Using complex statistical methods to draw inferences and make conclusions in data analysis can be challenging. But as the use of technology in data science increases, many tools emerge to analyze raw data in a much more manageable fashion. This convenience we enjoy today has drastically reduced the burden of technicalities involved in data analysis.
Generally, data analysis involves a series of steps, including data exploration. The more we understand these steps, the more we can appreciate their value and potential for analytics. On that note, this article throws more light on data exploration and its benefits.
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Data Exploration Defined
Data exploration plays a very crucial role in data analysis. But not many people know this specific role. Quite often, typing “what is data exploration” into any search bar brings up tons of generalizations to make the topic more confusing. But take it this way. Data analysis often involves large volumes of relevant data from various sources segregated into different variables. It’s only right that the first step focuses on data cleaning and data preparation.
And that’s where data exploration comes to play. Data exploration involves the deconstruction of a company’s data to uncover any form of variance, correlation, and points of interest. Also known as exploratory data analysis, data exploration helps data analysts answer complex questions like the typical value of an attribute, etc.
These answers break grounds for data analysts to gain a deeper analysis of large data. The more you know about a dataset, the easier it becomes to prepare it for further analysis. That’s why, in the exploration process, understanding the data is the initial step. It’s only after this step that an anomaly or outlier can be detected. And the exploration process doesn’t end until all these inconsistencies that can render a database redundant are eliminated.
There are many approaches in data management and data science. Even though the process is often similar, all of them may not use the same data exploration technique. In master data management, for instance, data handlers can use both manual and automated efforts to explore metadata.
Manual data exploration tools are often open-sourced. A typical example is Excel for the visualization of data. But automated data exploration options may have visualization algorithms reserved for software proprietorship.
Why You Need Data Exploration
Data quality is indispensable in data consumption. Without quality data, businesses risk inaccurate decisions. As such, many databases have in-built quality checks at different data usage points influenced by consumer feedback. Data exploration can be the most effective means to rid a database of quality issues like gaps, anomalies, etc. What’s more beneficial is how data exploration ensures quality early on in the data cycle before it is deemed ready for use. And in situations where any data becomes obsolete, exploration makes it easier to purge a database and ensure access to relevant data at all times.
Having to dig through a database after a breach can be a challenging task, especially if there’s no central entry point to the database. Early detection of breaches accelerates data security efforts and helps solve issues before damages become extensive. Data exploration resources afford data handlers a bird’s-eye view of their database. That way, they can adequately control what works and what has become exposed to unauthorized access in real time.
Businesses rely on effective decision-making to attain optimum efficiency and an edge over competitors. A huge part of effective decisions depends on real-time data access and the available data quality to aid the process. Data exploration tools can scale down big data to manageable sizes. This way, businesses can tailor any situation to help decision-making a great deal.
