IT Lifecycle Management - Configuration, Change and Release Management
Learn how a state-of-the-art IT automation software helps you unify the handling of a multi-site IT estate configuration and change management, including hardware and software assets, by automating service desk processes, life cycle management and compliance policies and improving service availability.
With the support of an IT automation software, you have more time and resources to focus on strategic decision-making for your IT environment.
This webinar will help you understand how to deploy a streamlined, end-to-end configuration, change and release management plan, advise you on how to enforce process controls and how best to integrate technology and process.
Register for this webinar and learn about:
· Best practices for identifying, assessing/approving, implementing, testing/releasing IT assets, processes and software
· The processes and goals of IT Asset Management and Life Cycle Management
· Reducing risk in operational and application changes
· Improving time to market and reducing risk from uncontrolled changes
· Uncovering savings through process improvement and support for strategic decision-making
· Designing reports and release audits to ensure highest efficiency
· Improving stakeholder collaboration for quicker changes and higher success rate
RecordedJun 12 201343 mins
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Rafael San Miguel Carrasco, Senior Specialist, British Telecom EMEA
This case study is framed in a multinational company with 300k+ employees, present in 100+ countries, that is adding one extra layer of security based on big data analytics capabilities, in order to provide net-new value to their ongoing SOC-related investments.
Having billions of events being generated on a weekly basis, real-time monitoring must be complemented with deep analysis to hunt targeted and advanced attacks.
By leveraging a cloud-based Spark cluster, ElasticSearch, R, Scala and PowerBI, a security analytics platform based on anomaly detection is being progressively implemented.
Anomalies are spotted by applying well-known analytics techniques, from data transformation and mining to clustering, graph analysis, topic modeling, classification and dimensionality reduction.
If a volcano erupts in Iceland, why is Hong Kong your first supply chain casualty? And how do you figure out the most efficient route for bike share replacements?
In this presentation, Chief Data Scientist Dmitri Adler will walk you through some of the most successful use cases of supply-chain management, the best practices for evaluating your supply chain, and how you can implement these strategies in your business.
Merav Yuravlivker, Chief Executive Officer, Data Society
If a database is filled automatically, but it's not analyzed, can it make an impact? And how do you combine disparate data sources to give you a real-time look at your environment?
Chief Executive Officer Merav Yuravlivker discusses how companies are missing out on some of their biggest profits (and how some companies are making billions) by aggregating disparate data sources. You'll learn about data sources available to you, how you can start automating this data collection, and the many insights that are at your fingertips.
Apache Spark for Big Data Analysis combined with Apache Zeppelin for Visualization is a powerful tandem that eases the day to day job of Data Scientists.
In this webinar, you will learn how to:
+ Collect streaming data from the Twitter API and store it in a efficient way
+ Analyse and Display the user interactions with graph-based algorithms wi.
+ Share and collaborate on the same note with peers and business stakeholders to get their buy-in.
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 50% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing, streaming analytics ingestion, and data wrangling within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Spark, Talend or KNIME. The session also discusses how this is related to visual analytics, and best practices for how the data scientist and business user should work together to build good analytic models.
Key takeaways for the audience:
- Learn various option for preparing data sets to build analytic models
- Understand the pros and cons and the targeted persona for each option
- See different technologies and open source frameworks for data preparation
- Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
Cloud analytics has great momentum and that is for a reason: it allows for real-time and live analytics without needing to prepare an environment. In this webinar you will learn how to apply SAP Cloud analytics using BusinessObjects Cloud and the Digital Boardroom. Be amazed by the easiness’ of use and the great visualization capabilities.
Iver van de Zand – SAP Analytics Leader – will provide a deep dive session on the modeling and visualization capabilities of this stunning product
Natalino Busa, Head of Applied Data Science, Teradata
Jupyter notebooks are transforming the way we look at computing, coding and problem solving. But is this the only “data scientist experience” that this technology can provide?
In this webinar, Natalino will sketch how you could use Jupyter to create interactive and compelling data science web applications and provide new ways of data exploration and analysis. In the background, these apps are still powered by well understood and documented Jupyter notebooks.
They will present an architecture which is composed of four parts: a jupyter server-only gateway, a Scala/Spark Jupyter kernel, a Spark cluster and a angular/bootstrap web application.
During the last decades, concepts such as Big Data and Data Visualization have become more popular and present in our daily lives. But what is visualization?
