The Rise of Deep Learning in the Enterprise

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In a recent IDC report IT decision makers believe 75% of enterprises applications will use AI by 2021. Artificial Intelligence is not a new solution in fact we have seen various cycles of excitement followed by lulls. What makes this cycle any different? Two words: Deep Learning

What is deep learning?

The early stages of World War II brought about many challenges. Aerial warfare left the historically safe areas vulnerable to attacks from the air. Building a bigger wall or using the ocean as a barrier strategy was quickly deemed useless. In Thomas Rid’s Rise of the Machine: A History of Cybernetics he walks through how learning machines were born out the necessity to create capable anti-aircraft machines. By combining man and machine the anti-aircraft guns were more dynamic at repealing aerial attacks thus the dawn of man and machine began. Over the years those learning machines have advanced from simple pattern recognition to neural networks allowing machines to self-drive automobiles, drones, and trains.

Many of the advances in learning machines has been made possib le by Deep Learning. Think of Deep Learning as a subset in Machine Learning where algorithms learn much like the human brain. In Machine Learning, algorithms are programmed with a defined set of features. For example, trying to identify car types with Machine Learning require to predefined  features like size, spoiler, wheelbase, etc. However Deep Learning allows for the Neural Network to define the features from the data feed through the input layer.

Why Deep Learning Now?

The mathematics and research for Deep Learning may have been around for decades but only recently has the true potential begun to be realized. The key to the rise of Deep Learning is in the Data. Stanford Professor and one of the foremost experts on Artificial Intelligence and Deep Learning Andrew Ng attributes the rise of deep learning to improved algorithms and rise in data. As Data Scientist and Machine Learning Engineers use better algorithms and larger data sets the accuracy of the models improve. Insights gleaned from Deep Learning still fall in the Big Data Maturity Model but are of a higher order of maturity.  Reporting like that seen with Descriptive Analytics don’t require complex deep learning algorithms or large varied data sets. However, as businesses move up the maturity model, like teaching an automobile to drive itself through the busy streets like Nashville or San Francisco, they require more complex algorithms, varying data types, and vast amounts of data.

Data is Key to Better Models

The improved accuracy shows Data Scientist Teams that the key to creating the best model is using better algorithms and neural networks, but the technology is only part of the equation. In fact, I would argue the neural networks are the easy part. Look at the biggest implementors of Deep Learning like Google & Facebook. Both have been driving the open-source versions of their Deep Learning solutions to the public in the form of Tensorflow & Caffe.  Why would Facebook risk doing that? Couldn’t a rival or startup build models to predict what ads our friends and families maybe likely to click on? The problem for that challenger is they couldn’t come close to the accuracy because they wouldn’t have anything close to the Data Capital Facebook has. Data is the real differentiator not the algorithms which is why companies open-source their deep learning frameworks.

Ready for Data Capital

Data Capital has moved beyond a theory and it seen as a law for all industries disrupting the market. The data businesses hold is their accelerant to innovation and will decide who the winners and loser of tomorrow. Data is the great equalizer in deciding if the next application or project your business undertakes will be successful. No longer do we have to rely on gut decisions hoping we made the right call. We can now reduce our risk by letting the data tell us what to do. Now the challenge lies in how we are collecting, protecting, cleansing, and giving access to our data. Tomorrow’s disruptors understand the real risk is not having a strategy for managing Unstructured Data at Scale.

 

To continue this conversation further, both Dell EMC and I will be at the O’Reilly Artificial Intelligence event next week in San Francisco.  Contact me on Twitter @Henson_TM or stop by the Dell EMC booth in the expo hall.

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A modernized data protection strategy enables customers to more efficiently transform their IT – delivering benefits all round. 

In the fourth of a series of blogs inspired by influential research published by industry analyst ESG, we learn how modern data protection strategies, tools and processes enable and support IT Transformation. 

Modernizing the IT environment is a fundamental step that companies of all sizes must take on their journey towards achieving IT Transformation – and that includes the implementation of modern data protection devices and processes.

