By Sophia Avallone, Data Analyst

Companies constantly strive to gain insights from extensive information to make informed decisions. While advanced technologies play a significant role in the process, mathematics forms the fundamental basis of data analysis. In this blog, we’ll explore how math provides businesses with the necessary tools to transform data into a usable format to extract meaningful information.

How does Data Analysis Work?

Data analysis begins with cleaning and preparing the dataset for analysis. This task must be systematic, utilizing mathematical techniques to identify potential insufficient data and prove normalcy in a sample. The mean, median, mode, and standard deviation are crucial in identifying erroneous data points. Transforming the data through specific operations, like standardization, relies on mathematical reasoning. These math techniques help ensure the reliability and consistency of the data, laying the groundwork for accurate analysis.

Proper data analysis technique requires the use of relational databases, especially as data scales. Discrete math topics such as logic principles play a role in the design of relational database schemas. These principles establish the relationships between tables through primary and foreign keys. Other logic principles are crucial in providing a foundation for efficient and accurate data querying. By applying operators such as and, or, and not, analysts can formulate complex queries that retrieve subsets of data based on specific criteria. Table relationships also allow for table joins, combinations, transformations, and further querying, which can correlate to certain mathematical set theory concepts such as unions, partitions, and intersections. Thinking about table transformations in terms of these math concepts and using visuals such as Venn diagrams can increase understandability. These math concepts are essential for effectively managing and retrieving database data.

During the transformation and analysis portion of the data process, many different coding languages need to be written (depending on the software) to create calculated columns, measures, and advanced formulas to get a deeper understanding of the data. While the number of different coding languages can seem overwhelming, they all have one theme in common: their key functions are fundamental math concepts. While each language will have its own syntax, the core functions such as minimum, maximum, sum, count, average, and modulus will all be used. A basic understanding of these (often simple) math concepts will allow users to switch between languages more efficiently and build complex code out of basic operations.

Math: A Common Denominator

There are ample forms of data analysis, but they all have one common theme: they revolve around math. Quantitative analysis is at the core of data analysis, which utilizes mathematical principles to interpret numerical data. From basic arithmetic operations to more complex algebraic concepts, such as the creation of equations, standard data practices, including calculated columns and formulas, are rooted in theories from these areas of math. Statistics is another mathematical field that plays a vital role in data analysis. Statistical techniques enable businesses to draw inferences and test hypotheses based on sample data. Easy-to-use tools such as Excel’s built-in Descriptive Statistics can be used on simple data sets to draw valuable conclusions. Math principles also aid in another form of analysis being visualizations. Understanding ways to summarize and aggregate data to create visuals such as charts can help viewers identify patterns and trends.

Every occurrence worldwide has underlying math foundations, and data analytics is no exception. While advanced tools aid in data analysis, it is essential to acknowledge that mathematics remains the backbone. By embracing mathematical concepts and continually honing our skills, we can unlock the full potential of data analysis and leverage it to drive informed decision-making.

If your business needs help with any part of the data analysis process, contact the Wiss Technology Advisory Team for data support!