Visualization is an intellectual discipline that allows to generate knowledge through visual forms. And as in every other field, there are good and bad practices that can help consumers or mislead them.
In this webinar, we will address:
-What it’s Data Visualization and why it’s important
-How to choose the right graphic forms in order to represent complex information
-Interactivity and new narratives
-What tools can be used
Ronald van Loon, Director Business Development (Adversitement) and Ian Macdonald, Principal Technologist (Pyramid Analytics)
As companies face the challenges arising from a surge in the number of customer interactions and data, it can be difficult to successfully manage the vast quantities of information and still provide a positive customer experience. It is incumbent upon businesses to create a consumer-centric experience that is powered by (predictive) analytics.
Adopting a data-driven approach through a corporate self-service analytics (SSA) environment is integral to strengthening your data and analytics strategy.
During the webinar, speakers Ronald van Loon & Ian Macdonald will:
•Expand upon on the benefits of a corporate SSA environment
•Define how your business can successfully manage a corporate SSA environment
•Present supportive case studies
•Demonstrate practical examples of analytic governance in an SSA environment using BI Office from Pyramid Analytics.
•Discuss practical tips on how to get started
•Cover how to avoid common pitfalls associated with a SSA environment
Stay tuned for a Q&A with speaker Ronald van Loon and domain expert Ian Macdonald, Principal Technologist, Pyramid Analytics.
Marketers deal with data every day in every channel. Need to segment leads by job title for an email campaign? We’ve got data for that. Want to prove which programs generate higher quality leads than others? Go ask the data.
In this webinar, we’ll show you exactly how a data company uses analytics in its marketing efforts. Susan Graeme, Marketing Director at Tableau, will show you examples of real marketing dashboards that we at Tableau use internally to drive world class marketing programs.
Natalino Busa, Head of Applied Data Science at Teradata
AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.
Machine learning and cognitive computing for:
-Process and Financial Audit
-Data computing systems
-Tools and skills
Merav Yuravlivker, Co-founder and CEO, Data Society
Is it worth it for companies to spend millions of dollars a year on software that can't keep up with constantly evolving open source software? What are the advantages and disadvantages to keeping enterprise licenses and how secure is open source software really?
Join Data Society CEO, Merav Yuravlivker, as she goes over the software trends in the data science space and where big companies are headed in 2017 and beyond.
About the speaker: Merav Yuravlivker is the Co-founder and Chief Executive Officer of Data Society. She has over 10 years of experience in instructional design, training, and teaching. Merav has helped bring new insights to businesses and move their organizations forward through implementing data analytics strategies and training. Merav manages all product development and instructional design for Data Society and heads all consulting projects related to the education sector. She is passionate about increasing data science knowledge from the executive level to the analyst level.
Ani Manian, Head of Product Strategy, Sisense and Philip Lima, Chief Development Officer, Mashey
So you’ve decided you want to jump on the data analytics bandwagon and propel your company into the 21st century with better analytics, reporting and data visualization. But to get a BI project rolling you usually need the entire organization, or at the very least the entire department, to get on board. Since embarking on a BI initiative requires an investment of time and resources, convincing the relevant people in the company to take the leap is imperative. You’ll need to construct a solid business case, defend your budget request and prove the value BI can bring to your organization.
In this webinar you’ll discover:
- Why organizations need to invest in BI to begin with
- How are organization deriving value from BI
- How to build an internal business case for investing in BI
- What are the intricacies of how to build a budget
- How to drive your company to a purchasing decision
- How to start realizing value from BI now
Natalino Busa, Head of Applied Data Science at Teradata
The best services have one thing in common: a superb customer experience. Banking services are no exception to this rule, and indeed the quest for an effortless, well informed, and personalized customer experience is one of the main goals of today's innovation in digital banking services.
According to what Maslow has described in his "pyramid of needs", customers are seeking a more intimate and meaningful experience where banking services can actively assist the customer in performing and managing their financial life. Predictive APIs have a fundamental role in all this, as they enable a new set of customer journeys such as automatic categorization of transactions, detecting and alerting recurrent payments, pre-approving credit requests or provide better tools to fight fraud without limiting legitimate customer transactions.
In this talk, I will focus on how to provide better banking services by using predictive APIs. I will describe the path on how to get there and the challenges of implementing predictive APIs in a strictly audited and regulated domain such as banking. Finally, I will briefly introduce a number of data science techniques to implement those customer journeys and describe how big/fast data engineering can be used to realize predictive data pipelines.
