Big Data and AI Uncategorized

Work Smarter, Not Harder. The Future of Estimating

I talked with Sanathanan Rajagopal, the current Chairman of the Society of Cost Analysis and Forecasting (SCAF) and Qinetiq Fellow.

We talked about the future of estimating and how big data, AI and machine learning means working smarter not harder.

Because of new tools, software, there is an expectation that estimates should be produced quicker. AI and big data is already having an effect. We want to use a lot of data to produce an estimate. Sometimes it isn’t available.

“We need the experience and expertise in the project world to have the qualitative element of estimate”

Sanathanan Rajagopal

AI brings in the quantitative information and provides an easy view of the trends e.g. where something is going, or understanding the effects of changes. However the qualitative engagement with the stakeholders and understanding the cognitive behaviours of the project delivery organisation also acts to influence estimating.

Also, with AI we still need people to feed in to the algorithm. Working smartly needs us to incorporate new technology, but let us not forget the elements around the estimating. The machine will learn from the data we input – data scientist needs to be able to validate the data first. This needs to be robust for big data analysis.

How can we help decision makers to trust the output from machine learning algorithms when estimating future work?

It is important to tell the story of the number to build confidence in the estimate. Where did the data come, how did the cost evolve, where is the risk, what is its probability?

You are telling a story. With Big Data, you have to validate that data and where it’s coming from.

How do estimators set up to face the challenges and take advantage of what big data and technologies will present?

We have just started seeing people move towards big data. But it’s at the early stage, there isn’t much analysis on this yet.

More often than not we don’t have enough data to do a full analytical analysis. If you look at the cost lifecycle of the US, estimators in America (by federal law) collect a lot of data to do full analytics at the early stage. In the UK we do a lot of analysis at a slightly later stage, to verify the number we have derived.

“The new world we are stepping into, we need to engage internal and external stakeholders at higher and lower level.”

Sanathanan Rajagopal

You are Chair of SCAF, what do they do and what are your aims for it?

The Society of Cost Analysis and Forecasting (SCAF) are a UK organisation, providing a platform to gain and share knowledge on cost estimating.

SCAF deliver about 4 to 5 workshops a year, including an annual estimating challenge for young estimators. This year it was online, where the young estimators had to estimate the cost of an electric vehicle for 2025 to 2030. They were judged on their development and approach to solving the problem, not on whether the answer was right or wrong!

A lot of events due to Covid, are now virtual – SCAF challenge in late September into October and joint event with ICEEA from November.

Link to a sign up for Sanath’s upcoming talk on ICEAA CEBoK: Link

ICEAA LinkedIn page: Link

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