Welcome!

@DXWorldExpo Authors: Zakia Bouachraoui, Yeshim Deniz, Liz McMillan, Elizabeth White, Pat Romanski

Related Topics: @DXWorldExpo, Java IoT, Microservices Expo, Microsoft Cloud, @CloudExpo, Government Cloud, SDN Journal

@DXWorldExpo: Blog Post

Big Data Defined for 2013

A definition that can help in your interaction with the IT community

We have previously written about the importance of discipline in terms of art like Big Data. There are plenty of indications that more discipline and rigor is required on how we use the term. To date, our key message has been that it is the enterprise CTO who is responsible for defining how the term should be used. We still believe that.

We have also always supported using the community-edited site Wikipedia’s entry on Big Data as a starting point for a Big Data definition. The definition I put there was morphed and edited by the community pretty significantly, but that is just the nature of the beast.  The end result of a collaborative site like that is usually far better than if a single person had created a definition so it is definitely worth checking out as you determine how to use the Big Data term in your enterprise.

But another source now offers a Big Data definition that I like even better than Wikipedia’s. The use of the term Big Data by the TechAmerica Foundation’s Federal Big Data Commission is a fantastic start and it is based on inputs from real champions of IT who have been fielding real solutions into the largest enterprises in the globe (see: TechAmerica Foundation’s Big Data Commission Publishes Comprehensive Guide to Best Practices for Big Data). They define Big Data as: “A phenomenon defined by the rapid acceleration in the expanding volume of high velocity, complex, and diverse types of data. Big Data is often defined along three dimensions — volume, velocity, and variety.”  They further underscore that Big Data requires "advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.”  I like the way they did this, because the first part of the definition is one that can be used by any mission focused planner and the second is one that is more actionable for designers of solutions.

So, as you consider how you will be using this term in your organization, I suggest you use this TechAmerica report as a starting point. The more we form up on these common definitions the better we will be able to articulate and move out towards common visions. Use of common definitions will also allow us to more quickly share lessons learned on what works and what doesn’t. The definitions to form up on are:

Big Data: A phenomenon defined by the rapid acceleration in the expanding volume of high velocity, complex and diverse types of data. Big Data is often defined along three dimensions– volume, velocity and variety.

Big Data Solutions: Advanced techniques and technologies to enable the capture, storage, distribution, management and analysis of information.

Read the original blog entry...

More Stories By Bob Gourley

Bob Gourley writes on enterprise IT. He is a founder of Crucial Point and publisher of CTOvision.com

DXWorldEXPO Digital Transformation Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.
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.
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...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...