Digitization Reading List

2017 Must Read Resources for Project Management Leaders

Peter Drucker stated that every organization needs a “theory of business.” Examining your enterprises’ business model theory of business is more relevant than ever as most enterprises are under threat from changing competitive conditions.  Digitization looks at how your organization can enable new data flows, insights and capabilities as you converge fragmented business process. The goal is organizational agility to allow you to compete in new ways.

How do you prepare for this journey?  Thankfully there are a lot of resources out there to help us.  This is my list.  Let me know if you have other resources.

Digital Business Transformation is Organizational Change through the use of Digital Technologies and Business Models to Improve Performance.
Digital Vortex: How Today’s Market Leaders Can Beat Disruptive Competitors at Their Own GameJul 4, 2016
by Jeff Loucks and James Macaulay
Managing Information Technology for Business Value: Practical Strategies for IT and Business Managers (IT Best Practices series) Paperback – April, 2004

Big Data Project Management

How To Communication Big Data Project Risks and Issues

Project Management Competing Demands

Project Management Competing Demands

How can you better communicate Big Data Project Management tradeoff’s with Leadership? You need a simple approach for positioning information.  I remember when I was working at Tandem Computers in the Manufacturing New Product Introduction (NPI) Program Office. I added to the standard project management template the prioritization of the Iron Triangle factors, e.g., cost, time, schedule and quality. It was exciting to move from mastering the craft of the complex project charter components and global team contributions to now having a leadership discussion on what is important to the organization.

Chris Nott does the same thing for Analytics project team decision.  In an IBM blog post he builds on the foundation of big data’s varying number of Vs: volume, variety, velocity and veracity to key analytics project factors to consider.  These guiding principles can be used to inform the leadership team and stakeholders on features, training and timing. I have added the project management implications for his approach.

Confidence:  What is the data architecture and data science behind the features? Do we need to educate business stakeholders on the tradeoffs made and possible suggest a staged approach to data-driven projects?
Context: Per Chris – Understanding context requires understanding who is asking the question and why. I consistently see that outsourced or offshore teams can make assumptions about the requirements. Projects need to have facilitated sessions and liteweight documentation about what this question is being asked, what is the value of answering it and not to over-constrain the solution.
Choice: Opting for a particular technology platform and analytics tools represents the third C of big data—choice. Infrastructure architecture and buildout needs to be completed quickly at the beginning of a project to reduce the time to outcome perception by the business user. Yet, continuous review of architecture needs to be completed by a cross-functional team during all iterations as technology is changing fast and proof of concepts are hardening into production ready systems.

Great insight on how to use Confidence, Contest and Choice to easily introduce tough and complex analytics project tradeoffs during big data project execution.
Source: http://www.ibmbigdatahub.com/blog/3-cs-big-data?

Solving the data conundrum: How to leverage tech and ‘big data’ for impact

The big problem of #BigData: incomplete, fragmented data sets and knowledge at NGO’s yet start right by asking the right questions on how to improve organizational performance.  https://t.co/ctyP0XzufO

Sourced through Scoop.it from: www.devex.com

Inspiring NGO Big Data Examples:  The big problem of Big Data is incomplete, fragmented data sets & knowledge. Yet start an analytics project right by asking the right questions on how to improve organizational performance.  


Example:  Drill down from high-level “what do you need on a Monday morning” to “How am I performing?” which would then be refined to, “In which areas of health am I failing the most, as of one month ago, and which specific clinics are contributing the most to my nonperformance?” 

Extracting Insights from Vast Stores of Data

Here’s how Amazon Prime, Heineken, and BuzzFeed do Analytics.

Sourced through Scoop.it from: hbr.org

Do you ever see an article you wish you wrote? Well HBR article is it for me as I believe in chasing the business problem vs only looking at only the data & anticipating that it may provide insight. HBR agrees & counters the common wisdom of looking at data first to “find” insights and instead states that “Companies that have been successful in harnessing the power of data start with a specific business problem and then seek data to help in their decision making.”  It then provides 3 examples; Amazon Prime, Heineken and Buzz Feed. A short yet powerful read!  https://hbr.org/2016/08/extracting-insights-from-vast-stores-of-data?