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Need a New Web Domain? Read this Firstn 201903
Need a New Web Domain? Read this First Your brand most likely operates its website on a domain with the company’s name. Or perhaps your matching domain wasn’t available. Diving deeper into the world of domains for your content marketing purposes will allow you to find helpful opportunities. Are you planning a new microsite or content hub? Looking for custom domains for a campaign? The options can be overwhelming – what extensions should you use, how much should you pay to acquire the domain, what can you do to avoid premium prices? Sit tight. I’m going to teach you about making smart decisions about domain extensions, what add-ons to include, and how much to budget for a domain.
#website  #domain  #advice 
18 hours ago by phil_hendrix
Why Data Science Teams Need Generalists, Not Specialists Eric Colson 20190308
Why Data Science Teams Need Generalists, Not Specialists Eric Colson MARCH 08, 2019 SUMMARY SAVE SHARE COMMENT TEXT SIZE PRINT RECOMMENDED Implementing a Patient-Centered Medical... STRATEGY & EXECUTION CASE 8.95 ADD TO CART What Sales Teams Should Do to Prepare... STRATEGY & EXECUTION HBR DIGITAL ARTICLE 8.95 ADD TO CART Health Systems Need to Completely Reassess... FINANCE & ACCOUNTING HBR DIGITAL ARTICLE 8.95 ADD TO CART HIROSHI WATANABE/GETTY IMAGES In The Wealth of Nations, Adam Smith demonstrates how the division of labor is the chief source of productivity gains using the vivid example of a pin factory assembly line: “One [person] draws out the wire, another straights it, a third cuts it, a fourth points it, a fifth grinds it.” With specialization oriented around function, each worker becomes highly skilled in a narrow task leading to process efficiencies. Output per worker increases many fold; the factory becomes extremely efficient at producing pins. This division of labor by function is so ingrained in us even today that we are quick to organize our teams accordingly. Data science is no exception. An end-to-end algorithmic business capability requires many functions, and so companies usually create teams of specialists: research scientist, data engineers, machine learning engineers, causal inference scientists, and so on. Specialists’ work is coordinated by a product manager, with hand-offs between the functions in a manner resembling the pin factory: “one person sources the data, another models it, a third implements it, a fourth measures it” and on and on. Alas, we should not be optimizing our data science teams for productivity gains; that is what you do when you know what it is you’re producing—pins or otherwise—and are merely seeking incremental efficiencies. The goal of assembly lines is execution.
#datascience  #organization  #advice  #innovation  #productivity  #applications  @EricColson  #tl 
10 days ago by phil_hendrix
The Path to Product Excellence Ebook Product Board 2019
We collected advice from over twenty product leaders about what it takes to build truly excellent products. Get inspired and learn how you can apply these ideas to your teams!
#product  #management  #OKRs  #advice  #bestpractices 
5 weeks ago by phil_hendrix
Newsletter Guide
A 201 guide for taking your newsletters to the next level
#email  #marketing  #ecommerce  #Newsletter  #research  #software  #guide  #advice 
5 weeks ago by phil_hendrix
Maximizing ROI on Crowdsourcing and Internal Collaboration Innocentive
Maximizing ROI on Crowdsourcing and Internal Collaboration An InnoCentive White Paper There are four critical steps for optimizing your external and internal crowdsourcing initiatives and accelerating your internal collaboration. Find out how to tackle each of them in this white paper, which includes: How to identify your innovation focus and select the right tools for the job How to frame and formulate your organizational problems so that you get the right solutions How to establish which audience is right for tackling your organizational problems How to motivate people and offer the right incentives This white paper is authored by Stephan Shapiro, Innovation Evangelist, speaker, author and advisor.
#openinnovation  #crowdsourcing  #advice  @Innocentive 
5 weeks ago by phil_hendrix
Products That Count - Product Management
Who We Are Products That Count is one of the largest networks of product managers in the world. Our community of more than 200,000 product managers is committed to building great products - coming together to network, learn and get inspired by leaders from Netflix, Coinbase, Box and more. We provide access to hundreds of online editorials and podcasts - along with monthly speaker series events and select invite-only salons in tech hubs around the world.
#product  #management  #bestpractices  #newsource  #tl  #advice  #jobs  #training  #A+ 
6 weeks ago by phil_hendrix
Data Visualization Posts - SAS Blogs
Data Visualization Get the right information, with visual impact, to the people who need it
#visualization  #dataviz  @SAS  #SME  #advice  #tl 
7 weeks ago by phil_hendrix
HBR Guide to Persuasive Presentations HBR Guide Series Nancy Duarte 201210
HBR Guide to Persuasive Presentations (HBR Guide Series) (Harvard Business Review Guides) Paperback – October 2, 2012
#visualization  #presentation  #presenting  #communicating  #guide  #advice  #tips  #book 
8 weeks ago by phil_hendrix
Tips for effective data visualization | Geckoboard
Play Your Charts Right Tips for effective data visualization DOWNLOAD THE POSTER DOWNLOAD THE CARDS
#visualization  #advice 
9 weeks ago by phil_hendrix
Background — Post-Industrial Design School
What enables artists, entrepreneurs, and activists to be successful in today’s networked world?
