I’m often surprised to find companies that continue to invest in data strategies that fail to respond to their business needs. They are taking a page from an old playbook and expect it to work in their new, rapidly evolving environments. They’re trying to forge a new path with an old map, which won’t help them keep pace with competitors, continue to innovate or ultimately grow their businesses. Companies today need to adopt a new mentality when it comes to data analytics. They should work to imagine and embrace the art of the possible. In doing so, they should ask themselves: What can we do now that we couldn’t do before using new technologies and approaches? It could mean the difference between market leadership or future failure.
As a company’s data sets continue to grow in size and vary in nature, the data management systems in place must allow the organizations the flexibility to grow and change with it. Beyond just the data, company’s also need to be able to execute new techniques to maximize the insights contained within the data, to promote better, faster business decisions, forge new paths forward and create new revenue streams.
One leading technique is Artificial Intelligence (AI) – a concept that has been around since the 1960’s, when AI pioneers began predicting that machines would discern and learn tasks without human intervention. They would quite literally be able to ‘think’ for themselves. Fast forward to 2025 there will be 163 zettabytes of data  with an estimated 80% that is no longer human parsable. Those predictions are not just a reality, but also a requirement. With all of this data spanning every industry, the use cases for AI are endless. But, as you look across those use cases there are a common core of AI techniques applied behind each application.
Take Deep Learning, for example – a branch inside of machine learning. Deep learning differs from traditional algorithms by using neural networks to uncover features and solve problems. Organizations have quickly adopted Deep Learning because of the ability to get insights from images, video, audio, and free text. The most common DL methods we come across are:
- Image detection, allows you to detect whether or not something is present
- Image classification, allows you to classify what type of thing is present
- Natural language processing, allows you ask the meaning and intent to words and text
- Speech recognition, allows you transform audio across languages into a common form for NLP
- Segmentation, allows you to determine to what extent something is present
- Prediction, allows you to ask what is the likely outcome
- Recommendation, allows you to get suggested outcomes
- Machine generated images and video used in the entertainment industry
Figure 1 below gives a snapshot summary of some of these methods and shows how they can be directly applied to a business case with measurable value. When applied individually or combined these methods provide the foundation for the ‘Art of the Possible’, making business applications that were impossible before, finally possible.
Figure 1: Mapping Data to AI Power Outcomes 
While the concepts of AI and Deep Learning still seem untouchable to many, we at Dell EMC are constantly finding new ways to bring AI to the forefront. We are taking our daily learning and turning it into validated infrastructure solutions that will make AI simpler, faster, and more accessible. Our family of validated Dell EMC Isilon and NVIDIA based solutions offer flexibility and informed choice by pairing high performance, high bandwidth GPU accelerated compute with high performance, scale-out flash storage. This puts our customers in the driver’s seat with forgettable scale-out infrastructure that makes nothing seems out of the question.
As an example of the ‘Art of the Possible’, we will be hosting a VIP evening event during the upcoming O’Reilly Conference in New York on April 17. The guest of honor will be Sophia – the humanoid robot who uses a combination of the techniques detailed to interact using human gestures, expressions and language. This will provide our customers a first-hand look at what is now possible and help them understand how these same technologies can be applied to drive innovation and growth within their own businesses. Sophia and I will also be featured on the O’Reilly YouTube channel and Sophia will be in the Dell Technologies booth on Wednesday April 17th from 10:30-11:30 AM where we’re going to be raffling off tickets to the evening event.
If you too are interested in learning more about how AI and Deep Learning can benefit your business, please get in touch with me at the show. We look forward to helping you turn the previously unimaginable into reality.
 Data Age 2025 – https://blog.seagate.com/business/enormous-growth-in-data-is-coming-how-to-prepare-for-it-and-prosper-from-it/
 Thanks to Tony Paikeday at NVIDIA for the foundation of this chart
The Challenge of Safeguarding Sensitive Data Today’s organizations are facing expansive requirements for safeguarding sensitive and confidential information. Whether it’s intellectual property, financial information, or PII (Personal Identifiable Information), there are data access risks that if not addressed properly, can be potentially devastating to an organization. With the risk of having to pay huge fines …
|Article Number: 490223||Article Version: 3||Article Type: Break Fix|
Data Protection Advisor 6.3,Data Protection Advisor
Customer uses the Data Processor tool to gather information from Data Domains. After information is gathered the “Data Domain File Distribution by Size” report is run.
This report generates results on a scale of Petabyte (PB), and Exabyte (EB) when it should report in Megabyte MB.
This is a code bug issue In DPA
This is a known Bug/Defect in Data Protection Advisor.
Resolved in DPA 6.2.3 Server patch build 446 or later.
The Patch Build is available through EMC Technical Support.
Please contact EMC Technical Support for further details or information.