Fresh Links Sundae – August 10, 2014 Edition

??????????????????????????????????????????????????????????????????????????????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!

In many organizations, processing information accurately can have a material impact on the financial bottom line. When the data volume is very large, finding inaccuracy within the data sets can be a big challenge. Thomas Redman recommends a list of steps that managers can use when dealing with such challenge. Even the Tiniest Error Can Cost a Company Millions (Harvard Business Review)

Even after nearly 20 years since the emergence of IT Architecture as a discipline, there is still much confusion surrounding what architects supposed to do. Stephen Lahanas proposes five guidelines to help clarify. The 5 Rules of IT Architecture (Technovation Talks)

Edward De Bono’s six thinking hats have seen many creative uses in disciplines other than just education. Debleena Roy discusses how the six hats can also be applied to be successful in the data science field. Six Thinking Hats and the Life of a Data Scientist (KDnuggets)

Some may hold the belief that IT does not matter because it is a commodity. The digital trend shows that IT does matters more, and companies that lacked the skills to manage IT effectively can suffer compared with competitors that had mastered those skills. Pearl Zhu talks about ways where organizations can manage IT more effectively. Digital Trend: IT Matters More (Future of CIO)

In IT organizations, good quality changes present the right information to the right people to make the right decision. Rob Spencer outlines the common reasons for poor quality in a change request and what actions the change managers can take to improve it. Back to basics: why your change fell at the first hurdle (ITSM Review)

What are the differences between data science, data mining, machine learning, statistics, and so on? Vincent Granville compares several analytic disciplines that overlap and explains the differences and similarities. 16 analytic disciplines compared to data science (Data Science Central)

Some organizations classify break/fix as standard changes, which usually get approved automatically and do not require impact assessment. Ryan Ogilvie explains why such setup is rarely a good idea. Practice Shouldn’t Always Make Perfect – Using Standard Changes for Break/Fix (Service Management Journey)

When taking on a difficult challenge in the organization, some leaders may opt to delegate the responsibility to one of their star direct-reports. While delegating difficult issues is tempting, Susan Cramm believes that it can only lead to disappointment. She discusses an example why strategic, change-oriented initiatives will likely require hands-on leadership by senior executives. Lead by Doing, Not by Delegating (Strategy+Business)

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