How do you maintain your data mining projects over time?

Data mining is the process of discovering patterns and insights from large and complex datasets. It can help you solve various business problems, such as customer segmentation, fraud detection, market basket analysis, and more. However, data mining is not a one-time activity. You need to update and maintain your data mining projects and portfolio over time to ensure their relevance, accuracy, and value. Here are some tips on how to do that.

1 Review your sources

Data sources are the raw materials for your data mining tasks. They can be internal or external, structured or unstructured, static or dynamic. You need to check if your data sources are still available, reliable, and valid. You also need to monitor any changes in the data quality, format, or schema that might affect your data mining results. If you find any issues or gaps in your data sources, you need to fix them or find alternative sources.

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Dr Andrea Isoni

PhD,Chief AI Officer, AI speaker
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There is no standard way , strictly right or wrong approach to review sources. All I can add is, sometimes synthetic data may be even better than real data. If data quality is very low it may be the a good option to consider.

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2 Update your methods

Another step to maintain your data mining projects is to update your data mining methods. Data mining methods are the techniques that you use to extract insights from your data sources. They can be supervised or unsupervised, descriptive or predictive, statistical or machine learning. You need to evaluate if your data mining methods are still appropriate and effective for your data mining goals. You also need to explore new or improved data mining methods that might offer better performance, accuracy, or interpretability.

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3 Validate your mining results

Data mining results are the outputs and outcomes that you derive from your data mining methods. They can be rules, clusters, associations, classifications, regressions, or recommendations. You need to verify if your data mining results are still relevant, accurate, and valuable for your business problems. You also need to test your data mining results against new or updated data, metrics, or benchmarks to measure their robustness, reliability, and generalizability.

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Dr Andrea Isoni

PhD,Chief AI Officer, AI speaker
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When reporting accuracy or similar metrics of a data mining software, it is crucial to report the training/prediction CPU time/cost associated with it. This allow stakeholders to (roughly) infer how the predictive solution can scale in large dataset in real environment. A fraud detection solution that has 1 day training time may be not as relevant as a solution with 30min training time.

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4 Document your projects

Data mining projects are the organized and structured activities that you conduct to perform data mining tasks. They can follow different phases, such as business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Record and report your data mining projects in a clear, concise, and consistent manner. Make sure you include all the relevant information, such as data sources, methods, results, limitations, and recommendations.

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Dr Andrea Isoni

PhD,Chief AI Officer, AI speaker
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The project needs to clearly highlight the return of investment or any clear business metric achieved (time spared, energy efficiency, cost avoided etc.). As much as possible, business metrics should directly link with technical parameters (fine tuning, false positive ratio, NN hyperparameters etc.). All the technical details and future work should follow this.

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5 Showcase your portfolio

Data mining portfolio is the collection and presentation of your data mining projects that demonstrate your skills, knowledge, and experience in data mining. It can be in the form of a website, a blog, a GitHub repository, a LinkedIn profile, or a resume. You need to update and maintain your data mining portfolio regularly to reflect your latest and best data mining work. You also need to highlight your data mining achievements, impact, and value proposition.

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6 Learn from your community

Data mining community is the network and interaction of other data mining practitioners, experts, and enthusiasts that share their ideas, insights, and feedback on data mining topics. It can be in the form of online forums, blogs, podcasts, webinars, books, or events. Engage and participate in your data mining community actively to learn new trends in data mining, and exchange knowledge, opinions, and support.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Dr Andrea Isoni

PhD,Chief AI Officer, AI speaker
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For easyness of maintenance, the whole data mining software should be written in a 'modular' way: this approach would allow new algorithm to be plugged in as the technology advances. AI is rapid eveolving field so it is important to structure software in a way that it takes advantage of future developments.

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How do you maintain your data mining projects over time?

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1 Review your sources

2 Update your methods

3 Validate your mining results

4 Document your projects

5 Showcase your portfolio

6 Learn from your community

7 Here’s what else to consider

Data Mining

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How do you maintain your data mining projects over time?

1

2

3

4

5

6

7

1 Review your sources

2 Update your methods

3 Validate your mining results

4 Document your projects

5 Showcase your portfolio

6 Learn from your community

7 Here’s what else to consider

Data Mining

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