Amassing and organizing data is a critical function of many businesses and organizations. There is an essential need in the modern world to gather and organize huge amounts of data. Businesses need to analyze and report on more data than they ever have before. Using data to make informed decisions is just good business practice.
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Data analysis is the process of:
- gathering
- organizing
- analyzing
- reporting on data
A key component of taking a comprehensive approach to data analysis is data exploration. Data exploration is one of the first steps in data preparation.
In short, data exploration is defined as the process in which data analysts explore data using statistical techniques and data visualization. The goal is to describe the data using features like quantity or size to develop an understanding of what the data is saying.
But as with anything in the field of Big Data, there is much more to the story. Let’s examine data exploration in more detail.
Data Exploration Basics
In the grand scheme of data analysis, data exploration is the first step. Typically, data exploration involves the identification of the primary characteristics of a dataset. Once those characteristics have been identified, data analysts can then summarize the data in the set.
When speaking of a dataset, we’re talking about a collection of information that is related yet composed of separate data elements. Those elements can be better understood if they are organized or manipulated into some sort of usable informational unit.
In the past, this involved manual organization of the dataset. Today this is frequently done using specialized computer software to speed up the process (though manual manipulation of data is still common).
Data analysts might use data visualization techniques to get a broad view of the data and get an initial understanding of what the data says. Then, manual manipulation might take place, such as using a drill-down method to find anomalies or patterns in the data.
Data exploration isn’t just for finding anomalies, though. Data exploration techniques can be used to identify:
- the structure of a dataset
- the relationships between different variables
- the presence of outliers
Additionally, these techniques can help focus the distribution of data values. The process can reveal points of interest in the data and patterns. This gives analysts a deeper understanding of what’s contained in the dataset.
Sometimes manual scripts can be used with automated data exploration techniques. Scripts can be written in programs like SQL or a spreadsheet program like Microsoft Excel to parcel out and view particular pieces of data. This is a valuable undertaking because data is usually gathered in huge quantities. Data is also typically gathered from multiple sources and is in an unstructured format.
With the use of organizational and data visualization tools, the process of data exploration makes analyzing data easier. Data exploration can save both time and money as it streamlines the process of data analysis. Analysts can build improved familiarity with the information, allowing them to search and find information in a dataset with much greater speed.
What is Exploratory Data Analysis?
Let’s take this a step further and examine exploratory data analysis.
This is a specific technique used in data exploration to develop an understanding of multiple facets of the data. Think of it as the process of summarizing the data. Data scientists use it to:
- explore data
- break it down
- summarize its essential qualities
Additionally, exploratory data analysis enables data scientists to:
- identify inconsistencies
- test speculations
- check suppositions
Likewise, exploratory data analysis gives data scientists the ability to observe what’s in a dataset, acquire information on the factors in the dataset, and explore their connections as well.
This process can be used to find exceptions in the data, find significant elements, gain new knowledge, and recognize mistakes.
Why is Automated Data Exploration Important?
The evolution of technology, particularly with the internet, smartphones, and other web-enabled devices, means that there is more information available more quickly than ever before. But it’s too much data to explore manually.
Thus, automated data exploration gives organizations a much quicker means of analyzing all that data.
For example, the automated component of data exploration can include profiling data. This process aids in placing raw data into a more structured form as a precursor to a manual examination and consideration of that information. In other words, the automated portion takes care of a lot of menial and time-consuming tasks, such as:
- Cleaning the data
- Handling outliers and missing values
- Handling imbalanced data sets
Python libraries like pandas profiling enable these processes to be automated. By automating these processes, you can save an enormous amount of time and put your attention elsewhere in the data exploration process.
Importance of “Data Drilldown”
The automated element of data exploration typically is followed by what is called “data drilldown.”
Data drilldown is a manual process that is utilized to identify any patterns or anomalies identified through the automated component of data exploration.
Data drill down involves viewing the raw data after the conclusion of the automated process. Data drill down may also necessitate the use of spreadsheets to consider raw data in something of a more organized manner. In addition, as discussed earlier, manual scripting and specific queries into the data may be necessary as part of the effort to identify patterns or anomalies.
Data Refinement
The final element of comprehensive data exploration is called data refinement. This involves what people involved in the process commonly called “pruning” or “refining” data.
Unusable elements of collected data are removed from the aggregate, an endeavor called “data cleansing.” The poorly formatted data is refashioned and relevant relationships across datasets are defined.
What Tools Are Used in Data Exploration?
As noted a moment ago, there are manual data exploration tools as well as computer software programs that can be utilized for data exploration.
From the perspective of manual data exploration, popular tools include scripts that analyze raw data or spreadsheets to manually filter data into an organized form.
