4 Data Analysis Techniques in Businesses
Many data analysis techniques can be used in a business, such as analyzing market data, consumer data, marketing data, and many more. In this digital era, data has become the most important asset that every business must have.
Through this article, you will learn about data analysis techniques that can be used by every company. But before that, you need to know what is meant by data analysis first. Let’s see the full explanation below!
What is Data Analysis?
Data analysis is the process of examining, cleaning, transforming, and modeling data to get useful information, informative conclusions, and supportive decision-making results. In today’s business world, data analytics has a role to play in making more scientific decisions and helping businesses operate more effectively and efficiently.
Data analysis can reveal trends or metrics that might not be visible to the naked eye. The data can then be used to optimize processes thereby increasing the overall efficiency of a business or system. For example, factories often record runtime, downtime, and work queues for various machines and then analyze the data to better plan workloads so the machines can operate at their best.
Another example of the use of data analysis techniques is in game development companies that use them to set a reward schedule for players which keeps the most players active in the game. Content companies also use a lot of data analysis to keep audiences clicking, watching, or similar activity on content to increase their number of interactions.
Read Also: What is Big Data
Data analysis is very important because it can help businesses optimize their performance. Applying data analytics to business models means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. Companies can also use data analytics to make better business decisions and help analyze trends and customer satisfaction, which can lead to new, better products and services.
Data Analysis Techniques in a Business
The best data analysis technique for each company will of course vary depending on the level of development and goals to be achieved. There are at least four data analysis techniques that are often used by many companies, including:
1. Descriptive Analysis
Descriptive analysis is the simplest data analysis technique and is the foundation of other data analysis techniques. The descriptive analysis allows you to draw trends from raw data and succinctly describe what happened or is happening. Descriptive analytics answers questions about what is happening now.
For example, imagine that you are analyzing data for a game company and find that there is a seasonal spike in sales for one of its products, namely the type X video game console. Here, a descriptive analysis might tell you, “This type X video game console is experiencing increased sales. in October, November, and early December each year.”
To present the results of a descriptive analysis, you can visualize the data, such as using charts, graphs, or even a map. The data visualization that is performed must be able to show trends in the data such as decreases or spikes in a way that is clear and easy to understand.
2. Diagnostic Analysis
Diagnostic analysis can explain the reasons behind the occurrence of a phenomenon. With slightly more complex analysis, this analysis technique includes comparing coexisting trends or movements, uncovering correlations between variables, and determining causal relationships where possible. The diagnostic analysis is useful for getting to the root causes of an organization.
Continuing with the example above, you could dig into the demographic data of users of a type X video game console and find that they are between eight and 18 years old. However, those who buy it tend to be between 35 and 55 years old. This analysis reveals that one of the main motivators for consumer to buy a type X video game console is the console’s potential as a gift for children. The surge in sales at the end of the year may be due to the holiday schedule and Christmas where children are often given gifts by their parents.
3. Predictive Analysis
Predictive analysis is used to make predictions about future trends or events. By analyzing historical data and trends, you can make accurate predictions about what the future may hold for your company, from the most likely to the least likely.
For example, knowing that sales of the type X video game console have spiked in October, November, and early December each year for the last decade can give you a lot of data to predict that the same trend will follow next year. Backed by increasing trends in the video game industry as a whole, this is a reasonable prediction to make. Making predictions for the future can help companies formulate strategies based on the most likely scenarios.
4. Prescriptive Analysis
The prescriptive analysis will help a company to prepare for the next business steps. The prescriptive analysis takes into account all possible factors in a scenario and suggests actions that can be taken next. This analysis technique can be very useful when making data-driven decisions.
After seeing the example of the type X video game console listed in the section above, what should your team decide given the predicted trend? You can run an A/B test with two ads, one serving the product’s end users (children) and the other targeting consumers (their parents). Data from these tests can inform these companies as to how they can further utilize the data they have obtained. You can also step up your marketing efforts in September with holiday-themed messages to try to extend the spike into the following month.
While you can perform prescriptive analysis manually, machine learning algorithms are often used to help decipher large volumes of data. The smaller the amount of data, the more optimal the recommendations for the next steps that can be taken.
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