Plain Old Data

"Plain old data," often referred to as "POD," refers to simple, well-structured data formats. This typically means tabular data with columns and rows, and includes formats like CSV, TSV, and simple JSON. It's the kind of data easily understood and manipulated by basic programming techniques.

While AI often involves sophisticated models, it relies heavily on the quality of the input data. Plain old data offers several advantages: it's easier to validate, interpret, and can often be processed by AI models, particularly machine learning algorithms. It acts as a reliable foundation and starting point for many AI projects.

Yes, absolutely. While deep learning often works with unstructured data like images or text, plain old data is invaluable for tasks such as classification, regression and even generating synthetic data for more complex deep learning applications. It frequently serves as the input for training deep learning algorithms.

Examples include spreadsheets (e.g. CSV or TSV), databases with well-defined tables, and simple structured JSON files. These are formats that are easily read and processed by common programming languages.

Unstructured data, like images, text, or audio, doesn't have a predefined data model. Plain old data is organized in a structured format, making it easier to access and use in code. Unstructured data typically requires more pre-processing before it can be utilized by AI algorithms.

Yes, absolutely. While big data often refers to the volume of data, plain old data focuses on the structure. Whether you're working with big data or not, structured data remains crucial for AI, particularly in tasks where interpretability is needed. Often large data will need to be transformed to a more structured form to facilitate easy use.

Basic programming skills are valuable for dealing with plain old data. Additionally, a good understanding of statistical concepts, data processing and machine learning can help unlock insights and build robust AI solutions.

Data quality is crucial. Techniques such as data validation, cleaning, and normalization are essential for obtaining usable data. Good record keeping and ensuring you know the provenance of your data will also ensure quality.

You're in the right place! plainolddata.com is dedicated to this topic. We provide articles, tutorials, and guidance. Additionally, resources like university courses on database design, and machine learning libraries documentation, are great places to learn more about this topic.

Start by identifying what type of problem you want to solve with AI. Obtain some plain old data relevant to your problem (either collect or find publicly available datasets). Then, try to analyze the data with a tool such as python's pandas or R. We offer resources and guides on this site to help you learn more.