Welcome!

Java IoT Authors: Pat Romanski, Liz McMillan, Elizabeth White, Yeshim Deniz, Zakia Bouachraoui

Related Topics: Containers Expo Blog, Microservices Expo, @CloudExpo

Containers Expo Blog: Article

To Achieve Self-Service BI, Consider Using Data Virtualization

Traditional BI and data integration not responsive to today’s BI needs

Dynamic Times Require Dynamic BI
Today's IT organizations face the daunting task of responding to constantly changing business demands, many of which require timely development of new or revised IT solutions.

Mergers and acquisitions are just one example.

Another is the fact that supply chains must form and re-form seemingly overnight as product lifecycles shorten and products become more personalized.

Further, with the explosion of social media and mobile computing, end users are adding new and unforeseen demands for fast access to information.

Has IT's mission to support business's dynamic information needs become an impossible quest?

Business No Longer Waits for IT
In true Darwinian fashion, the business side of most organizations is now taking greater responsibility for fulfilling its own information needs rather than depending solely on already-burdened IT resources.

For example, in a 2011 survey of over 625 business and IT professionals entitled Self-Service Business Intelligence: TDWI Best Practices Report, @TDWI July 2011, The Data Warehousing Institute (TDWI) identified the following top five factors driving businesses toward self-service business intelligence:

  • Constantly changing business needs (65%)
  • IT's inability to satisfy new requests in a timely manner (57%)
  • The need to be a more analytics-driven organization (54%)
  • Slow and untimely access to information (47%)
  • Business user dissatisfaction with IT-delivered BI capabilities (34%)

What Is IT's New Role?
As the business takes greater ownership of its information needs, how does IT's role change?

In the same survey report, authors Claudia Imhoff and Colin White suggest that IT's focus shifts toward making it easier for business users "to access the growing number of dispersed data sources that exist in most organizations."

Examples Imhoff and White cite include:

  • providing friendlier business views of source data
  • improving on-demand access to data across multiple data sources
  • enabling data discovery and search functions
  • supporting access to other types of data, such as unstructured documents; and more.

Given today's information technology challenges, it is time for both IT and the business to move beyond maintaining the status quo and explore together new options to meet increasingly demanding needs for information.

An Evolving BI and Data Integration Landscape
Over the past fifty years, business use of information systems has expanded from the initial automation of financial accounting functions, such as general ledger and accounts payable, to enterprise-wide business process automation solutions such as ERP, CRM, HCM, SCM and more.

With core business processes systemized, BI solutions were a natural follow-on. These solutions enabled business users to leverage the data assets locked inside transaction systems to improve business decision agility and overall business performance.  However, transaction system architectures were optimized for transaction processing, not for the heavy duty query requirements inherent in BI reporting and analysis solutions.

As a result, BI solutions were based on a different architectural paradigm. In this architecture, BI reporting and analysis applications displayed information to business users. Data integration and data management solutions prepared the data behind the scenes.

To support this architecture, new data integration middleware technologies, such as extract, transform and load (ETL), data replication and data propagation, were developed and adopted. And as a complement to this data integration middleware, new data management solutions - e.g., data warehouses, data marts and cubes - emerged to store, manage and deliver the integrated and consolidated data necessary to support BI.

Advantages Of Traditional Approaches
There are many advantages to adopting these now-traditional data integration and data management approaches. The most important is that they enable businesses to successfully meet increasingly complicated information needs.

In fact, an entire ecosystem has formed around these approaches to satisfy functionality requirements and reduce risk.

  • Technology vendors provide powerful tools.
  • Organizations such as The Data Warehouse Institute (TDWI) and the Data Management Association (DAMA) provide education and document best practices.
  • Services firms provide external resources to complement internal IT staff.

Disadvantages Must Also Be Considered
However, there are two major disadvantages to these traditional approaches. The first disadvantage is the extended time it takes to develop solutions that meet new information requirements and to adapt existing solutions.  Because of their inherent architectural complexity, using traditional data integration approaches to support new or changed business needs has typically resulted in long lead times and seemingly endless backlogs.

Business dissatisfaction with this slow pace of change, or time to solution, is evidenced in the TDWI survey results shown above. Clearly, this constraint on IT responsiveness is suboptimal in a dynamic business environment that demands new solutions quickly.

The second disadvantage of using traditional data integration approaches is lack of resource agility. These approaches require design and development in three distinct technologies - BI, data warehousing and ETL. Creating and coordinating metadata, data models, objects and more across these tools is people intensive.

Further, replicating data into a data warehouse and/or mart necessitates additional infrastructure and governance resources to effectively manage multiple copies of data. Balancing these resource-intensive efforts against financial constraints often means fewer resources are available to meet new business needs.