The presentation will unfold in three parts:
1) Define banking services: Maslow's law, modern vs traditional banking
2) Examples predictive and personalized banking experiences
3) Examples of data science and data engineering pipelines for banking and financial services
Lonny Northrup, Sr. Medical Informaticist – Office of Chief Data Officer, Intermountain Healthcare
Hear first hand from one of the nation’s leading healthcare providers, Intermountain Healthcare, on what is actually being accomplished with big data and machine learning (cognitive computing, artificial intelligence, deep learning, etc.) by leading healthcare providers.
Intermountain has evaluated between 300 and 400 big data and analytic solutions and actively collaborates with the other leading healthcare providers in the United States to implement the solutions that are delivering improved healthcare outcomes and cost reductions.
Matt Davies, Head of Marketing EMEA, Splunk, & Sebastian Darrington, EMEA Director, Big Data & Analytics Solutions, Dell EMC
Leveraging Big Data and Analytics to create actionable insights.
Splunk & Dell EMC will share insights into the challenges & opportunities customers are seeing in the market – with the ‘needs to’; reduce costs and improve efficiency within IT (operational analytics), improve Compliance (security analytics) & implement Shadow IT due to the business not receiving the right service from IT. CIO Priority is keeping the lights on and so on…
Dell EMC & Splunk combined strengths are helping numerous organizations to ‘leverage Big Data and Analytics to create actionable insights’.
Natalino Busa, Head of Applied Data Science at Teradata
Today, data is everywhere. As more data streams into cloud-based systems, the combination of data and computing resources gives us today the unprecedented opportunity to perform very sophisticated data analysis and to explore advanced machine learning methods such as deep learning.
Clouds pack very large amount of computing and storage resources, which can be dynamically allocated to create powerful analytical environments. By accessing those analytics clusters of machines, data analysts and data scientists can quickly evaluate more hypotheses and scenarios in parallel and cost-effectively.
The number of analytical tools which is supported on various clouds is increasing by the day. The list of analytical tools spans from traditional rdms databases as provided by vendors to analytics open sources projects such as Hadoop Hive, Spark, H2O. Next to provisioning tools and solutions on the cloud, managed services for Data Science, Big Data and Analytics are becoming a popular offering of many clouds.
Analytics in the cloud provides whole new ways for data analysts, data scientists and business developer to interact with each other, share data and experiments and develop relevant insight towards improved business processes and results. In this talk, I will describe a number of data analytics solutions for the cloud and how they can be added to your current cloud and on-premise landscape.
Rick van der Lans, Independent Industry analyst, Lakshmi Randall, Head of Product Marketing for Denodo
The classic unimodal data warehouse architecture has expired because it is restricted to primarily supporting structured data but not the newer data types such as social, streaming, and IoT data. New BI architecture, such as “logical data warehouse”, is required to augment the traditional and rigid unimodal data warehouse systems with a new bimodal data warehouse architecture to support requirements that are experimental, flexible, explorative, and self-service oriented.
Learn from the Logical Data Warehousing expert, Rick van der Lans, about how you can implement an agile data strategy using a bimodal Logical Data Warehouse architecture.
In this webinar, you will learn:
· Why unimodal data warehouse architectures are not suitable for newer data types
· Why an agile data strategy is necessary to support a bimodal architecture
· The concept of Bimodal Logical Data Warehouse architecture and why it is the future
· How Data Virtualization enables the Bimodal Logical Data Warehouse
· Customer case study depicting successful implementation of this architecture
High performance and scalable data mapping offers unlimited opportunities for quickly categorizing and identifying key insights for retail, defense, insurance, utilities, natural resources, social sciences, medicine, public safety and more.
Organizations, already awash in customer data, know geospatial capabilities can put a new “lens”on existing reports. Data from smartphones, GPS devices and social media has organizations anxious to factor in customer location, origin or destination, with time or day.
Join IBM Product Marketing Manager David Clement and IBM Senior Product Manager Rick Blackwell and explore the new, world-class mapping and geospatial capabilities for IBM Cognos Analytics and Watson Analytics. Discover how you can add geographic dimension to visualizing critical business information in reports and dashboards in Cognos Analytics.
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.
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IT Lifecycle Management - Configuration, Change and Release ManagementPeeush Bajpai, Director of Professional Services EMEA, Kaseya; Chris Brightwell, Senior Engineer, Kaseya[[ webcastStartDate * 1000 | amDateFormat: 'MMM D YYYY h:mm a' ]]43 mins