#design  #entrepreneur  #course  #advice 
10 weeks ago by phil_hendrix
What it has taken me 33 years to learn | The Justin McElroy Institute
Schaue ich gerne mal rein, wenn ich ein wenig Inspiration brauche
#advice  #inspiration 
10 weeks ago by meydench
Predictions 2019: Expect A Pragmatic Vision Of AI Forrester 20181106
Predictions 2019: Expect A Pragmatic Vision Of AI Twitter LinkedIn Facebook Email Michele Goetz, Principal Analyst Nov 6 2018 Last year, Forrester predicted that firms would struggle with new technologies, particularly artificial intelligence. This prediction came true: Firms continued with AI experiments that lacked meaningful results. Adoption has now slowed (51% adoption in 2017; 53% adoption in 2018). And budgets remain low in contrast to the ROI and transformation expectations for AI (under $2M for 2018). Will firms claim defeat? On the contrary — in 2019, Forrester predicts that firms will address the pragmatic side of AI now that they have a better understanding of the challenges and embrace the idea that “no pain means no AI gain.” The AI reality is here. Firms are starting to recognize what it is and isn’t, what it can do and what it cannot. And they are seeing the real challenges of AI versus what they assumed the challenges would be. Firms will focus their attention on the data foundations, take creative approaches to building and holding on to AI talent, weave intelligence into business processes, and begin to establish the mechanisms to understanding why AI is acting the way it is.
#ai  #predictions  #outlook  #advice  @Forrester 
december 2018 by phil_hendrix
The CIO's Guide to Artificial Intelligence Gartner 20180102
The CIO’s Guide to Artificial Intelligence January 2, 2018 Contributor: Kasey Panetta DIGITAL BUSINESS CIOs can separate AI hype from reality by considering these areas of risk and opportunity. When a company realized that up to 30% of calls it received were from customers asking about order status, its leadership wanted to know if artificial intelligence (AI) would be able to help manage the interactions. The short answer was yes, a virtual customer assistant could answer questions ranging from “Where is my order” to “How long will I have to wait?” But the bigger question was if AI could help the company in even more impactful ways. “Look at how you are using technology today during critical interactions with customers — business moments — and consider how the value of that moment could be increased,” says Whit Andrews, vice president and distinguished analyst at Gartner. “Then apply AI to those points for additional business value.” “AI allows companies to collect data from a wide variety of places and apply self-improving analysis that can take action” For example, the interaction between company and consumer provides data about the customer. When combining information with other data about that particular customer (i.e., they order X amount of Y products every Z weeks), the company can use AI to further enrich the relationship beyond that interactio
#ai  #advice  #applications  #strategy  #2018  #CIO  @Gartner 
december 2018 by phil_hendrix
Intelligent process automation: The engine at the core of the next-generation operating model McKinsey 201703
Intelligent process automation: The engine at the core of the next-generation operating model By Federico Berruti, Graeme Nixon, Giambattista Taglioni, and Rob Whiteman Article Actions Share this article on LinkedIn Share this article on Twitter Share this article on Facebook Email this article Print this article Download this article Full intelligent process automation comprises five key technologies. Here’s how to use them to enhance productivity and efficiency, reduce operational risks, and improve customer experiences. Since the financial crisis of 2007–09, many companies have applied lean management to improve cost efficiencies, customer satisfaction, and employee engagement simultaneously, and many programs have achieved substantial impact on all dimensions. Progress on digital, however, has been more uneven. In the insurance sector, for example, an October 2016 FIS study found that 99.6 percent of insurers surveyed admitted they face obstacles in implementing digital innovation, while 80 percent recognize they need digital capabilities to meet business challenges. This difficulty has been compounded by the boom in “insurtech” investments in 2016—topping $3.5 billion in funding across 111 deals since 2015. As macroeconomic conditions continue to put pressure on profit margins across sectors, cost productivity and unlocking new value are back at the top of the senior-management agenda. The question is, what else can be done? That’s where intelligent process automation (IPA) comes in. We believe it will be a core part of companies’ next-generation operating models. Many companies across industries have been experimenting with IPA, with impressive results: Automation of 50 to 70 percent of tasks . . . . . . which has translated into 20 to 35 percent annual run-rate cost efficiencies . . . . . . and a reduction in straight-through process time of 50 to 60 percent . . . . . . with return on investments most often in triple-digit percentages. New technologies that promise double-digit or even triple-digit same-year returns should rightfully be viewed with skepticism. But our experience shows that the promise of IPA is real if executives carefully consider and understand the drivers of opportunity and incorporate them effectively with the other approaches and capabilities that drive the next-generation operating model. (For more on these approaches and capabilities, please read “The next-generation operating model for the digital world.”) What is intelligent p
#automation  #rpa  #ia  #innovation  #enterprise  #advice  @McKinsey 
december 2018 by phil_hendrix
Ready, Set, Fail - Avoiding setbacks in the intelligent automation race KPMG 2018
Ready, Set, Fail?: Avoiding setbacks in the intelligent automation race New study reveals most organizations’ low readiness to deploy artificial intelligence technologies
Executives have high expectations for the impact of intelligent automation, but they're not yet ready to implement it from the top down and at scale. They'll struggle to get adequate ROI until they recognize two critical issues: 1) intelligent automation investment decisions need to be C-level strategy imperatives, 2) intelligent automation is about business and operating model transformation not simply technology deployment.

