Know thy data
Great strides have been taken within companies large and small to harness their data to extract the value hidden within. Why? It’s because the data generated from a typical construction project can show us how to mitigate upcoming risks, peer into projects with granular levels of detail and even be used to predict the likelihood and quality of projects being delivered… And that’s just the start!
Brexit and Material Costs
Putting it all together.
Q: So, what has this got to do with the usability of the previous year’s data?
A: It concerns the potential impact the data may have upon platforms and ML models which are designed to optimise the decision-making process.
In one case, the impact upon ML models trained on material costing data for the last financial year may create a model that returns predictions that could swing between overly conservative or risky. This depends on the assumptions built into the ML model as most are built to recognise historic patterns and predict their likelihood of occurrence in the future.
In another case, this data could impact platforms that rely on internal information to analyse a business’s short-term and long-term financial health. A sudden shift on a macro or microeconomic scale could affect the flow of data and any pre-COVID data models used in benchmarking business performance, internal resources, and forecasting. It could be that the analysis could lead to some poor decision making or reduce a business’s agility under turbulent market conditions.
To revisit the original question… Can we still use data from the last three years?
In my opinion, I believe we can. Recent events don’t necessarily mean that the data is of poor quality or unusable, quite the opposite! However, this data can only be used to inform future decisions if the ML model can take market conditions and other assumptions into account. We should approach the data and the assumptions we build on the information with caution. Some businesses may need to rebuild their data models applicable to the company selectively.
I put the question to you, the reader;
- Can we still use the data from the last three years?
- How much of this data is impacting ML models currently?
- Should construction companies be cautious of the data they use in ML and data platforms from now on?
Let us know your thoughts by leaving a comment on the associated LinkedIn post.
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