Around the world, data continues to grow at a phenomenal pace. There’s an equally rapidly expanding need for mobility and the intrinsic value of data to business is also increasing. So optimum protection is paramount.

As your customers and prospects make the necessary move to modernize their IT environments – and specifically data center technologies – they need to ensure that their data protection strategies, tools and processes also evolve accordingly.

What does a modern data protection approach involve?

In today’s increasingly digitally driven economy, the typical workloads, service levels and consumption models that organizations have to provide vary widely. So a ‘one size fits all’ data protection strategy is unlikely to be appropriate. Instead, a suitably modern and agile data protection solution should be deployed to meet the unique needs of each environment that it protects.

This should include everything from backup and availability to archiving solutions, all of which should be validated against specific workload requirements and the ways they are run or accessed – whether that’s from on-premises physical and virtual environments, via hybrid and public cloud services or endpoint devices.

The impact of modern data protection on IT maturity

A comprehensive data protection strategy is essential to support effective IT Transformation. It has also been shown to play a key role in the ranking of an organization’s IT maturity.

Earlier this year, ESG conducted a survey of 4,000 IT executives from private- and public-sector organizations across 16 countries to evaluate their progress in embracing IT Transformation1 – and rank them as ‘Legacy’, ‘Emerging’, ‘Evolving’ or ‘Transformed’.

In general, organizations that had achieved ‘Transformed’ status were nearly 10X more likely than ‘Legacy’ organizations to have invested in modern data protection solutions to cover a broad range of environments – ranging from cloud to on-premises to endpoints.

The 88% versus 9% response is clearly a stark difference – and those businesses that have made the move to modernize their data protection strategies as well as their IT environments are also benefiting from other significant operational advantages.

Flexible data protection strategies to fit specific needs

In its Research Insights Brief on how modern data protection strategies support and enable IT Transformation2, ESG reports that 85% of ‘Transformed’ IT organizations have at least three unique data protection mechanisms in place to safeguard assets and data – covering the spectrum from archive software to continuous availability technology. In contrast, more than half of ‘Legacy’ organizations have no more than two data protection technologies implemented.

This is partly due to the fact that ‘Transformed’ organizations already have a greater diversity of workloads and the consequent need to protect a broader range of IT environments.

However, these organizations have also embraced self-service data protection. This enables line-of-business owners and application administrators to manage data protection tasks like setting backup policies and recovering data themselves. Empowering users with these tools has the potential to minimize delays between the creation and protection of data and enable faster recovery of data, among other benefits – because it reduces or even eliminates the dependence on IT to provision resources or resolve issues.

The ESG study found that two-thirds of ‘Transformed’ organizations reported extensive availability of self-service data protection capabilities.

Significant operational and wider business benefits

Overall, the ESG research found that modern data protection strategies, tools and processes delivered significant operational and wider business benefits.

Compared with ‘Legacy’ organizations, the ‘Transformed’ organizations in the study:

  • Were 13X more likely to offer well-established self-service for data protection management.
  • Were nearly 2X more likely to have exceeded their revenue goals in 2017.
  • Were able to recover their VMs 31% faster.
  • Were 14% more likely to hit their recovery targets.
  • Were 8X more likely to believe they are in a very strong competitive position.

Do you still have customers and prospects in the ‘Legacy’ camp? They clearly need to start to consider what they’re going to do to catch up and remain competitive…

Read and share the full ESG Research Insights Brief >>

Discover how a modern data protection strategy can help to improve operational performance and speak to prospects and customers to discuss their specific needs. You can also use the free ESG online assessment tool with them to demonstrate the opportunities and value of IT Transformation.

Explore our dedicated IT Transformation campaign and marketing tools >>

ESG Research Insights Paper, ‘Research Proves IT Transformation’s Persistent Link to Agility, Innovation, and Business Value’, March 2018.

ESG Research Insights Brief, ‘Enabling IT Transformation with Modern Data Protection Strategies’, May 2018.

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