It's not clear whether most companies understand that intelligent automation is about changing business processes, and then restructuring the organization around those new processes now driven by technologies that didn't exist before. This means shifting the business and operating model from one of people supported by technology to one of technology supported by people. It's a digital-first operating model.

KPMG recently undertook a study to understand the reasons for and implications of deploying IA and what it takes to scale. KPMG professionals interviewed executives from numerous industries and geographies worldwide about their experiences with deployment and their perspectives on the future. Most emphasized that IA is poised to digitally transform their companies and industries and profoundly impact their employees' roles.

At the same time, executives highlighted several challenges. In addition to grappling with the extraordinary pace of change, they are faced with understanding and choosing among hundreds of technology options, the need for effective data and analytics, prioritizing automation focus, and defining their future workforce. KPMG research considered three main areas of intelligent automation -- basic or robotic process automation (RPA), enhanced automation and cognitive automation.
#ai  #ia  #status  #outlook  #strategy  #advice  #recommendations  @KPMG  #2018 
december 2018 by phil_hendrix
Artificial intelligence that improves job performance Fast Company 20181024
This artificial intelligence won’t take your job, it will help you do it better AI-powered tools are everywhere. The challenge lies in deploying them so they actually do some good. [Photo: wutwhanfoto/iStock] BY GWEN MORAN4 MINUTE READ Artificial intelligence (AI) and machine learning are increasingly powering workplace platforms and tools. The sophisticated automation tools have been widely promoted as relieving workers from tasks that are “dirty, dull, or dangerous,” unleashing them to do higher-level work and create. PwC research estimates that AI will contribute $15.7 trillion to the global economy by 2030, driven primarily by productivity gains and AI-fueled product innovation. In various categories, it’s beginning to deliver on its promise. Financial services companies are using such technologies in ways that range from chatbots that answer basic customer questions to AI-powered platforms that help prevent fraud and money laundering. Human resources (HR) applications help companies sort through resumes, find talent, and even conduct initial interviews. It can be used for maintenance alerts and prevent equipment and vehicle failure in automotive fleets. Purchasing algorithms can help sort through data to make better procurement decisions. In healthcare, promising applications range from robotic surgery to diagnoses of various conditions to AI-powered preauthorizations and other medical certifications.
#ai  #automation  #jobs  #work  #advice  #A+ 
november 2018 by phil_hendrix
What We Often Get Wrong About Automation HBR 20181011
What We Often Get Wrong About Automation Ravin Jesuthasan John Boudreau OCTOBER 11, 2018 When leaders describe how advances in automation will affect job prospects for humans, predictions typically fall into one of two camps. Optimists say that machines will free human workers to do higher-value, more creative work. Pessimists predict massive unemployment, or, if they have a flair for the dramatic, a doomsday scenario in which humans’ only job is to serve our robot overlords. What almost everyone gets wrong is focusing exclusively on the idea of automation “replacing” humans. Simply asking which humans will be replaced fails to account for how work and automation will evolve. Our new book, Reinventing Jobs: A 4-Step Approach for Applying Automation to Work, argues that while automation can sometimes substitute for human work, it also more importantly has the potential to create new, more valuable, and more fulfilling roles for humans.
#ai  #automation  #impact  #work  #jobs  #strategy  #advice  #framework  @RavinJesuthasan  @JohnBoudreau 
november 2018 by phil_hendrix
Reinventing Jobs: A 4-Step Approach for Applying Automation to Work, Ravin Jesuthasan, John Boudreau, eBook - Amazon.com
Reinventing Jobs: A 4-Step Approach for Applying Automation to Work Kindle Edition by Ravin Jesuthasan (Author), John Boudreau (Author)
#ai  #automation  #jobs  #work  #tasks  #impact  #advice  #framework  #book  #tl 
november 2018 by phil_hendrix

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