Analytics

Showing 1 - 10 of 23 pages tagged with: Analytics

Augmentation, Collaboration, Governance: Defining the Future of Self-Service BI

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...

Read More

Delivering Faster Insights: The Hybrid Cloud Data Warehouse (APAC)

The adoption of cloud data warehousing is no longer seen as teetering on the bleeding edge of technology; a growing number of organizations are already using a cloud data warehouse or cloud data lake to support enterprise business intelligence, reporting, and dashboards. Businesses that migrate to the cloud still face challenges that complicate their ability to leverage cloud-based resources. Although conventional wisdom promotes the cost-effectiveness of the cloud, ungoverned operations allow data storage, egress, and computing resource costs to spiral out of control. Cost management is a...

Read More

A logical architecture is always a flexible architecture (A/NZ)

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...

Read More

Enabling Real-Time Analytics in Today's Complex Enterprise Data Environments

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...

Read More

Bringing a real time flavor to your enterprise analytics

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...

Read More

Analyst Webinar: Enabling a Customer Data Platform Using Data Virtualization

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...

Read More

Myth Busters: I’m Building a Data Lake, So I Don’t Need Data Virtualization (ASEAN)

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...

Read More

Data Virtualization: An Introduction

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...

Read More

Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data Virtualization

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...

Read More

Trustworthy Analytics and Machine Learning with Data Virtualization (EMEA)

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...

Read More

What's Next?

Gain real-time insights from your data and begin
your digital transformation today!