Why Expert Judgment will Become Data Analysis Expertise in the New Age of Automation Projects

Consider for a moment 3 surprising findings:

1. Many news articles state that Artificial Intelligence innovations will automate jobs for millions in the coming years
2. If we agree with the previous conclusion, this means that some tasks (or many tasks, depending on the scope of work) performed by project management professionals will be automated by that time
3. There is growing concern that income inequality is rising

These three disparate facts have one thing in common. All these factors relate to strategic innovation risks of building and sustaining innovation.

Co-Innovation Risk: requirement of other innovations for successful commercialization

• This co-innovation risk is related to surprising fact number 1 as many automation projects are linked to successful partnerships with users of AI in other industries (evidence includes privacy laws passed by California recently)

Execution Risk: requirements for your company to bring forth your product with required specifications on time/budget

• This execution risk is related to surprising fact number 2 as many of these AI projects will require project management techniques and expertise

Adoption Chain Risk is the requirement of partners to adopt your product before end customers can assess the full value proposition

• This adoption risk is related to surprise fact number 3 because many of the lower income end users of society will need to adopt or maybe (understand) these new technologies in order to access the full value of the products or innovation

Consider a concrete Example:

Algorithm for detecting credit card use for promotions in apps such as Grub Hub, HelloFresh, and Doordash

Algorithm uses automatically generated fields for address and inside the computer system: saved credit card information and address data

Use of these algorithms do not account for multi-residence buildings, credit cards shared by partners or ordered for other households, and moving or visiting scenarios.

In order to capitalize on promotion targeted at other end users the real value of the products must increase over time, not artificially or adjusted for inflation but in real terms.

What this means:

Experts in project management consult forty nine process areas which include 31 data analysis tools and 36 expert judgment tools.

1. Data analysis tools are very likely to become the expert judgment process areas and expert judgment would be eliminated altogether in this food delivery example. – Many of the tools performed by expert judgment may be performed by data analysis and vice versa.
2. This is because in order to build a system where knowledge of the end user is refined to the user level compliant with privacy and data protection laws – database systems of food delivery will need to ACID properties (Accuracy, Consistency, Isolation, and Durability)
3. These systems do not display these properties currently and arguably may need to be reinvented to create a system that displays these properties compliant with data science principles.

The current 6th edition of the PMBOK guide will be updated soon and many of my certified project management colleagues will contribute to it. As many of the privacy and automation trends indicate, the updates will likely be focused on incorporating the data analysis techniques demanded by the market where subjective or psychological needs captured by “expert judgment” are framed more in reference to data analysis ACID principles. Right now in projects, too much weight is focused on “expert judgment.” In fact, it is the most widely cited tool in the sixth edition of the PMBOK (36 expert judgment tools vs 31 data analysis tools), in the future, data analysis and those with data science skills, like myself, will be in higher demand on mission critical solution architecture projects relating to transactions in large databases like those of GrubHub, HelloFresh, and DoorDash. California is pulling ahead of this data privacy and infrastructure improvement. Technology industries are catching on to the fact that these data privacy and ACID infrastructure are more than just trends, they represent revolution, in a new age of transactions; powered by automation oriented data science projects.

The failure of data projects will be driven by how much “common practice” replaces common sense. Stakeholder engagement and environmental considerations are important and will always be important. With data, context matters!

Original content and article by E.B. Akiode, PMP, a project management professional with 13+ years of leadership and data analysis expertise. She can be reached on LinkedIn for project engagements.