
By allowing your teams to be distributed over multiple locations you give your organisation the competitive edge to hire the best talent globally, the ability to quickly scale its work force up and down and the chance to save great sums of money on overhead costs. In this talk, I will cover our experiences in running data science teams online in our Virtual S2DS programme and I will share concrete tips on how to set up and run distributed data science teams.
CapSpecialty is upping its game to become the preferred provider of specialty insurance products using MicroStrategy Analytics and Snowflake Cloud Data Warehousing.
CapSpecialty’s investment to overhaul its data pipeline and management systems has delivered fast and measurable results. The stage has been set for CapSpecialty executives to view dashboards that display real-time profitability and KPIs. Insurance analysts and underwriters have self-service access to 10 years’ worth of governed data, allowing them to analyze customer trends and view product performance by category, geography, and agent. CapSpecialty is witnessing measurable business results from the engines that power their BI environment: MicroStrategy enterprise analytics platform firmly integrated with Snowflake’s cloud-based elastic data warehouse.
Attend this webcast to learn how CapSpecialty has combined enterprise analytics with an elastic cloud-based data warehouse, a solution that serves as the cornerstone of their agile, metrics-focused culture.
Daniel Karuppiah, Director of Product Marketing, Microsoft; Saptak Sen, Group Manager, Hortonworks
The emergence of Big Data has driven the need for a new data platform within the enterprise. Apache Hadoop has emerged as the core of that platform and is driving transformative outcomes across every industry.
Attend this webcast to understand:
•How you could leverage the unique advantages of Open Enterprise Apache Hadoop with an overview of the technology
•How it fits within the enterprise, and gain insight into some of the key initial use cases that are driving these transformations
•How Microsoft Azure provides a first-class support for your Linux-based Apache Hadoop applications
•How easy it is to create a HDP cluster in Microsoft Azure and data movement automation for disaster recovery scenarios between your on-premises HDP cluster and your HDP cluster running in Azure
Managing and analyzing data to inform business decisions
Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource.
Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.