Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions.
Computer systems consisting of multi-core CPUs or GPUs using parallel processing and extremely fast networks are required to process the data. However, legacy storage solutions are based on architectures that are decades old, un-scalable and not well suited for the massive concurrency required by machine learning. Legacy storage is becoming a bottleneck in processing big data and a new storage technology is needed to meet data analytics performance needs.
Both are fueled by a drive for progress, for pushing
boundaries and advancing the status quo. In these fields,
new trends are like a currency. Keeping ahead of the
next big trend means being aware of the next big seller
and allocating all the right resources – fashion design,
manufacturing, and marketing – for maximum impact.
Miss the hype and the next fashion season is bound to
hurt the bottom line.
New trends are also important to marketers because
owning a new trend is a way to differentiate in today’s
fast-moving digital landscape. It’s a way to stand out
from the pack by investing strategically in the right
approaches and technologies at the right time, then
reaping the benefits organically by leading where others
follow. Naturally, making these decisions requires a bit
of trial and error. Nobody has a magic crystal ball that
guarantees success. But as a rule of thumb, the companies
winning in digital marketing are the ones willing to
adopt new technologies while keeping an sharp.
What does it take to be relevant today? In the era of hyper-connectivity, consumers
have become entitled, demanding more control over their experiences and
expecting that marketers use data and insights to create a seamless, relevant brand
experience. Research shows that communications containing relevant information
and offers are the best drivers of brand loyalty and conversions
Context can make or break the communication – and, ultimately, the relationship – between
a consumer and a brand. Today’s consumers expect relevant communications that speak
directly to their needs in the moment. We have the technology today to deliver such messages –
but there are significant barriers to developing relevant, contextual programs of this kind.
Some of the development challenges represent new versions of old challenges. Take data as an
example: it has always been hard to harness data from different sources and to leverage insights in
real time. But today, there are additional opportunities – if not expectations – for marketers to use
contextual data to better reach and engage customers through the optimal channel(s).
This paper provides an introduction to deep learning, its applications and how SAS supports the creation of deep learning models. It is geared toward a data scientist and includes a step-by-step overview of how to build a deep learning model using deep learning methods developed by SAS. You’ll then be ready to experiment with these methods in SAS
Visual Data Mining and Machine Learning. See page 12 for more information on how to access a free software trial. Deep learning is a type of machine learning that trains a computer to perform humanlike tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. Deep learning is used strategically in many industries.
The General Data Protection Regulation – or GDPR – is a European
Union (EU) law that protects the rights of individuals with respect to
their data. Adopted as an EU law in April 2016, organizations that hold
data about any resident of the EU must be compliant by May 2018.
With attention-grabbing fines of €20 million or 4% of global annual
turnover, GDPR commands attention at the highest levels. And despite
the “legalese” that compliance suggests brands utilize, the brands that
balance legal compliance with a human approach will turn GDPR to
This white paper provides a series of actions you can take to make
the most of GDPR to both enhance your customer relationships and
Among all the trends and buzzwords currently shaking up the marketing industry, one concept is emerging as the one
to watch: customer experience (CX). Providing individual customers with the best possible experience is becoming the
top priority at the moment, even to the point where experience outshines product quality(!) as the main differentiator.
This is confirmed by a recent Gartner1
survey, in which 86 percent of participating companies listed customer experience
as the main factor for gaining a competitive advantage, compared with merely 36 percent in 2012.
20% of customers will be responsible for 80% of profit – or
so says The Pareto Principle, also known as the “rule of the
vital few.” So, while marketers are trying hard to increase market
share, they should be equally (or even more) concerned about
nurturing the customer relationships they already have. That
means finding ways to strengthen bonds with your best
customers and figuring out how to turn good customers into
Personalization, truly helpful support, data-driven contextual
marketing, re-engagement strategies, gamification… There’s
an almost overwhelming number of options out there, each
touted as your golden key to an enduring bond with your users.
In the pages that follow, you’ll learn about five strategies to drive
engagement and retention with actionable tips from Selligent
clients – top brands that are at the forefront of creating and
sustaining customer loyalty.
LOOK TO RELEVANT USE CASES FOR YOUR BUSINESS. While use cases vary across industries, the most common ones fall into these categories and are usually associated with the listed neural network:
• Image classification or object detection: convolutional neural network (CNN) • Time-series predictions: long short-term memory (LSTM) • Natural language processing: recurrent neural network (RNN) • Unlabeled data classification and data labeling: autoencoder (AE) • Anomaly detection: autoencoder (AE) • Recommender systems: multilayer perceptron (MLP)
Work with your deep learning talent or consultants to identify which use cases best match your organization and desired solutions. Then recreate a successful, already proven method.
Life revolves around prediction—for example, the route you take to get to work, whether to go on a second date, or whether or not to keep reading this sentence are all forms of prediction. We are already seeing machine learning powered by Apache Spark changing the face of innovation at IBM. Learn more.
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