Many companies nowadays run their business through multiple channels. So to get insight into customer behavior they may perceive a need to focus on creating an omni-channel view. Obviously this is primarily on data collection, but using the data for visualization and analytics is that important.
It will facilitate use of BI tools by stakeholders to get the right insights. But are all tools suitable for all people, what are best practices and how to organize your teams to get best results?
In this webinar, Ronald van Loon will:
• Elaborate on the challenges
• Show how a new approach contributes to meeting them
• Discuss several case studies and their results
RecordedSep 15 201649 mins
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Andy Kriebel, Head Coach at The Data School, Tableau Zen Master & Eva Murray, Tableau Evangelist at EXASOL, Tableau Trainer
Whenever there is data, there is the chance to visualize it and gain valuable insights that can drive change and improvements. Governments have realized the potential that data holds for transforming our towns, cities, living spaces and communities to better address the needs of our modern society.
Governments may want to change public transport services to suit commuters who move away from city centers due to increasing living costs, or develop programs that deliver more support services to areas showing high incidences of mental illnesses, or simply monitor bike traffic to assess the necessity of additional cycle lanes and bike share programs in our capitals. Data and data visualization can help us identify the needs of our communities and can support us in addressing them effectively.
In this webinar Andy and Eva will present examples of Government using data visualization to improve services for communities and will share how you can get involved through analyzing open data and becoming part of the wider 'dataviz' community.
Moving from A-B is slowly being revolutionised through data. Car-sharing and ride-hailing are just the beginning. Thousands of connected devices are currently monitoring data points, and although stand-alone analysis can be useful, true innovation occurs when these data sets are combined to transform into something new.
In the near future, IoT data explosion and the API revolution will collide to change city planning, urban movement and the role of the car in the 21st century.
First off, we set out to understand what big data means in the context of transportation, answering questions such as what is it, where is it coming from, and what can you do with it.
- Next, we'll zoom out and apply these learning to transport innovation in a wider context, considering how it will influence concepts such as urban movement, social mobility, and quality of life.
- Finally, we'll discuss the relationship between open data and innovation.
Dr. Boris Adryan, Head of IoT & Data Analytics, Zühlke Engineering GmbH
For decades “things” have been connected to the Internet. Embedded in carefully planned end-to-end solutions, the what and why of the data arising from these devices has often been hard-coded. In other words, in the M2M world, it is usually clear from the outset what is going to happen with the data. In a future IoT, this won’t necessarily be the case. In a world full of connected devices, the meaning and the potential of the device data is only going to become clear in the context where it is needed in.
But how can software tell that your connected thermometer is useful for a medical application, or that a car in the drive way is likely an indicator of your partner’s presence? This is where device catalogues, information models and ontologies come in handy.
While the talk is not specifically tailored towards a smart city focus, it should become clear how these technologies can be useful in such environment.
See how Netflix built its analytics in the cloud with Tableau and Amazon Web Services
Building out a data platform doesn't have to be like building a House of Cards, and our friends at Netflix know this better than anyone else. With 86 million members and counting, and more than 700 billion events per day, Netflix has had to expand their data capabilities by developing a scalable and flexible analytics platform built on Tableau and AWS.
Attend this webinar to hear from Albert Wong, analytics expert at Netflix, to see how they simplified their data stack by building a data lake/data warehouse strategy which allows Netflix to collect and store massive amounts of data, supporting thousands of Tableau users with managed data.
You'll learn about:
How to set up effective analytics on top of enormous data sets
How Netflix serves large groups of people with governed data
The details of Netflix's data lake/data warehouse strategy
How Netflix manages Hadoop with Tableau
Dr. Fahim Kawsar, Director of IoT Research at Nokia Bell Labs
We are observing a monumental effort from the industry and academia to make everything connected. Naturally, to understand the needs of these connected things, we need a better understanding of humans and where, when, and how they interact. This behavioural understanding would help us to create digital services and capabilities that fundamentally change the way we experience our lives.
In this talk, I will explore the system and algorithmic challenges in modelling human behaviour. I will discuss how mobile and wearable devices together with the wireless network can be used as a multi-sensory computational platform to learn and infer human behaviour and to design user-centred connected services across Enterprise, Urban City and Lifestyle.
Dr Fahim Kawsar leads the Internet of Things research at Bell Labs and holds a Design United Professorship at TU Delft. His current research explores novel algorithms and system design techniques to build transformative multi-sensory systems for disruptive mobile, wearable and IoT services. He borrows tenets from Social Psychology, learns from Behavioural Economics and applies Computer Science methods to drive his research. He is a frequent keynote, panel and tutorial speaker, hold 15+ patents, organised and chaired numerous conferences, (co-)authored 100+ publications and had projects commissioned. He is a former Microsoft Research Fellow and has worked before at Nokia Research, and Lancaster University. His work and publications can be viewed at http://www.fahim-kawsar.net.
BigData requires processing performance but even more it requires agility of your cloud analytics. Iver will demonstrate how today's SAP BusinessObjects Cloud has leading capabilities when used in a highly complex and dynamic environment accessing extreme data volumes.
Muralidhar Somisetty, Co-founder and CTO, Innohabit
Big data analytics is undoubtedly one of the most exciting areas in computing today, and remains an area of fast evolution. Thanks to the data deluge from millions of sensors from IoT networks, it is humanly impossible to analyse and make sense of the data from sensors without analytics tools and processes.
In this webinar, we will go over basics of big-data analytics, how analytics is different from traditional data warehouses or business intelligence systems, different tiers of data analytics etc., We will also see different use-cases of IoT from Smart Home to Transporation to Smart City context and how analytics can be applied for various use-cases for actionable insights.
