Fresh Links Sundae – October 19, 2014 Edition from #SMFUSION14

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NOTE: This is an abbreviated post from FUSION 14 in Washington. DC. If you are there this week, I appreciate you coming out to support the Chapter and the Conference #SMFUSION14.

Fresh Links Sundae encapsulates information I have come across during the past week. Often they are from the people whose work I admire or resonate with me. I hope you will find these ideas thought-provoking at the minimum. Even better, I hope these ideas will, over time, help my fellow IT pros make better decisions, be awesome, and kick ass!

Roll over Dogbert’s Tech Support, the application economy is here by Robert Stroud (CA Technologies)

Your Relationship with Metrics by Mark Dalton (HDIConnect)

Problem Management – The value in not knowing by Ryan Ogilvie (Service Management Journey)

Where Art Thou Hadoop? by Svetlana Sicular (Gartner Blogs)

16 Options To Get Started and Make Progress in Machine Learning and Data Science by Jason Brownlee (Machine Learning Mastery)

How to re-balance a data migration project plan by Dylan Jones (The Data Roundtable)

Project Risk Management, PMBOK, DoD PMBOK and Edmund Conrow’s Book by Glen Alleman (Herding Cats)

What Peter Drucker Knew About 2020 by Rick Wartzman (Harvard Business Review)

Fresh Links Sundae – October 12, 2014 Edition

http://www.dreamstime.com/-image28379626Fresh Links Sundae encapsulates information I have come across during the past week. Often they are from the people whose work I admire or resonate with me. I hope you will find these ideas thought-provoking at the minimum. Even better, I hope these ideas will, over time, help my fellow IT pros make better decisions, be awesome, and kick ass!

Much of the current big data and artificial intelligence work have been focusing on using a data-driven approach to answering or solve business problems. Michael Schrage discusses how the work in automated hypothesis might increasingly inspire tomorrow’s breakthrough innovation. Let Data Ask Questions, Not Just Answer Them (Harvard Business Review)

Selecting the right features or attributes is one key step in strengthening the effectiveness of a predictive analytics model. Jason Brownlee explains what feature selection is and outlines a handy checklist for machine learning model building. An Introduction to Feature Selection (Machine Learning Mastery)

“Organisations are far too quick to blame their software tools for their woes,” said Rob England. He also points out that processes and tools are rarely the main causes of an organization’s problem. Don’t blame the tool: squeeze the asset, fix the behaviour (The IT Skeptic) Rob England

Organizations often shy away from the zero-based budgeting (ZBB) method because they believe it means “budgeting from zero.” Shaun Callaghan, Kyle Hawke, and Carey Mignerey dispel the myths behind ZBB and explain why it is a sustainable alternative to cost management appropriate for many. Five myths (and realities) about zero-based budgeting (McKinsey & Company)

Steve Schlarman believes that data classification is an absolute core tenet of information security. He explains how to leverage business context for building an information security strategy. The Data Classification Curve (RSA Archer GRC)

A number of people believe we should all be using the DevOps approach to managing our IT services. Stuart Rance briefly discusses what is behind the DevOps movement and some DevOps ideas that he thinks will work for every category of IT service. DevOps isn’t only for startups (Optimal Service Management)

Even with their inherent drawbacks, passwords remain one highly effective mean of securing information and access. Keith Palmgren explains that good passwords need not be hard to remember and difficult to use. How to Build Complex Passwords and Avoid Easy Breaches (SANS Institute)

Project management is one key competency area that is critical to an organization’s success. In a 6-part series, Tim McClintock discusses the pitfalls that project managers should work hard to avoid. (Global Knowledge Training Blog)

Fresh Links Sundae – October 5, 2014 Edition

business analytics word cloudFresh Links Sundae encapsulates information I have come across during the past week. Often they are from the people whose work I admire or resonate with me. I hope you will find these ideas thought-provoking at the minimum. Even better, I hope these ideas will, over time, help my fellow IT pros make better decisions, be awesome, and kick ass!

Predictive analytics can never offer any ironclad guarantees around prediction, so how do you evaluate a new tool or a new model? Theos Evgeniou offers some basic questions in evaluating new predictive models to help you get the most out of your predictive analytics. How to Tell If You Should Trust Your Statistical Models (Harvard Business Review)

Some organizations implement ITSM with a number of sophisticated processes that end up too bureaucratic to be effective. Stuart Rance suggests how you can simplify things but stay effective at the same time. Do you really need all those cumbersome processes? (Optimal Service Management)

Jason Brownlee believes that, in order to get good at applying machine learning algorithms, you need to build up an intuition of how an algorithm behaves on real data. He describes the process we should follow when studying machines learning algorithms. How to Build an Intuition for Machine Learning Algorithms (Machine Learning Mastery)

Some organizations’ metrics programs fail while others are successful. Phyllis Drucker outlines the steps for organizing your metric framework and how you can kick your metrics game up a notch. A five-step framework for business oriented metrics (The ITSM Review)

As a product manager for cloud services, Alex Bordei’s mission is to make sure his team gets the highest performance possible out of the technologies used in their services. He discusses how NoSQL databases can scale vertically and horizontally, and what you should consider when building a cluster. Scaling NoSQL databases: 5 tips for increasing performance (O’Reilly Radar)

When we train and deploy machine learning models for big data analytics, we run several risks when we strive for perfection and over-train the model. Kirk Borne advocates that we can reap great benefits from data analytics by having fast, simple, slightly imperfect machine learning. Machine Unlearning: The Value of Imperfect Models (MapR)

A number of IT organizations have transformed themselves from a technology-oriented to a services-oriented organization with the practice of IT service management. John Worthington discusses the various approaches and the continual cycle of service definition. What Does It Mean for IT to Be Customer-Focused? (VMware CloudOps)

Some people feel the traditional IT enterprise architecture (EA) can be too rigid or not flexible enough for today’s fast-changing environment. Charles Betz shares his ideas of what an Agile EA approach might look like. Agile and Enterprise Architecture (lean4it)