Five Factors That Make Deep Learning Different - Go Deep Baby!

Mike Gualtieri

At the highest conceptual level, deep learning is no different from supervised machine learning. Data scientists start with a labeled data set to train a model using an algorithm and, hopefully, end up with a model that is accurate enough at predicting the labels of new data that is run through the model. For example, developers can use Caffe, a popular deep-learning library, to train a model using thousands or millions of labeled images. Once they train the model, developers can use it within applications to probabilistically identify objects in a new image.  Conceptually like machine learning, yes, but deep learning is different because:

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AI Is Not An Exception – Technology Has Always Taken Jobs

Mike Gualtieri

Yes, AI will take jobs away from many workers - our relatives, friends, and neighbors. So too have all technologies created throughout human history. We invent things to make things easier and the impossible possible. The invention of the wheel made transport easier. Gutenberg’s printing press put lots of monk’s out of business. The chainsaw saw a reduction in the number of sawyers (lumberjacks). Modern medicine created a sharp decrease in snake oil charlatans. The Wang word processor annihilated typing pools. The list goes on. Technology changes how and who performs work, but it also enables new work that no one ever imagined. AI is but another technology in a long list of technologies dating back to the blunt club.

The culprit is gray matter

It is human intelligence. There is nothing that can stop it. But, it is that same gray matter that finds a way – a way for humanity to flourish – at least statistically. If life is precious, then the last hundred years have seen a dramatic increase in life expectancy. According to the National Institute On Aging, the most dramatic and rapid gains have occurred in East Asia, where life expectancy at birth increased from less than 45 years in 1950 to more than 74 years today.

AI will short-term replace workers just as all technology has, but longer term it will raise wages as human workers become exponentially more productive because their efforts are augmented by intelligent machines – non-human servants.

We can go back or we can go forward. Let’s go forward.

Are You On An Agile+DevOps Journey? Don’t Miss Out On Continuous Testing Services!

Diego Lo Giudice

It happens often in conversations with clients that I realize they have disjointed initiatives going on to support their digital transformation. The most dangerous parallel initiatives are those where, on one side, they are changing their development teams to become more Agile, but a separate initiative in the same enterprise exists where their Operations folks are running a development and operations (DevOps) transformation. The first thing I recommend to those clients is to unify or tightly connect those programs with an underlining common lean strategy. But I don’t want to dig in here about Agile+DevOps and how overused and abused the term “DevOps” is. I will just recommend to you some reports we’ve published explaining how “Agile” and “DevOps” are two sides of the same coin (see, for example, “Faster Software Delivery Will Accelerate Digital Transformation”).  The Modern Application Delivery playbook I’ve co-authored for years is all about what it means to adopt Agile+DevOps. Check that out too.

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Not too late to catch Digital Transformation Forum, 2017

Stephen Powers

The moment of truth for your digital re-invention has arrived. Digital technology has rendered your legacy systems obsolete, and has liberated your customers to adopt - and abandon - your offerings at a moment’s notice. You already know it’s time to change. You need to transform your firm to meet your customers’ expectations and ensure flexibility for the future. For hungry companies, the idea of "digital transformation" is an opportunity to expose new business opportunities, evolve operations, and grow.

Next week in Chicago, on May 9-10, Digital Transformation Forum 2017 will help you lay out the next steps in your digital strategy. It will feature sessions where leaders from companies such as Allstate, Bloomingdale’s, Gap, GE Oil & Gas, Expedia, Nespresso, Visa, and AIG will tell stories of how they helped their firms digitally transform and what they learned. In addition, Forrester analysts will present sessions on how you can:

  • See the big picture. Martin Gill will frame Digital Transformation as an enterprise-wide initiative – and one that can’t wait.
  • Engage your customers on any platform. Julie Ask will show how amorphous channels will house your firms’ digital customer interactions in the future and help you plan to add value and win customers through better experiences.
  • Mature your AI strategy from novelty to strategic advantage. Rob Koplowitz will introduce Forrester’s framework for developing the next generation of human/machine interactions.
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Forrester Methodology To Select Business Intelligence Implementation Service Providers

Boris Evelson

Business Intelligence (BI) pros continue to look for outside professional services. Forty-nine percent of decision makers say their firms are already engaging and/or expanding their engagements with outside data and analytic service providers, and another 22% plan to do so in the next 12 months. There are two main reasons for this sustained trend:

  • The breadth and depth of BI deployments cannot be internally replicated at scale. Delivering widely adopted and effective BI solutions is not easy. It requires rigor in methodology, discipline in execution, the right resources, and the application of numerous best practices. No internal enterprise tech organization can claim this wealth of expertise and experience; this only comes after delivering thousands of successful and unsuccessful BI projects — which we believe is solely the realm of management consultants and systems integrators. These partners have collectively accumulated such experience over many years and thousands of clients and projects.
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Is Business Intelligence (BI) Market Finally Maturing? Forrester Three Big BI Market Predictions

Boris Evelson

No. The buy side market is nowhere near maturity and will continue to be a greenfield opportunity to many BI vendors. Our research still shows that homegrown shadow IT BI applications based on spreadsheets and desktop databases dominate the enterprises. And only somewhere between 20% and 50% of enterprise structured data is being curated and available to enterprise BI tools and applications.

