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?

Insights on Analytics Project Management

Big Data Conference Interview

Analytics Conference

Analytics Project Management Interview

Global Big Data Conference is beginning shortly. Pictured below (on the left) is General Keynote Session Speaker Dr. Zhongcai Zhang on The Role Of CAO: A Practitioner’s Perspective (Zhongcai Zhang, Chief Analytics Officer, New York Community Bancorp)

Just had a great conversation with KRS Murthy (pictured on the right), the conference Technical chair, serial entrepreneur and strategic advisor to the new University of California (UC) Merced campus. I asked him on the “State of Analytics Project Management.”

He had several industry observations:
1) Industry is new so Anyone can call themselves Project Management (or fill in the blank) Expert
2) Therefore, organizations launching new Big Data initiatives can fall prey to the Blind Leading the Blind.
3) This is exacerbated by a global university talent shortage

Key Next Steps: Take a holistic approach to data science e.g., strategists, product management, UX/UE, data science and engineering, domain expertise and PROJECT MANAGERS:-) KRS (I took editing liberty to emphasize Project Management – Laughing)


How to Make Data Experiments Powerful

Easy Implementation Steps for Analytics Project Management

This article provides two clear examples of how managers can move their projects and organizations to become data-savvy organizations. The fun of this Analytics article is that an example is from an industry that you wouldn’t think of being open to IOT and Big Data!


Experimentation is powerful when it deepens managerial intuition. The first example asks teams to runs lots of tests and to ask how each one impacts the organizations key performance indicators (KPI’s.)  So the team has to look at all the “touchpoints, the task completion, metrics, more deeply than have they had in the past. Managers can quickly test their insights, either validating their thinking or sending them back to think more, then swiftly bring changes to scale.This really changes the team culture and how they look at analytics.


Experimentation is powerful when the organization has unique data. The next example looks at a laundry operations that has a unique set of data and opportunities for introduction of IOT to collect data. “This organization has made it easy, both technically and culturally, for managers to test ideas and learn from them.”


This is an easy read and a big win for project managers looking for examples of how to implement Analytics Projects.

Sourced through Scoop.it from: sloanreview.mit.edu