For example, you can use Microsoft Excel to create simple charts. You can also use Excel to view raw data and identify correlations between two variables. Chi-squares, stacked column charts, and two-way tables are additional features in Excel that enable you to undertake data exploration.
Automated data exploration tools are much greater in number.
Data exploration software runs the gamut from complete platforms to data visualization software to business intelligence suites. These types of programs provide data analysts with scatter plots, bar charts, and various types of graphs (among many other visualizations) that facilitate more organized and efficient exploration of data.
Some popular software programs for data exploration include:
- Matplotlib – This program essentially recreates the graphics from MATLAB, but in a much simpler form. Matplotlib also includes robust support for visualizations. It has the advantage of being very fast and efficient. It is open-source with cross-platform support. A disadvantage of Matplotlib is that plots are only static – there are no interactive plots. Likewise, to make a custom plot you have to do a lot of repetitive coding.
- Pandas – this is one of the most popular Python libraries for data exploration. It began as a tool for quantitative analyses of financial data. Today, it’s most often used with table format data, like .xlsx or .csv. Pandas supports series and dataframe data structure types and gives you an easily readable representation of data. It has robust file format capability and is good at handling large datasets. It doesn’t, however, work well with 3D data, and indexing can be slow in series objects.
- Seaborn – This data exploration tool is based on Matplotlib. It allows you to create beautiful, informative charts with very little effort. Seaborn integrates well with pandas and allows you to use categorized plots directly. This type of plot is self-contained and provides the opportunity for alteration, such as adding legends or axis labels automatically. The advantage of this program is that plot customization is extremely easy. However, plots are not interactive.
- Bokeh – This Python data visualization library gives you the capability of creating interactive plots and charts. You can create plots and charts that are powered by JavaScript without writing and JavaScript code. Bokeh also offers interactive support like selecting, panning, and zooming within the plot. That said, the interactivity options are somewhat limited and it doesn’t yet have 3D graphic support.
- Scikit Learn – What began as a simple summer code project is today one of the most popular robust libraries for data exploration. Scikit Learn is built on Python and uses NumPy, SciPy, and Matplotlib. Because of this, it has a large collection of tools that includes data classification, clustering, preprocessing, and regression, just to name a few.
In addition to these software options, two programming languages are most frequently used for data exploration: Python and R.
There are many benefits of conducting data exploration in Python including:
- integration with common tools
- it has a great deal of support from a large community of users
- it offers a large library
When used with Pandas, Python makes data exploration analysis even easier by providing:
- Time series functionality
- Reading and writing tools for data
- Aggregating data
- Subsetting of large data sets
- Merging and joining of datasets
- Pivoting and reshaping datasets
- Hierarchical axis indexing
- Intelligent data alignment
These are just a few examples of the many functionalities Python and Pandas offer.
Who Uses Data Exploration?
Historically, what is known as data exploration today was a prime focus for statisticians. In this day and age, data exploration is more widely undertaken. Data exploration is the work of such professionals as data analysts and data scientists.
The data scientist represents a relatively new professional designation. Data scientists tend to be found in larger companies and other types of organizations, including governmental agencies and some nonprofit entities.
Whoever is conducting data exploration is taking part in an important process – converting mountains of numerical data into something easier to understand.
The challenge, of course, is to give meaning to thousands, tens of thousands, or hundreds of thousands of data points. Data exploration helps narrow the focus while data visualization techniques help bring the most important data to life through:
- Shapes
- Colors
- Lines and points
- Dimensions and angles
Doing so allows data scientists and data analysts to visualize the data more effectively, derive meaning from it, cleanse it, and present streamlined information.
How are Data Mining and Data Exploration Related?
When analyzing large amounts of data, you can mine it, which is an automatic process, or you can explore it, which is the manual equivalent.
Data mining refers to finding and extracting patterns in the data by using various algorithms. On the other hand, data exploration offers direction for making additional statistical treatments to the data.
Essentially, both processes are highly similar, and the terms are sometimes used interchangeably.
Also highly similar are the processes of data exploration and data examination. In undertaking data examination, you look at the internal consistency of the dataset. Doing so allows you to confirm the quality of the data before further analysis.
Another process that’s related to data exploration is data discovery.
Once data exploration has been done and the data is refined, data discovery can be undertaken. This process is used by businesses and organizations to answer extremely specific questions.
To provide the end-user with the level of specificity that’s needed, data discovery looks for patterns, sequences, and trends. It seeks out clusters of data, uses time-series analysis, and offers a means of visualizing data.
Data Exploration is a Critical Part of Data Analysis
In the final analysis, the various elements of data exploration are designed to create a meaningful, understandable, and usable mental model.
It is also meant to achieve a suitable definition of basic metadata, which includes:
- structure
- relationships
- statistics
In layperson’s terms, the ultimate objective of data exploration, and the application of its parts, is to make once disparate datasets truly usable.
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