We often hear about big data. We at Coevolve IT can provide services to deal with it. But what if you don’t have enough data? Is a shortage of data always a problem?
Fortunately not. You can deal with it. If your data is numeric you can use linear algebra to reduce the number of dimensions in your dataset, from a high-dimensional sparse (with lots of items missing) to a lower-dimensional dense (with most items present) dataset. Techniques such as matrix factorization can help here.
What happens if your data is non-numeric, for example text, categorical or images? Are you completely lost? Not at all. Techniques exist to convert non-numeric data into numeric form, so that techniques such as matrix factorization described above can be used. For example, one-hot encoding converts categorical data to zeros and ones. There are many other alternatives for different types of data. Software for data science and machine learning, such as Scikit-learn and Tensorflow have these capabilities built in.
So shortage of data is not always a problem: it can be converted into more efficiently packed data for processing. Contact Coevolve IT to discuss how.