• ARE YOU
    ALSO IN NEED OF
    FASTER DATA?

    Being able to easily access and use vast data stores has always been challenging. Over the past decades, businesses have made huge, ongoing investments in information capture, storage and analysis. But in the past few years, the data problem has become 10 times worse. If it was only the amount of data, adding more compute horsepower would have fixed it.

    UNLOCK THE POTENTIAL OF MY DATA

The bigger issues for businesses are the proliferation of data silos along with the ever-expanding distribution complexity. In the past, humans entered most important business data and managed it in centralized data warehouses and transaction systems such as SAP. These were the traditional data sources for many large businesses.

Today, data is being moved to the cloud, forming even more data silos loaded with huge amounts of data. Salesforce and Workday applications, external data providers, and elastic storage and compute services such as Amazon Redshift and Microsoft Azure are a few examples. Multiple silos ultimately lead back to issues for the business. Its productive data resides in some – or all – of these places, in addition to disconnected desktop spreadsheets and people’s heads.

According to Gartner 90 percent of the information assets from big data analytic efforts will be siloed and unusable across multiple business processes. To win, businesses need to take advantage of all of their information. But how?




Access, integrate, and deliver up-to-the minute data with Data Virtualization. Query data from multiple sources as if it were in a single place. Fulfill business demand for an integrated enterprise, cloud, and big data far faster than traditional data warehousing, at a much lower cost. 




















Industry-leading businesses are addressing the challenge with data virtualization.

Data virtualization is an agile data integration approach that organizations use to:

  • Gain more insight from their data

  • Respond faster to accelerating analytics and business intelligence requirements
  • Reduce costs by 50 to 75 percent compared to data replication and consolidation approaches

Data virtualization abstracts data from multiple sources and transparently brings it together to give users a unified, friendly view of the data that they need. Armed with quick and easy access to critical data, users can analyze it with their favorite business intelligence and analytic tools to solve a wide range of problems. For example, they can increase customer profitability, bring products to market faster, reduce costs, and lower risk.



The Total Economic Impact of Data Virtualization

Cost Savings And Business Benefits Enabled By Data Virtualization

Download The
Forrester Consulting
Economic Study
ROI
Use Case
101

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EXPERTS


Who Needs  Data Virtualization?



  • Business leaders

    Data virtualization fosters better business outcomes from business data.

  • Information consumers

    Everyone – from spreadsheet users to scientists – has instant access to all the data they want, the way they want it.

  • Chief Data Officers (CDOs)
    and Enterprise Architects

    Data virtualization provides data integration flexibility to successfully evolve your data management strategy and architecture to take advantage of new business opportunities. business.

  • Chief Information Officers (CIOs)
    and Data Architects

    Data virtualization’s agile integration approach lets you respond faster to business needs for less.

  • Business Intelligence (BI)
    and Integration Competence Center (ICC) Managers

    Data virtualization is an easy, productive way to deliver greater business value sooner.

 



WHAT
IS
INSIDE?


Self-service access
to information
Data Abstraction
Query Optimization
Data Integration
Data Vault and Data Virtualization: Double Agility Migrating to Virtual Data Marts using Data Virtualization Combining Data Streaming with Data Virtualization for Real-Time AnalyticsStreamlining Self-Service BI with Data Virtualization and a Business Directory Streamlining Self-Service BI with Data Virtualization and a Business Directory Data Virtualization in the time of Big Data

Data Vault is a modern approach for designing enterprise data warehouses. The two key benefits of Data Vault are data model extensibility and reproducibility of reporting results. Unfortunately, from a query and reporting point of view a Data Vault model is complex. This whitepaper describes the SuperNova modeling technique. It explains step by step how to develop an environment that extracts data from a Data Vault and how to make that data available for reporting and analytics using the Data Virtualization. Guidelines, do’s and don’ts, and best practices are included. 

Get Your
Data Vault & Data Virtualization
Whitepaper

Almost every BI system is made up of many data marts. These data marts are commonly developed to improve query performance, to deliver to users the data with the right data structure and the right aggregation level, to minimize network delay for geographically dispersed users, to allow the use of specific database technologies, and to give the users more control over their data.  Unfortunately, data marts are expensive because they require a lot of work to develop, operate, and maintain. They also complicate the architectures of BI systems. Virtual data marts can be provide a solution in various ways. This whitepaper describes in detail a step‐by‐step approach for migrating physical data marts to virtual data marts using Data Virtualization. The approach is based on an evolutionary migration that does not impact the existing reporting workload.

Get Your
Virtual Data Marts
Whitepaper

When every millisecond counts... Environments for real‐time analytics analyze the data produced by events instantaneously, they make automated decisions, and they initiate an immediate reaction. Generally, the time between the event that produces the data and the reaction is merely a few milliseconds. In a traditional BI environment it can easily take minutes, hours, and even days, before the data representing a specific event is analyzed, a decision is made, and some action is taken. Therefore, integrating real‐time analytics with a classical BI environment is not straightforward.

This whitepaper describes data streaming and the relationship with business intelligence. It also explains how Data Virtualization can be used to develop BI systems that merge the traditional forms of analytics and reporting with real‐time analytics. In other words, allow reporting and analytics on data in rest and on data in motion.

Get Your
Combining Data Streaming
with Data Virtualization
for Real-Time Analytics
Whitepaper

This whitepaper describes how self‐service BI can be promoted to managed self‐service BI by combining it with Data Virtualization and a Business Directory. With managed self‐service BI, most of the common problems can be avoided, without users losing flexibility, high productivity, self‐serviceness, and independency. The whitepaper describes how to streamline a self‐service BI environment in which metadata specifications are implemented and descriptive information is maintained to improve search capabilities and to explain what data means.

Get Your
Streamlining Self-Service BI
with Data Virtualization
and Business Directory
Whitepaper

This whitepaper describes how self‐service BI can be promoted to managed self‐service BI by combining it with Data Virtualization and a Business Directory. With managed self‐service BI, most of the common problems can be avoided, without users losing flexibility, high productivity, self‐serviceness, and independency. The whitepaper describes how to streamline a self‐service BI environment in which metadata specifications are implemented and descriptive information is maintained to improve search capabilities and to explain what data means.

Get Your
Streamlining Self-Service BI
with Data Virtualization
and Business Directory
Whitepaper

Big data has raised the bar for data virtualization products! Until now data virtualization servers have focused on making big data processing easy. They can hide the complex and technical interfaces of big data storage technologies and they can present big data as if it’s stored in traditional SQL systems. But with scale and performance rising, making big data processing easy is not enough anymore. As such, the next challenge for data virtualization is parallel big data processing. 

Get Your
Whitepaper

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