Analytics

Presented at Data Architecture Online A/NZThe current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML and AI. The physical consolidation of enterprise data into a central repository, although possible, is both expensive and time consuming.A logical data warehouse is a modern data architecture that allows organisations to leverage all of their data irrespective of where the data...

Achieving the goals of real time analytics in today's increasingly complex enterprise data environments poses numerous challenges. Most organizations today require efficient access to integrated data assets across a wide variety of on-prem and cloud data sources. In addition to the inherent complexities of real time integration of data across a variety of disparate sources that is enabled by data virtualization technology, being able to do so in a secure and performant manner while addressing concerns relevant to accessing delicate data sources makes this a most challenging obstacle for...

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...

In today’s digital economy, customers are all-powerful. They have the ability to search for and find products and services, find reviews and ratings, find out about alternative products and services via search and social networks, ask questions of others about products and services, and find comparison sites. All of this can be done from a mobile device while on the move and with so many options available to them. They can switch to a competitor at the touch of a mobile phone screen or the click of a mouse.In this kind of economy, customer loyalty is cheap and so it is essential to know more...

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...

What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad...

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...

Data is an insurer’s most valuable asset. Capitalizing on all of that stored and incoming data to draw valuable insights for business decisions is what ultimately makes a competitive difference.But, insurers face challenges when it comes to modernizing and digitizing their data architectures. Most organizations rely on traditional systems and data integration processes that are time consuming and slow. In addition, as many adopt cloud strategies, these traditional approaches fill the cloud modernization process with downtime and end user frustration.This is why insurers need a flexible and...

In the digital transformation era, banks need a single view of all their data and a way to establish security controls across the entire infrastructure. This can be achieved with Data Virtualization.