Companies generate and collect large amounts of data during their business, but if they collect this data without analyzing it, it will not be useful for the company’s work. In our data-rich age, understanding data analysis and extracting insights has become one of the key drivers for success. What is meant by data analysis How to analyze your company’s data
What is data analysis?
Data analysis is the process of systematic application of statistical and logical methods, aimed at describing, clarifying, summarizing, and evaluating data. It is also defined as the process of collecting and modeling data, to extract insights and statistics that support decision-making. Businesses rely on data analytics to analyze their data, to make decisions that may enable them to improve their overall performance.
Data science and data analysis
Data science is known as an interdisciplinary field, it focuses mainly on the scientific study of data, and it can be used in any field such as; retail, healthcare, e-commerce, and many others. It aims to analyze huge amounts of unstructured raw data, in search of strategies that will help push the work of companies forward.
Data science includes many complex tasks such as data modeling, building algorithms from start to finish, managing large teams, and building new data structures and their implementation. Data analysis is one of the disciplines of data science. Although it is a stand-alone field, it is limited; while data science answers large-scale strategic questions, data analysis acts in the narrower role of searching for answers to specific questions.
What is big data
Big Data refers to a large and complex combination of structured and unstructured or semi-structured data, obtained from a variety of sources, and analyzed to give statistics and information used
Examples of quantitative data include:
Personalized e-commerce shopping experiences.
Enhanced medical research from data aggregation.
Learn about retail shopping habits, and optimize products according to them.
Suggestions of videos on streaming applications.
Analysis of ways to reduce congestion in cities.
Learn about education habits for students, schools, and cities.
The importance of data analysis
Data analysis has great value in the work of companies, if used correctly, it helps companies to::
Improved decision making
Companies use data analytics to make data-driven decisions. Analytics gives companies insight that helps them become more efficient by improving their decision-making process. Business Analytics also allows for fully automated decision-making processes, and this provides correct responses instantly when needed.
One of the advantages of data analysis is that it provides statistics about your target customers and about the performance of marketing campaigns, which is very valuable information because it enables you to conduct more effective marketing campaigns in the future, and this certainly helps you achieve better sales.
Better customer service
You can collect customer data from multiple sources such as online stores, retail stores, social media, and other channels. thanks to data analysis, you will be able to turn this data into comprehensive customer profiles that help you gain insights about their behavior and needs, and by extension provide a way better and more personalized custom experience
Types of data analysis
There are four main types of data analysis that are most used today in companies, they are::
1. Descriptive analytics
Descriptive analyzes are analyses that answer the question “What happened?”It is the simplest type of data analysis, and at the same time, other types of analytics are built on it. Suppose you run a sports accessories company, and while analyzing the data you found a seasonal increase in swimsuit sales in the summer months of each year, this is what we can call a descriptive analysis that appears in the form of easy-to-understand and clear graphs, showing data trends.
2. Diagnostic analyzes
Diagnostic analyzes address the following logical question “Why did this happen?”If we go back to the previous example, we will find that the majority of swimsuits sold were suitable for people under the age of 18, although the ages of buyers ranged from 30 to 50 years. When analyzing customer data, we will find out that the main motivation for buying swimwear is, firstly, the hot summer months, and secondly, parents gifting those clothes to their children, and this explains the high sales in these months of the year.
3. Predictive analytics
By analyzing past data, you will be able to get predictions about what you should work on in your company in the future.
Applying the previous example, you will find that swimsuit sales have been rising in the summer months of each year for several previous years, and from here it is possible to predict the same thing to happen next year, which helps you develop future strategies based on the analysis of the predictive data available to you.
4. Directional analytics
Directional analyzes answer the question “What should we do next?”, Which is useful when making data-driven decisions. Returning to the example of swimwear, you will find that the best decision is to increase marketing efforts by implementing marketing campaigns aimed at parents in the month before the start of the summer holidays, which encourages them to buy swimming accessories for their children on vacation before they go to swimming places, and all this will bring your company a seasonal increase in sales in the holiday months.
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