Webinar also briefly touches upon machine learning tools / techniques that are available as-a-service on cloud today.
Andy Kriebel, Head Coach at The Data School, Tableau Zen Master & Eva Murray Tableau Evangelist at EXASOL, Tableau Trainer
Join Andy Kriebel and Eva Murray to hear about #MakeoverMonday, the popular social data project linking hundreds of members from the global data visualization community in an effort to create better charts and more useful data stories.
In this webinar Andy and Eva will share how Makeover Monday not only results in thousands of better data visualizations, but also helps people find their 'voice' in the community and land their dream jobs all while becoming better analysts and story tellers.
They will also discuss the challenge for week 20, present their own makeovers, and the design and thought process that went into them.
Priyanka Mane, Technology Consultant at Saviant Consulting
Join this webinar to learn:
1. What is Power BI?
a. More power to Business Inteligence
2. Why Power BI?
a. Which data sources we can connect and analyse with Power BI?
b. How to connect those data sources to Power BI?
c. What is the role of R as a data source?
d. How to visualise and Analyse?
i. Design skills
ii. Tips and tricks
iii. Analytics patterns
1. Custom visuals
2. R Custom visuals (Predictive Analytics)
4. What about Backend Management?
a. Excel / Azure SQL / Azure Storage or Other
b. Custom Tables / Columns / Measures
i. What / Why / How?
5. Power BI Updates
b. Power BI Community for queries and request/issues updates
i. How to use this?
It’s easy to get caught in the excitement of machine learning and start optimizing RMSE, AUC or recall, but machine learning success starts with aligning to the business.
Join Patrick Rice, CEO of Lumidatum, as he leverages his decade of experience in big data analytics including implementing machine learning solutions at Amazon to outline the secrets to winning at machine learning and delivering real ROI to the business.
Brian Lange, Partner and Data Scientist, Datascope
Good applications of machine learning and AI can be difficult to pull off. Join Brian Lange, Partner and Data Scientist at data science firm Datascope, as he discusses a variety of ways machine learning and AI can fail (from technical to human factors) so that you can avoid repeating them yourself.
Sparkling Water integrates H2O, open source distributed machine learning platform, with the capabilities of Apache Spark. It allows users to leverage H2O’s machine learning algorithms with Apache Spark applications via Scala, Python, R or H2O’s Flow GUI which makes Sparkling Water a great enterprise solution.
Sparkling Water 2.0 was built to coincide with the release of Apache Spark 2.0 and introduces several new features. One of the latest and largest features is the ability to configure Sparkling Water for different workloads, scale and optimize the platform according to your data and needs.
In this talk we will introduce the basic architecture of Sparkling Water, go over different scaling strategies and explain the pros and cons of each solution. We will also compare the approaches with regards to the specific use cases and provide the rationale why or why not each solution may be a good fit for the desired use case.
This talk will finish with a live demo demonstrating the mentioned approaches and should give you a real time experience of configuring and running Sparkling Water for your use case(s)!
Boaz Farkash - Head of Product Management, Sisense and Philip Lima - CEO, Mashey
Machine learning can identify patterns that humans tend to overlook or can’t find easily in masses of data.
When it comes to business intelligence, machine learning brings real opportunity to:
- Automatically uncover business insights
- Help make products more suggestive in nature
- Empower users to get value faster out of their BI projects
- Reduce implementation complexities
- Generate predictive models
Organizations have begun to notice that by using machine learning, they are able to make new discoveries, as well as identify and solve issues faster, gaining the competitive edge over their competition.
Join Boaz Farkash, Sisense Head of Product Management and Philip Lima, Mashey’s CEO, as they explore how Machine Learning is unlocking the power of Business Intelligence.
To be discussed:
- The Rise of the All-in-One Machines
- Machine Learning is Revolutionizing Immediate Decision Making
- The Power of Business Intelligence Bots
- How Smart Can One Machine Be?
- What Lies Ahead
Charlie Leahy, Head of Software Architecture and Data Science (Hufsy)
Banks have a vast wealth of mineable data available to them, but traditionally have provided their customers with little feedback beyond a balance and list of transactions.
In this talk Charles Leahy, Tech Lead at Hufsy, looks at ways in which tools such as visualisation and machine learning can be employed to give users meaningful insights, helping them make the most of their money.
Giovanni Lanzani, Chief Science Officer at GoDataDriven
Now that the Data Science hype is levelling out, many companies are wondering what went wrong as they could not extract values from their data science efforts.
In this webinar we will explore what does it take to apply data science and machine learning in the real world.
Key takeaways include:
- How can you go beyond the traditional data warehouse when doing machine learning
- How should you adapt your processes to keep monetizing on your data
- How to close the feedback loop between your customers and your machine learning models
- What kind of profiles are essential to successfully become a data driven organization
Wim Stoop, Senior Product Marketing Manager EMEA, Cloudera, Dr. Chris Royles, Systems Engineer, Cloudera
Machine learning is all about the data, but it's often out of reach for analytics teams working at scale. Cloudera customers such as Wargaming.net can store, process and analyse 550 million events each day to help them improve gamers’ experiences and increase their customer lifetime value.
Whether you are new to machine learning and advanced analytics, or you already take advantage of the possibilities, this session will explore practical examples and give you some new ideas to take away. Discover how enterprise organisations can accelerate machine learning from exploration to production by empowering their data scientists with R, Python, Spark and more in one unified platform.