Have you ever had a boss give you a confusing task? Have you ever asked for more direction before starting that task? It’s only natural that you don’t want to begin any more work without proper guidance because you know that if you proceed in the wrong direction, all of your work will be wasted. Even worse, you might have to do additional work to undo the “wrong work” that has already been done. Avoiding this pitfall is the essence of visual data exploration.
Visual Data Exploration Defined
Data exploration is that first step before you proceed to other business processes and is a form of analysis—specifically data analysis. The visual aspect of data exploration is that the data needs to be arranged in a way that the human eye can comprehend. Furthermore, the visual presentation of the data needs to be intuitive so that the user can form a strategy based on their interpretation of the data.
This visualization may take the form of spreadsheets, bar charts, graphs, and may employ data visualization software. The thing to understand is that while pattern recognition may be science for algorithms, for humans it’s an art. To be able to view raw data, data within a data set, and find a correlation between two facts out of thousands of facts is not easy.
In the business world, the ability of an organization to find important correlations so that they can decide whether or not they can move forward without further analysis can be invaluable. That’s why data exploration and visual data exploration are crucial business intelligence tools.
Data Analysis Defined
As stated earlier, data exploration is really a subset of data analysis which is simply the examination of data. It’s important to understand that raw data sets must be aggregated into relevant data before any useful information, such as business intelligence and insights, can be gleaned. The characteristics of the data, initial patterns, points of interest, and anomalies have to be detected and categorized into relevant data for proper data models to be formed.
What separates data analysis in the general sense from data exploration is that this analysis in the general sense does not need to be interpreted by the human eye. As long algorithms are doing the analysis, data exploration techniques and exploratory data analysis are not necessary.
Therefore, data analysis is the analysis of data with or without visualization tools for the benefit of humans. Data visualization, data exploration, and visual data exploration require visualization tools for the benefit of humans.
Data Visualization Defined
Visual data exploration and data visualization both help us to visualize and analyze data, especially when it comes to large datasets. So, what’s the difference between the two?
The vital concept to digest is that all forms of data exploration involve the very first step of analysis. Data visualization, on the other hand, does not have to be part of the first step. Therefore, data visualization can have a deeper analysis of graphic representation because it does not have to be as immediate as data exploration.
Data Analyst Uses
Whether the discipline in question is data exploration or some other data science, there are so many data analyst uses:
- Univariate analysis
- Bivariate analysis
- Multivariate analysis
- Big data exploration
- Data analytics
- Data discovery
- Visual exploration
- Data mining
- Feature engineering
- Predictive analytics
- Business analytics
- Machine learning
- Deep learning
- Representation learning
The possibilities of data analysis are exponentially limitless. It may be daunting to attempt to deal with all of this unstructured data and solve what is becoming an information overload problem for humans. Regardless of the challenge, data sciences is working to make computers smarter so that humans don’t have to work harder.
