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Data Exploration

As the first step of data analysis, data exploration is the process of reviewing raw data to look for trends and patterns. Data exploration usually involves both manual and automated analyses to define the data and give a basic understanding of its contents.

Data exploration is a great starting point to build your data analysis process, so it’s vital to ensure it’s done properly.

Why is data exploration important for RevOps?

Data exploration allows you to make better data-driven decisions when building or improving sales and marketing strategies. With data exploration, you can start finding the trends and topics your target audience is interested in and better understand your market. Various industries analyze data from public directories, for example, to generate leads, study the competition, and keep user personas updated with fresh data.

You can intuitively start the data exploration process by using simple visual tools to interact with the data and find relevant data points.

How is data exploration used?

Data exploration is particularly useful with large databases since data exploration allows you to examine the data at a high level. You begin to analyze massive amounts of data and determine which sections of the data to focus on. 

Good data exploration tools will usually have data visualization options to express the data in different ways and allow you to find connections and interactions more efficiently.

Data exploration tools

Data exploration tools make exploring and understanding your data easier, even if you lack coding skills.

Microsoft Power BI and Tableau are the most popular data exploration tools. Unfortunately, these require a highly technical setup and are difficult to customize for non-technical users.

Open-source data visualization tools, like Gephi, Weave, and ParaView, may also help you in your data exploration process. However, these require significant technical know-how as well.

Polymer is the perfect tool for no-code setup and customization. You can go from zero to data exploration in seconds.

Common data exploration issues

Since data exploration is the start of your data analysis process, it depends on you to determine which data to use. Nowadays, data is collected at a massive scale, so you will often need to use automated systems to keep the data updated and determine which parts are relevant to the scope of analysis.

You can help ensure good data is chosen by focusing on the right KPIs and metrics. Clearly defining the scope of your data exploration before starting will help you stay on track to find the insights you need.

How to perform data exploration

Start your data exploration process by cleaning your data to ensure it’s relevant and updated. Once your data is clean, you can start researching specific variables and look for relationships between them.

Data exploration tools make it easier for you to analyze and understand data. You can skip the setup time by connecting your data to Polymer and letting the AI find connections in your raw data. Then you can easily explore and begin analyzing it in minutes. You can use tags to summarize data & quickly get an idea of the distribution, making it easy to discover trends and interactions.

Twelve ways you can explore data

1. Unique Value Count

Unique Value count is a metric that measures the number of unique values in a data set. This metric is often used to assess the quality of data, as it can help to identify duplicate values or errors in data.

2. Frequency Count

Frequency Count is defined as the number of times a given element appears in a dataset. In other words, it is a measure of how often an element is repeated in a dataset. Frequency count is a very important metric in data analysis, as it can be used to identify patterns and trends in data.

Frequency Count visualized in Polymer using a horizontal bar chart

3. Variance

Variance is a statistical measure of how far a set of numbers is spread out.

High variance metrics found using Polymer’s Overview block

4. Pareto Analysis

Pareto Analysis is an analytical method used to help decision-makers identify the most important factors in a given situation. It is based on the principle that for many events, roughly 80% of the effects come from 20% of the causes.

Import factors (above average and below average) found using Polymer’s Outlier block

5. Histogram

A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson.

Histogram created using Polymer’s bar chart with a numeric metric on the x-axis

6. Pearson Correlation and Trend between two numeric columns

This is a statistical measure of the linear relationship between two variables. The Pearson Correlation coefficient is a measure of the strength of the linear relationship between two variables. The trend is the direction of the relationship (positive or negative).

Pearson Correlation and Trend visualized using Polymer’s scatterplot

7. Correlation between two specific categorical columns

Correlation between two specific categorical columns is defined as the strength of the relationship between the two variables, where the value can range from -1 (indicating a strong negative relationship) to 1 (indicating a strong positive relationship).

Correlation visualized using Polymer’s heatmap

8.  Cluster Size Analysis

Cluster Size Analysis is a statistical method used to estimate the optimum number of clusters in a data set. It does this by calculating the within-cluster sum of squares (WCSS) for a range of cluster numbers and selecting the number that produces the smallest WCSS.

9. Clustering or Segmentation

Clustering or Segmentation is the process of dividing data into groups or clusters, so that data within each cluster is more similar to each other than data in other clusters. This process can be used to find groups of similar data points in a dataset or to divide a dataset into distinct groups.

10. Outlier analysis for individual numeric column

This is the identification of unusual values in the data that are significantly different from the rest of the values. These outliers can be caused by errors in the data collection process, or they can be legitimate values that are simply rare. Outlier analysis can be used to detect errors in the data, or to find unusual patterns that can be investigated further.

11. Outlier analysis for multiple columns

Outlier analysis for multiple columns is the process of identifying extreme values in multiple columns of data. This can be done by looking at the minimum and maximum values of each column, or by using a more sophisticated method such as the interquartile range.

Outliers found using Polymer’s Outlier block

12. Specialized Visualization

Specialized Visualization is the process of creating and manipulating images to communicate data or information. This can be done using a variety of methods, including: charts, graphs, maps, diagrams, and infographics.

Dependency Wheel visualization in Polymer

Learn more about data exploration

What is Sales Intelligence: 

How to pick a Business Intelligence tool: 

How to use views and sharing: 

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