Business Data Analysis | 2 mins read

Business Data Analysis- Process and Real Life Examples

business data analysis process and real life examples
Chloe Henderson

By Chloe Henderson

Access to valuable data insights makes all the difference when making vital business decisions. Even if a company has several datasets, the information may not be helpful without proper interpretation.

With business data analysis, organizations can gather, clean, and interpret information from internal systems to generate actionable insights.

How Data is Used in Business

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Businesses use data analytics to enhance their decision-making to improve operations and performance. Analyzing data enables analysts to develop actionable insights on various areas of business, including-

  • Market Trends
  • Product Lines
  • Customer Reviews
  • Process Optimization
  • Employee Performance
  • Marketing Campaigns

Without sufficient data, businesses cannot be confident in their decisions. Relying on guesswork and hunches will not guarantee results as data-based decisions do.

There are four primary types of data analytics that companies can utilize, such as-

1. Descriptive Analysis
Descriptive data analysis considers historical data to determine what event occurred and whether it had a negative or positive impact. This approach typically uses key performance indicators (KPIs) such as revenue, sales, and demand to detect trends.

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2. Diagnostic Analysis

Diagnostic data analysis determines why the event occurred. By defining patterns, management can pinpoint what caused the event and how it could have been prevented or enhanced.

For example, while descriptive analysis may show that website traffic flow increased in August, it won't explain why. The diagnostic analysis would pinpoint exactly which marketing campaign or promotion boosted leads to spike traffic.

3. Predictive Analysis

Predictive data analysis estimates future events based on historical patterns. For example, a retailer forecasting next year's generated revenue will need to incorporate data from the prior years to make an educated prediction. If income has consistently increased over the past decade, they can calculate how much sales will continue to grow.

While this is a simple scenario, predictive analysis can be applied to more complicated models to handle risk assessment, demand forecasting, and future sales.

4. Prescriptive Analysis

Prescriptive data analysis collaborates information from descriptive, diagnostic, and predictive analyses to form a comprehensive plan of action. This sophisticated method optimizes decision-making by generating detailed insights, ideal scenarios, and different approaches businesses can take.

6 Steps of the Data Analysis Process

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The data analysis process utilizes tools, such as management software, to gather and extract valuable information. This enables businesses to form conclusions on their overall performance and efficiency.

To begin the data analysis process, companies need to-

1. Gather Data

First, management should determine what operation they are seeking to improve in order to gather relevant data. They also need to choose which type of data analytics best fits their needs.

For example, businesses trying to determine why an issue occurred should use diagnostic analysis to gather data on processes that may have impacted the event.

2. Collect Data

Once management determines what they need to gather, they can begin collecting relevant metrics and measurements. Each data set should be organized to streamline data cleaning. This includes documenting where each piece of information was sourced from and the collection date.

3. Clean Data
After all of the necessary data has been collected, it is time to filter out irrelevant, erroneous, and duplicated figures. This includes empty data fields and incorrect or unstructured information.

The goal of cleaning data is to ensure that finalized data sets are error-free and ready for analysis. Therefore, if the information is not prepared correctly, management cannot generate reliable conclusions.

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4. Perform Data Analysis

In this step, management determines if the collected data is sufficient or if they need to gather more information.

Once all essential data sets are available, management can use software and analysis tools to detect trends, interpret metrics, and draw conclusions.

5. Interpret Data

After the analysis software breaks down the information, it is time for management to interpret the results.

It is essential for managers to find the best way to summarize their findings so stakeholders can fully grasp the value of the insights. Otherwise, more complex results may be hard to comprehend and therefore overlooked.

6. Create Data Visualization

A great way to represent large data sets and more complex trends is through data visualization such as charts, graphs, and tables. These visual tools help explain patterns, progression over time, and relationships between multiple variables.

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How Big Companies Use Data Analysis

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Business data analysis is used by startups looking to expand and large enterprises adapting their internal processes to improve performance.

Big companies often use data analysis to-

  • Boost Customer Acquisition and Retention
  • Generate Marketing Insights
  • Enhance Risk Management
  • Improve Product Development
  • Optimize Supply Chain Management

For example, Netflix is constantly incorporating real-time data to develop targeted advertising. With over 100 million subscribers, Netflix has access to large volumes of customer data to gain insight into preferences, watch history, and dislikes. This enables the company to promote accurate watch suggestions and keep viewers interested.

Likewise, Coca-Cola uses data analysis to promote customer retention. In 2015, Coca-Cola launched a digital loyalty program to collect more customer data. In addition to discovering preferences, this enabled the company to collect direct feedback from shoppers. Management was able to determine what type of content consumers responded to the most to improve their campaigns and product development.

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