Page 4 I September/October 2020 PMI TodayFrom the Board Continued from page 3
Statistics through 31 July 2020
PMI has
314 charteredand
9 potential
chapters
...in 214 countries
and territories
TOTAL
MEMBERS
CERTIFICATIONS
Total Active Holders of:
615,245 CAPM®Certified Associate in Project Management 43,998 PMI-ACP® PMI Agile Certified Practitioner 35,902
PMI-PBA®PMI Professional in Business Analysis 4,114
PMI-RMP®PMI Risk Management Professional 5,962
PMI-SP®PMI Scheduling Professional 2,108
PMP®Project Management Professional 1,036,367
PgMP®Program Management Professional 2,943
PfMP® Portfolio Management Professional 856
reasonably sophisticated analyticaltools like AI, Monte Carlo simulationand handling voice data. At level
3, prescriptive data analytics isspecifically concerned with usingdata to identify optimal projectperformance, not only in respect totime, budget and work performance,but in serving the organization’sstrategic objectives. Amazon isa master in this area: All of itsdecisions—project or otherwise—are informed by a concurrentjuggling of its customer relationshipmanagement (CRM) data, vendorperformance data, psychologicalprofiles associated with buyerbehavior and more. When a friendrecently told me that Amazon knewher better than her mother did, shewasn’t kidding.
How necessary is data analytics formanaging projects effectively?
The need to use data analytics capa-
bilities is determined situationally. For
example, it is hard to imagine running
even the simplest projects without
using basic data to inform decision
making. When dealing with major
programs, data analytics enables
program managers and business
managers to integrate cost, sched-
ule, quality, work performance, and
employee, customer, purchasing and
contract data so they can have a full
understanding of project prospects
and status and can adjust perfor-
mance to deliver better outcomes
faster. Taking advantage of evolving
big data analytic capabilities, they
can work with unthinkably large data
sets instantly (including voice data)
to have a real-time understanding of
billion-dollar projects, enabling them
to guide the project management
effort effectively.
We’ve been hearing a lot about AI,machine learning, the Io T and otherexotic-sounding terms. Where dothey fit in project management?
Regarding AI, an easy answer is thatwe’ve embedded AI in our businessprocesses for decades. A key featureof AI is that it enables us to makehuman-like decisions without humans.
If we’ve set up a project scheduling
system that updates task start
and end dates as we acquire actual
data on work performance, and it
automatically computes impacts on
the project completion date, we’ve
got a low-end AI system. High-end
AI can be enormously sophisticated
and can assume many of the chores
carried out on projects without
human intervention. As the Turing
Test suggests, when our AI-driven
processes produce results that are
indistinguishable from actual human
behavior, we’ve reached the pinnacle
of artificial intelligence.
Does this mean that AI will makeproject managers obsolete? Thegeneral consensus is: not any timesoon. Prevailing wisdom holds thatby taking care of routine drudgework, AI frees project professionalsto direct their attention to high-value conceptual, strategic, people-focused and entrepreneurial matters.While project managers and high-value staff will still have their projectwork cut out for them, the increasinguse of AI on projects will changethe nature of the work they do,moving away from routine choresto addressing high-value challenges.It will have an impact on thecompetencies they need to master.Historically, the project team’s job hasbeen defined as managing the tripleconstraint (i.e., to get the job doneon time, within budget and accordingto specifications). This entailedlearning and using 50-year-oldtechniques in the areas of budgeting,allocating resources, managingcosts and scheduling work. The newcompetencies promote developing anagile mindset, thinking strategically,
