Construction Data Management | The Proper Strategy!

People, machines, nature, and companies create data. The question is how much data can we capture? Unfortunately, only a small percentage of data can be captured.

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Data Strategy

You have data. Data looks like money in your bank. You can give permission to financial advisers to manage it or you can make an investment plan. My suggestion is that you should make a construction data management plan instead of delegating this process to third parties.

Analyse Data

Now we have data and we have a construction data management plan. Now, we need to start working on the data to get insights. First, we need a list of questions that we will try to answer using data. Ask real questions without limitation. Exploring data should not be a random experiment, the data should give meaningful results that can be positive or negative.

Play with Data and Compare

The second step is to play with the data to see different details. Use an analytical tool and do experiments with data, create charts and graphs to see different relationships. Use SQL functions to filter, join, and search data to understand more. The third step, talk to people who live with problems. And take their feedback on your data analysis findings. Give them the opportunity to play with the data and compare it to their experience.

Objectives

It can be said that we must define the objectives at the beginning of the project. In my opinion, goals can be meaningful after understanding the data. So my recommendation is that you define your goals after data analysis. For example, one goal may be to reduce construction waste. If you have a suitable data set on this goal, this goal can be met.

Use Machine Learning Models

At this point, you can start using ML models to show the results of your analysis and solutions to business problems. Don't go with complicated algorithms, start with easy algorithms like regression. Be consistent in making sets of data to test and verify. Use test data to tune the algorithm and use the set of verification data to verify your model.

Production

It has a prototype, beta user feedback, and a constant stream of data. We must be careful about publishing prototypes for production. The maturity and accuracy of the ML model must be within acceptable limits. Therefore, you should test it under different conditions.