Intro’

Did you know that 90% of the world’s data was generated in the last two years alone? That is to say, approximately 402 million terabytes of data are created every day. The world is literally drowning in data. In the context of data, “created” means data that has been generated, copied, consumed, or captured. Individuals or organizations who will be able to make sense of this data will (without a doubt) hold the key to the future.

As a result of the growing need to make sense of data, Data Analytics has emerged as a key field enabling individuals and/or organizations to convert their raw data into insights. By leveraging Data Analytics, organizations such as businesses get a deeper understanding of their products, processes, or services. When insights obtained from Data Analytics are connected to action, it becomes easier for businesses to craft personalized experiences for their customers, optimize their operations, and increase efficiency.

Data Analytics refers to a broad term consisting of data collection, validation, visualization, analysis, and communication. Of importance in this article is Data Analysis which is a field and a profession by itself within the data space. While Data Analytics is a more expansive term including the above processes, Data Analysis is just a subset limited to the actual extraction of meaning from data. A detailed discussion on data analysis and a guide on how to become a data analyst has been given below.

What is Data Analysis?

Data Analysis is the process of extracting meaningful information from data. As the data available to organizations continues to grow, the need for effective and efficient processes and ways for harnessing value from it becomes essential. As a result, businesses need to master the process and key steps in data analysis in their quest to support their data-driven decisions. This process is outlined below;

Stage 1: Identifying Business Questions
At this stage, the business should craft business questions and understand how data can be used to answer the questions.

Stage 2: Collecting Data
This stage involves collecting raw data or putting together datasets that will be used to answer business questions. In a business context, data may come from different sources. Examples include; conducting surveys, Customer Relationship Management (CRM) applications, software, social media applications, or other secondary sources such as government records or publicly available datasets.

Stage 3: Cleaning Data
Raw data is not always clean. It is often characterized by imperfections such as spelling or punctuation mistakes, anomalies, incorrect formats, missing field values, or duplicated records. It is believed that the outcome of any data analysis project is as good as the data used, that is, garbage in garbage out. Common activities involved in this stage include;

• Removing duplicates.
• Handling missing data.
• Handling outliers, i.e., data points significantly far away from the rest of the data.
• Fixing structural errors, e.g., typos, poor naming conventions, or incorrect punctuation.

To avoid false conclusions that may result from working with unclean data, businesses need to create a culture of quality data. This may be achieved by defining what quality data business to the business and thoroughly documenting some of the tools that can instill a culture of quality data.

Stage 4: Data Analysis
At this stage, (clean) data is manipulated using different tools and techniques to discover trends, patterns, variations, and correlations. Common techniques used in data analysis include; descriptive, predictive, diagnostic, inferential, quantitative, qualitative, and exploratory data analysis. Common tools used during this stage include; Microsoft Excel and Power BI, Tableau, Python, and R programming languages, and SAS. The tool to choose for your data analysis needs may largely be determined by factors such as capabilities.

Stage 5: Interpretation and Communication
This is the stage where businesses establish whether data can answer the formulated business questions. Depending on the questions developed, a business can make recommendations based on its data.

Data Analysis in Action

Here is an example where data analysis (or analytics at large) has been applied to answer business questions;

Microsoft on Improving Collaboration and Productivity
In 2015, Microsoft needed to improve the collaboration and productivity of its engineering teams. The company understood the need for face-to-face collaborations with its staff and how the same could be used to improve performance and boost collaborations. The Workplace Analytics Team in the company found that moving teams closer reduced the distance they had to cover for meetings. Through relocations of teams, the company was able to save more than $500,000 per year on employee time.