What is Data Analysis?
Welcome to my opinion class: What is Data Analysis?
Today I will share my opinion and perspective on data analysis. The major points of discussion are definition of data analysis, objectives of data analysis and applications of data analysis.
The mental image conjured in my mind regarding to data analysis process involves a woodpecker bird diligently pecking a hole in a tree bark to find insects to eat.
The constant pecking of a tree bark is akin to the never-ending process of extracting value from the massive volume of data generated from devices used by humans, social networks used as modern water coolers, business activities and manufacturing equipment. Hammering into the trunks of trees to make holes to extract insects and sap is the lifestyle of any woodpecker bird — the bread and butter it needs to survive.
Data analysis is currently the bread and butter for many data professionals, established behemoths like Amazon, Google and Microsoft, and up and coming businesses focusing on adopting data analysis in their business processes and business models.
Definition of Data Analysis
The billion dollar question that has shaped the foundation of multiple industries in the 21st century. The root elements of the question in rooted on data (raw material/input) and analysis (defined process of transforming the data).
Data analysis is simply a process of extracting value from data via transformation of raw data into meaningful insights that are easily interpretable and communicable to the end user. The process usually involves data collection and storage, data exploration, data transformation, data interpretation and data communication.
Communicating data insights to the end user is the most significant phase of the data analysis process. It represents the final output of the data analysis process. The needs and goals of the data analysis are incorporated in the communication phase, thus adoption of any sap extracted from the tree (data) relies on excellent presentation of the insights to the end user.
Objectives of Data Analysis
Different organizations and institutions adopt data analysis for various reasons. Common goals of incorporation data analysis in business processes include:
· Improve operational efficiencies: production processes, customer interactions and forecasting customer demand.
· De-risking decision-making process: selecting critical business alternatives such as product launches and entering into new markets.
· Optimize customer experience.
· Optimize business models. Some business incorporate data analytics into their business models.
· Enhance unfair advantage.
Applications of Data Analysis
Data analysis, which forms part of data analytics, has a wide array of applications in various industries and departments of a business. Industries such as retail, finance, transport and logistics, insurance, manufacturing, healthcare and telecommunication utilize data analytics in their business processes.
Business utilize data analysis in marketing, sales, logistics, product development, operations and human resources.
Common applications of data analysis include:
1. Product development in retail and finance sector. Historical data for past and current product offerings provide insights on the feasibility of commercial success of new products.
2. Pricing analytics. Adjusting prices based on insights of analysing large volume of transactional data.
3. Predicting customer demand
4. Reducing customer churn. Analyzing customer churn rate enables companies adopt customer retaining strategies that prevent loss of current customers.
5. Improving employee productivity via adoption of human resource analytics
6. Fraud detection and prevention
In summary, I believe every business, small, medium or large is a data business. The value derived from the data can change a venture’s performance on essential metrics such as revenue margins, net profit margins, product quality and customer experience.
Happy wood pecking day for all entities hammering the data tree for valuable insights.
