A Guide to Exploring Data

In an organization, nearly every action a person makes creates data. Unimaginable amounts of data are produced every day by every company in the world. Harnessing the power of this data allows businesses to gain insights into their workflow and make predictions about future scenarios.

Data is organized and read by analytical platforms that can extract pertinent information from the mass. With so many moving parts in the data management pipeline, it can be challenging to pinpoint where these processes all begin. In short, exploring data is step one.

What is data exploration?

Even though every organization will have different data analysis processes, data exploration is always step one. During the exploration phase, data analysts utilize mathematical and statistical techniques to characterize data adequately. Elements of the data that will be acknowledged include its quantity, quality, and nature.

What kind of data is explored during data exploration?

There are two primary types of data. The first type of data is unstructured data. Sometimes, unstructured data is referred to as “raw data.” Raw or unstructured data is usually large in quantity and completely unorganized.

It can be derived from various sources. These immense amounts of data are difficult to understand and apply in this state. Accordingly, data analysts use data visualization and exploration to develop a more organized and comprehensive view.

How is data explored?

There are several different means of data exploration. Each of these means typically falls into one of two categories: manual analysis and automated analysis. Manual analysis is done by data analysts who sift through large amounts of data and identify critical components.

Automated data exploration techniques involve automation software solutions. These solutions organize and visualize data so that it can be applied to different scenarios. The primary goal of data exploration is to gain further insight into the following:

  • Relationships between data variables
  • The presence of outliers
  • Patterns in data sets
  • Particular points of interest

Why is it important to explore data?

It is difficult for people to assign value to the large amounts of unstructured data that businesses produce. Data exploration provides visual structure to data so it can be better understood. It helps data analysts draw conclusions and define data sets in a way that is beneficial to business processes.

Without data exploration, many relationships and cause-and-effects would go undetected, robbing organizations of the ability to predict and prepare for the future.

Who can benefit from data exploration?

Any entity that produces data will benefit from data exploration. As mentioned above, every business creates significant amounts of data every day. Transactional data, customer data, employee data, and inventory all contribute to the larger picture of data.

Without examining this unstructured data for insight, you could miss out on opportunities to improve business processes, customer relations, and more. Additionally, your data exploration can be coupled with analytical support. Data analytics can then help you make data-driven decisions for your organization.

Implementing a data management initiative can be highly beneficial to your organization. Since every business is different, it can be difficult to decide which data exploration technique will work best for your company.

It is recommended to consult with an industry expert in data science software like TIBCO for an expert recommendation. Scheduling a consultation or a demo can help kick-start your data management implementation.

First, it will present you with each of the options available to you. Then, experts in the field of data science can examine your current business processes and identify areas of strength and weakness. Lastly, you will be assisted in creating an actionable plan for collecting, organizing, analyzing, and monitoring your company’s pertinent data.