Data, in its raw form, is useless, unless you transform it and interpret it. That is when you start reaping the benefits of the data that you hold. It helps organisations and professionals to take the right decisions for their business or for their clients. 

So, a bunch of text, numbers or images all brought together without any structure say absolutely nothing. It is simply data overload to no effect, where you risk being lost in data, or, even worse, misinterpreting it. It is only upon the correct transformation of data into readable information that we can start truly “seeing” what the message behind the data is. This is the interpretation of data. 

When analysing large datasets, data is likely to come from different sources, which makes this exercise even more complex. For example, imagine we want to analyse the major news events taking place over the past ten years, utilising different sources: for instance, various social media, various news websites, forums, blogs. What you need to do is to put different data types in different databases. That way you have a comprehensive dataset from various sources which you can then link together. 

Structuring data is therefore essential when combining data from different sources. There are many tools out there that can help to do this. Python is one of the preferred tools for data scientists, helping with structuring and cleansing the data. Cleaning of data can take many forms, from changing formatting to unit conversions to more complex algorithm-based techniques to filter out unwanted data, outliers and so on. Available libraries in the Python framework, such as NumPy and Pandas greatly help in data wrangling. 

Machine learning, through the use of algorithms and equations can then start making sense of all this data. Machine learning models can learn on their own from the constant inputs being fed to them, by humans or otherwise. A classic example is spam calls identification. Of course, there isn’t a person stopping these calls before they reach you, but it is an automated machine learning; a complex set of rules built and integrated into a Machine Learning algorithm. This constantly improves and learns on its own by the input made by us humans. So, if a type of call having specific features is being identified as a spam call by a large amount of people, then the algorithm identifies the pattern amongst these different spam numbers that make them classify as spam. So, that is how unsupervised machine learning learns: through the continuous autonomous improvement which is contingent on the reaction and input of us humans. 

One of the most basic and yet most important factors in data analysis and visualisation is to understand what the goal is… what is the question you are trying to answer? The goal must be specific. Only this way can the analysis and visualisation give you the answer to your question, because we are using the correct dataset to start with, cleansing the data as appropriate, and the using the relevant algorithms to answer the target question. 

You don’t want to get lost into too much data when it comes to strategic and important decision-making. Decision-making should be based on reliable information. That reliable information must be presented in a way that stakeholders understand: whether they are analytical or not, the data must speak to them. That is why visualisation is much more than making the data look nice and colourful. It is about the ability to present the data in such a way as to answer the goal or question that you have, without any ambiguity and uncertainties, as well as making it easy for other stakeholders to understand the data.