Data Virtualization Addressed Agility Needs
To fulfill rapidly-expanding and ever-changing information needs on the business side and at the same time increase time-to-solution and resource agility on the IT side, a new approach to data integration, called data virtualization, has evolved with wide adoption over the past ten years.

Data virtualization is a data integration technique that provides complete, high-quality and actionable information through virtual integration of data across multiple, disparate internal and external data sources. Data virtualization is implemented using middleware technology that connects to data sources, executes queries to retrieve requested data, combines or federates this data with other data, abstracts and transforms the data to conform to the business information need and then delivers the data to the consuming application.

Contrasting Data Virtualization with Traditional Data Integration

Perhaps the easiest way to understand data virtualization is to contrast it with traditional data integration.

Instead of copying and moving existing source data into physical, integrated data stores (e.g., data warehouses and data marts), as is done with traditional data integration approaches, data virtualization creates a virtual or logical data store. In other words, data virtualization leaves source data in place and uses a set of virtual views and data services to access, integrate, represent and deliver the data to business users and applications.

How Data Virtualization Provides the Agility BI Requires
By significantly improving business decision agility, time-to-solution agility and resource agility, data virtualization provides enterprises can enable self-service BI more successfully and sooner than through traditional data integration methods alone.

  • Data virtualization delivers the complete high-quality, actionable information required for agile business decision making.
  • Data virtualization uses a streamlined approach, an iterative development process, and ease of change to significantly accelerate IT time to solution.
  • And finally, data virtualization directly enables greater resource agility through superior developer productivity, lower infrastructure costs, and better optimization of data integration solutions.

Conclusion
Self-service BI is business's natural response to a fast moving business environment where traditional IT cannot keep pace.  Data virtualization is a data integration approach and technology that can enable self-service BI more successfully and sooner.

But don't just take my word for it.  For a look at how ten large enterprises across a range of industries and domains are successfully doing it as well, go to www.datavirtualizationbook.com.

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

IoT & Smart Cities Stories
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
To Really Work for Enterprises, MultiCloud Adoption Requires Far Better and Inclusive Cloud Monitoring and Cost Management … But How? Overwhelmingly, even as enterprises have adopted cloud computing and are expanding to multi-cloud computing, IT leaders remain concerned about how to monitor, manage and control costs across hybrid and multi-cloud deployments. It’s clear that traditional IT monitoring and management approaches, designed after all for on-premises data centers, are falling short in ...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
In an era of historic innovation fueled by unprecedented access to data and technology, the low cost and risk of entering new markets has leveled the playing field for business. Today, any ambitious innovator can easily introduce a new application or product that can reinvent business models and transform the client experience. In their Day 2 Keynote at 19th Cloud Expo, Mercer Rowe, IBM Vice President of Strategic Alliances, and Raejeanne Skillern, Intel Vice President of Data Center Group and G...
Discussions of cloud computing have evolved in recent years from a focus on specific types of cloud, to a world of hybrid cloud, and to a world dominated by the APIs that make today's multi-cloud environments and hybrid clouds possible. In this Power Panel at 17th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the importance of customers being able to use the specific technologies they need, through environments and ecosystems that expose their APIs to make true ...
The current age of digital transformation means that IT organizations must adapt their toolset to cover all digital experiences, beyond just the end users’. Today’s businesses can no longer focus solely on the digital interactions they manage with employees or customers; they must now contend with non-traditional factors. Whether it's the power of brand to make or break a company, the need to monitor across all locations 24/7, or the ability to proactively resolve issues, companies must adapt to...
We are seeing a major migration of enterprises applications to the cloud. As cloud and business use of real time applications accelerate, legacy networks are no longer able to architecturally support cloud adoption and deliver the performance and security required by highly distributed enterprises. These outdated solutions have become more costly and complicated to implement, install, manage, and maintain.SD-WAN offers unlimited capabilities for accessing the benefits of the cloud and Internet. ...
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
DXWorldEXPO LLC announced today that "IoT Now" was named media sponsor of CloudEXPO | DXWorldEXPO 2018 New York, which will take place on November 11-13, 2018 in New York City, NY. IoT Now explores the evolving opportunities and challenges facing CSPs, and it passes on some lessons learned from those who have taken the first steps in next-gen IoT services.
Founded in 2000, Chetu Inc. is a global provider of customized software development solutions and IT staff augmentation services for software technology providers. By providing clients with unparalleled niche technology expertise and industry experience, Chetu has become the premiere long-term, back-end software development partner for start-ups, SMBs, and Fortune 500 companies. Chetu is headquartered in Plantation, Florida, with thirteen offices throughout the U.S. and abroad.