Big Data Analytics

Self-service Analytics BI is often quoted by many - ie, allow users to discover and access data without having to ask IT to create a data mart, or by allowing users to directly export/copy the data from the data sources themselves into their analytics tools and systems. The challenge is not just to provide access to the data – even from Excel this can be done - but to do this in real time without creating processing overhead, while getting trusted data, with the best response time possible, in a managed, governed and secure way in order for these users to trust the output of the analysis.

According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes...

Real time analytics techniques promise to enrich your traditional analytics with real time data points. It's key for many scenarios like supply chain management or customer care. Data Virtualization is well known for offering real time connectivity to diverse sources and federation capabilities: the two base ingredients for real time analytics. However, building a strategy around these concepts can be challenging. Impacting delicate data sources, security and performance concerns are often mentioned.Attend this session to learn more about:What are the scenarios where the value of real time...

So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data...

So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data...

Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. For these projects to be successful, data scientists, business analysts, and other personnel need access to data from across the enterprise. However, gaining access to all of the data in an integrated central repository has been a challenge, resulting in up to 80% of the project time being spent on data acquisition and preparation tasks.Data virtualization enables many organizations today to gain data insights from multiple, distributed data sources...

Según Forrester, el Data Fabric ofrece una vista completa, unificada y confiable de los datos de negocio, mediante la integración de fuentes de datos de una manera automática, inteligente y segura que puede procesar grandes volúmenes de información. Analizamos junto con Kellogg Company, L’oréal, Chubb, Campofrio y El Corte Inglés las claves para que un proyecto de Big Data sea realmente efectivo.Descubre cómo:Asegurar el auto-servicio en una plataforma de Big Data y garantizar el gobierno de los datos.Facilitar la reutilización, exploración y descubrimiento de datos para usuarios.Gestionar...

Today’s CIOs carry a paradoxical responsibility of balancing the yin and yang of the Business – IT interface. That is, "Backroom IT’s quest for Stability" with the “Frontline Business’ need for Agility".A paradox that is no longer optional, but is essential. A paradox that defines the business competitiveness, business survival, and business sustainability. Also enables the visibility to the fuzzy future.“Trusted Data Foundation with Data Virtualization” provides a powerful ammunition in the hands of the CIO, to effectively balance these Yin and Yang at the speed of the business. In a trusted...

Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.Attend this webinar and learn:

In the first webinar of the series, we bust the 'performance' myth.“What about performance?” is usually the first question that we get when talking to people about data virtualization. After all, the data virtualization layer sits between you and your data, so how does this affect the performance of your queries? Sometimes the myth is perpetuated by people with alternative solutions… the ‘Put all your data in our Cloud and everything will be fine. Data virtualization? Nah, you don’t need that! It can't handle big queries anyway,’ type of thing.Watch this webinar to look at the basis of the '...