The sell side of the market is a different story. Forrester’s three recent research reports are pointing to a highly mature, commoditized and crowded market. That crowded landscape has to change. Forrester is making three predictions which should guide BI vendor and BI buyer strategies in the next three to five years.

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Amazon Connect - The Elephant In The Room In The Customer Service Space

Kate Leggett

Amazon just launched a preliminary cloud contact center offering, built on AWS. It offers an IVR, natural language understanding via Lex, queueing and routing and telephony infrastructure. It supports basic self-service interactions, and phone interactions. The best videos to explain what Amazon Connect does is at: Getting Started With Amazon Connect  and Introducing Amazon Connect

Even though this is a first step in the commercial contact center world, this offering is really cool. Why? Because Amazon knows how to build and run contact centers. They built their own infrastructure to  power "millions of customer conversations". Amazon Connect has the potential for democratizing customer service technologies - making them simpler, smarter and prepackaged, to allow companies of all sizes to offer good service. 

Today, a customer service organization needs 3 technology categories to support their operations: queuing and routing technologies (to route incidents to the right agent), a  CRM or customer service agent desktop (to capture customer and case details), and workforce optimization technologies (to manage agent staffing, productivity, quality and forecasting).

This technology ecosystem is cumbersome, unintegrated, and vendors offer pieces of this ecosystem. Sure, there’s been movement to consolidate these categories over the last several years. But, still nobody offers the end-to-end solution that customers demand.

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Aprimo’s Acquisition Of ADAM Software Signals Market Consolidation

Nick Barber
Marketing resource management (MRM) vendor Aprimo snatched up ADAM Software, which bolsters Aprimo’s digital asset management (DAM) capabilities and reinforces the consolidation and convergence that we predicted. 
 
There are a lot of small DAM vendors, but there has been a move to consolidate. Specifically, the capabilities of MRM, DAM, and content marketing platforms (CMP) continue to blur. MRM vendors like Aprimo help marketers assign tasks, track resources, budget, and review materials. But they stop short of organizing large libraries of content, integrating with upstream creative workflows, and delivering content downstream. The clear benefit of this merge is that now marketers will have one solution across the entire content lifecycle. 
 
Other large vendors in the market have taken a similar approach. Adobe has built out its offering to include DAM, web content management (WCM), analytics and other capabilities. Multinational conglomerate Danaher purchased MediaBeacon to merge under product-packaging software vendor Esko. Shutterstock bought WebDAM to give Shutterstock a viable story for stock image management.
 
ADAM’s purchase comes as no surprise. Their strength in DAM, noted in our Forrester Wave, and relatively small size made them an attractive acquisition target. 
 
What does Aprimo’s acquisition of ADAM mean for the market?
 
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Customer Success Should Be A Team Sport

Kate Leggett

Customers hold the power in their relationships with businesses. Today, it's not enough for businesses to deliver products. Customers expect them to deliver outcomes and success.

To do this, businesses must understand who the customer is, what their pain points are in achieving their business goals, and must help them choose the right products to meet their goals. The relationship does not stop there. Businesses must ensure that a new customer is properly onboarded, and is realizing ongoing value from their purchase. Forrester data backs these statements up. 68% want vendors who “understand my business, my problems – and help me solve them.”

This is the mission of customer success teams. They actively manage customers post-purchase, to ensure their ongoing success, with the end goal of reducing churn, increasing customer lifetime value and advocacy - the latter of which influences new sales.

Most businesses pursue this mission by standing up customer success organizations. They use a health score  — comprised of financial data, CRM data, product usage data, support cases, customer feedback  — to track their customers. However, most company employees interacting with customers don’t have this visibility into a customer’s health which can impact overall relationships.

Totango, a vendor of customer success solutions, has a very different view of customer success. Sure customer success teams manage overall customer relationships. However, Totango believes that everyone interacting with customers must have access to customer data and their health in order to better engage with them. Employees must also be able easily, with little friction, access this information from within the context of their application.

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Fourteen Machine Learning Solutions For Data Scientists - Which One Is Best For You?

Mike Gualtieri

Yogi Berra, Machine Learning For Predictive ModelsThe Power To Predict Is Mighty

Yogi Berra once said, "It's tough to make predictions, especially about the future." It is tough indeed, but enterprises that can make probabilistic predictions about customers, business processes, and operations will have an edge over enterprises that can't. These predictions don't have to be macroscopic to be consequential. Predictions about what a customer is likely to buy next. Predictions about marketing content that will resonate with a prospect. Predictions about the next best action to take in a business process. Predictions about when an expensive asset is likely to break down. Virtually any customer journey, business process, and even strategic decision can be made better if permeated with the power to predict.

Predictive Analytics And Machine Learning Solutions Make It Possible

Yes, making accurate predictions is tough, but predictive analytics and machine learning (PAML) solutions provide data scientists and developers alike with the tools to make it happen. Forrester defines PAML solutions as:

Software that provides data scientists with 1) tools to build predictive models using statistical and machine learning algorithms and 2) a platform to deploy and manage predictive production models.

The Forrester Wave™: Predictive Analytics And Machine Learning Solutions, Q1 2017

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