Information is considered a valuable commodity in this day and age of digitization. There is a bombardment of information daily; hence, a need to prepare data and assess a data source effectively is in demand.

Data analysis and visualization using spreadsheets is a skill set very much in demand in the workforce. Using spreadsheets for data analytics can bring data to life using custom templates. Simple Sheets offers thousands of sheet templates that you can use for data analysis. Work will be more efficient, automation possible, and create data models for effective data linkage.

This article will discuss how you can use Excel spreadsheets for data analytics, methods of analysis and the formulas or functions you need to maximize the use of spreadsheets for data analysis.

Analyze Data Using Microsoft Excel

Microsoft Excel is a popular program for data analytics. Data analysis is a process where the raw data is cleansed, transformed, and analyzed to achieve usable and relevant information for making wise business decisions.

Data analysis also concerns Excel power tools, data collection, organization, tools, techniques, and storage. Data analysis can transform raw data into statistical figures and information that are meaningful to businesses.

You must first be comfortable using the Excel user interface to encode formulas and functionalities. You can then easily navigate around the spreadsheet to visualize data.

Data analysis can be presented in different visual formats, such as Excel charts, graphs, tables, and images. Excel’s built-in pivot tables are a popular data analytic tool.

Several Excel functions can be used to analyze data, such as conditional formatting, data model, financial analysis and function, Power Pivot, and more. Some of these functions will be further discussed in this article.

5 Excel Data Analysis Functionalities and Formulas

Data analysis is a challenging process to do. It often involves using advanced Excel functions to develop the correct analysis.

Here are some advanced excel functions that you can use to interpret data in Excel:

1. Concatenate Formula

The concatenate formula may seem overwhelming if you hear it for the first time. On the contrary, this formula is both powerful and straightforward to understand and use.

This formula combines data from several cells into a single cell. The formula is =CONCATENATE(data range).

2. IF Functions

There are several IF functions that are very much used in data analysis. As a data analyst, you will be using IF Functions regularly. It is best to familiarize yourself with the common IF functions below:

SUMIFS is a familiar and commonly used formula in data analysis. This function is not simply about adding values in a range of cells. The SUMIF function is used when adding data according to multiple criteria. The formula is =SUMIFS(sum_range, range1, criteria1, [range1],…].

This formula is used for getting the average value based on one or more parameters. The formula is =AVERAGEIFS(avg_rng, range1, criteria1,…].

This powerful Excel function counts the values that can satisfy a set of conditions. This function doesn’t require a sum range. The formula for is =COUNTIFS(range, criteria).

3. VLOOKUP and HLOOKUP Functions

VLOOKUP or Vertical Lookup function is used when looking for a specific value in a column and to return the value from another column but in the same row. The formula is =VLOOKUP(lookup_value, table_array, column_index_num, [range_lookup]).

The HLOOKUP or Horizontal Lookup function looks for a particular value in the top row of a data table and returns the value from a row in the same column. The formula is =HLOOKUP(lookup_value, table_array, row_index, [range_lookup]).

4. Find and Search Functionalities

You can use the find function when you return one text string’s position within another. If encoded incorrectly, it can result in a #value error. The formula for the find function is =FIND (find_text, within_text, [start_num]).

The search function, on the other hand, is used for searching for anomalies and unique identifiers. For instance, if you are going to search for a particular word like “Small”, the results can be “SMALL” or “small”. The formula for the search function is =SEARCH (find_text, within_text, [start_num]).

5. Rank Function

The rank function is one of the oldest Excel functions that you can use for data analysis. The rank function can quickly show how values in a data set ranking according to an ascending or descending order. In business, this function can be used to monitor which customer has placed the most orders in a month or year.

5 Methods for Data Analysis in Excel

An Excel spreadsheet is versatile; you can apply several methodologies for data analysis, whichever you find convenient to use. Below are the five common methods used for data analysis using Excel spreadsheets.

1. Data Visualization

The use of data visualization helps make your spreadsheet more visually appealing. You can even create stunning interactive dashboards. Charts can be modified, adjusted, and provide legend location and data labels.

Excel spreadsheet allows you to create different types of charts such as line chart, column chart, scatter plot, and more.

2. Conditional Formatting

Conditional formatting helps highlight cells that are considered important. With conditional formatting, you can apply different colors to a cell, change the font, or add symbols beside the cell according to predetermined criteria.

Conditional formatting can help you with data bars, icon sets, searching for duplicates, comparing lists, checklists, and making Heat maps.

3. Pivot Tables

Pivot tables are considered the most practical method of data analysis. It can be used to access essential data from a huge dataset. The use of Pivot tables helps you summarize data and create dynamic reports.

4. Data Validation

Always encode valid data values to avoid seeing error messages on your spreadsheet. Setting up data validation values for a particular cell is possible and helpful for data analysis. You may receive a message prompting users about acceptable data values to encode, which can make data validation faster.

5. Data Cleaning

Before you begin data analysis, you must clean and organize the data you have gathered from multiple sources. You can clean data in Excel using three approaches – using text functions, containing data values, and containing time values.

Final Thoughts

Working with spreadsheets can be a repetitive and time-consuming task. However, Microsoft Excel has undergone several updates to improve user experience. Some of the significant upgrades of Excel include Power Pivot and Power Query.

Data analysis and visualization using spreadsheets have proven beneficial across various industries. Data analytics is not easy, and taking lessons or short courses in Data Science can help you improve your spreadsheet skills from basic to advanced.