How do you choose the best data mining technique for your analytics project?

While it's true that data mining and machine learning share similarities, their distinction lies in how they're applied within analytics projects. Data mining is more exploratory in nature, focusing on extracting patterns or hidden relationships within vast datasets, often without a predefined outcome. On the other hand, machine learning brings in the ability to automate and enhance this process by learning from the data and making predictions. The choice between the two often hinges on whether the goal is discovery (data mining) or prediction and optimization (machine learning). It’s important to understand that integrating both can significantly improve the depth of insights and efficiency, depending on the project’s objectives.

In my experience, one tends to forget the actual purpose of data mining by focusing too much on the data and their inability to link it to the business objectives. While machine learning is an extension of data mining, without an understanding of the business and the data, the extension is just a waste of time. Yes, you can run the data and make forecasts, but then you will still have to do a debrief. Although ML can save thousands of human hours when analyzing complex data sets.

From my experiences, what you should do is this. Start by clearly defining your problem and understanding your data. Select a technique that fits your data type and objectives, such as clustering, classification, or regression. Finally, test and refine the chosen method based on its performance metrics to ensure it effectively meets your project's goals.

Yet the key question remains; Will AI replace the majority of data mining methods? The response is "NO" as AI and data mining are likely to work together. AI has the ability to automate operations and enhance the effectiveness of data mining. Nevertheless data mining methods are often customized for sectors/industries or uses whereas AI/ML models may need domain adjustment. AI/ML excels, in detecting patterns and constructing models while data mining provides a set of tools for creating features.

Data mining makes it possible for businesses to make intelligent insights by extracting useful information from raw data. The data mining process differs from use case to use case. The computational process leads to discovery of patterns in data sets using Machine Learning models for predictions and Artificial Intelligence for automating some repetitive tasks used in data analysis and mining information on future trends

In my experience data as a product is something organisations should focus on. Getting a high quality data is a debatable topic because in machine learning some of the records are dropped due to presence of invalid or garbage data but sometimes the impact of those records is overseen. I believe data quality plays very important role to trust data driven decisions.

Absolutely, data needs to be wrangled and cleaned before using it to fit any model. Also, it is very important that the data is free from any bias so that the predictions made by the model are accurate.

I have found that a strictly defined hierarchy (or maybe set of hierarchies) is key to working well with data. And while many out here might reply "Well, duh...", I've found it surprising to see such a lack of good hierarchical data structures in the professional world. If your hierarchy is solid, the size of the data almost doesn't matter. And while quality can play havoc with analyses, data quality can usually be solved with adherence to proper data governance and a good ETL layer. I would never try to reduce my data, but instead extrapolate to fill in blanks. In almost every case, more data is better data. And a great ETL implementation is pure gold.

Understanding data characteristics is crucial for selecting the right analytical approach. Structured data lends itself well to traditional statistical models, while unstructured data may require more advanced techniques like natural language processing. Large datasets might benefit from distributed computing frameworks, whereas small datasets can be handled with simpler tools. Data quality is paramount; poor quality can lead to misleading insights, necessitating rigorous preprocessing steps. The choice of technique is not one-size-fits-all but should be tailored to the specific data at hand.

The type of data you are working with plays an important role in selecting data mining or machine learning. Volume: Large datasets might benefit from more sophisticated machine learning models that can automatically process and learn from vast amounts of data. Variety: Diverse data types (text, images, numerical) may require specific preprocessing techniques and algorithms. For instance, text data might use natural language processing (NLP), while numerical data could be analyzed with regression analysis or neural networks. Veracity: The accuracy and consistency of your data can affect your choice. If data quality is an issue, you'll need to start with cleaning and normalization. After that, the appropriate technique can be applied.

A critical part of this process is to make sure you aren't making these determinations in a monologue with yourself, but rather a dialogue with the business. Make sure you are familiar with the business case and context of the question, either through interviewing stakeholders/customers or having domain experience yourself. Before concluding that particular approaches, dimensions, or datasets are in or out of scope, you need to ensure you have a clear problem statement (or statements) in place.

First, ask yourself: Is my query addressing an 'actionable' business question? Second, remain realistic in your expectations of what your data can answer, as well as efforts required. Too many times I've seen these two facets of data work utterly ignored. There are plenty of methods for messing about with your data, and they all have their places. But if you're not chasing 'actionable' results or you're blowing time and resources on too granular a prize, you're not where you should be.

The Goal and scope of the Analysis helps define the direction your analysis will take, and the story telling style it will acquire. I usually lay down all the possible tools and visualisations that will help me construct the story around my analysis related to the business scope and goals, that helps me in constructing a compelling presentation that not only captures the audience's attention, but also stimulates their interest and curiosity. The relationship and data patterns along with real business scenario correlation, together with effective and relevant visuals help drive the flow of the analysis. Further, a definite KPI scope and goal significance is also important to drive home the findings of the analysis.

In each analytics project, maintaining a focused approach is the gold rule; it allows teams to allocate their time and resources efficiently, leveraging the most appropriate techniques and strategies. That also leads to quicker insights and more effective decision-making, while the pursuit of numerous techniques can scatter efforts and impede progress.

The article section rightly emphasizes the importance of aligning data mining techniques with the specific goals and scope of an analysis. From my experience, a common challenge is the temptation to use complex models for simple problems, which can lead to unnecessary complications. A solution is to start with simpler models to establish a baseline and then gradually move to more complex algorithms if needed, ensuring that the model complexity is justified by the data and the analytical goals. This approach is both efficient and cost-effective.

While it's great to have competency in these and other tools, I would stress that tools are simply a means to an end: delivering business value through data analysis and insights generation. I have seen hugely impactful solutions delivered using Excel spreadsheets and a Word document; conversely, I've seen analytics projects lose trust and fail to delivery useful insight even when backed by hundreds of lines of code and elaborate dashboards. If you apply the most cutting-edge tools to the wrong business questions, you are going to end up with disappointed stakeholders, no matter how impressive your technical skills.

The resource and tool consideration is pivotal in analytics, as it directly impacts the feasibility and sophistication of the project. Leveraging open-source tools like Python or R can significantly reduce costs while providing robust capabilities. However, it's essential to balance the allure of advanced techniques with the practicality of project constraints. Investing in skill development or selecting scalable tools can offer long-term benefits, enabling teams to adapt to evolving data challenges and maintain a competitive edge in the analytics landscape.

I often see organizations with far too powerful data and analytics packages for the amount of data they have or the analyses they need. Much more rarely have I seen organizations with data and analytics packages not powerful enough to yield actionable results. Most of the time, organizations lack the experience or vision to see outside their data silos to find the actionable results. Big tools and sophisticated, complex, and expensive techniques all have their places. But without understanding the results you need, no tool is of any help.

Consider the resources available to you, including budget, time, expertise, and computational resources. Some techniques may require specialized software, hardware, or domain expertise. Open-source libraries like scikit-learn, TensorFlow, and PyTorch offer a wide range of data mining and machine learning algorithms that are accessible and often free to use. Additionally, cloud-based platforms like AWS, Google Cloud Platform, and Azure provide scalable infrastructure for data analysis and model deployment.

Always focus on the goal of any analysis. Rarely will anyone care the tool that was used to derive the insights. Ive seen even the most advanced analysis pulled from complex pipelines end up just getting summarized via SQL with the final calculations/visualizations happening in Google Sheets. Tools are just tools.