How to Analyze Data That Impacts Business Decisions

In the 21st century, data has become a new currency that is an important and very valuable asset for businesses. However, only companies that do data analysis correctly can feel the valuable benefits of data.

The benefits of data in business can be the basis for making decisions that are not just based on assumptions. With data, decisions taken will be more measurable.

The existence of data is also an important lesson for businesses, for example when conducting a marketing campaign, there is content that is successful and not. Businesses can find out the cause from the existing data.

Data is just a dead number that means nothing if it is not analyzed properly. Data analysis is a systematic process of collecting, organizing, examining, and interpreting data in order to gain insight, make decisions, and solve problems.

In data analysis, various techniques and methods are used to identify patterns, trends, and relationships that can provide useful information for decision making or to gain a better understanding of certain phenomena.

So, what are the best practices for data analysts in conducting data analysis that can be useful for business? Here are the stages of data analysis.

1. Understand Business Objectives

Before starting the analysis, it is important to understand the business context and the goals the company wants to achieve. The mindset we need is a mindset to be oriented towards business goals.

This can include increasing sales, optimizing costs, or increasing customer satisfaction. The analysis carried out must be in line with these business objectives.

To find out the business objectives to be achieved in this data analysis process, a data analyst will communicate with stakeholders from various departments, such as marketing managers, product teams, or finance teams, to understand the problems they face and the goals they want to achieve.

A data analyst will ask questions that are more focused on, "What do you want to achieve?" and "What problems need to be solved?". This is the earliest step in every data analysis process.

2. Data Collection and Cleaning

Proper data collection is very important because good business decisions can only be made based on accurate and complete data.

To obtain accurate data, a data analyst will identify relevant data sources (eg, company databases, customer data, transaction data, operational data).

Next is collecting data through various sources such as APIs, internal databases, or shared files. This process includes verifying the accuracy and completeness of the data and avoiding data errors that can affect the results of the analysis.

After collecting data, data cleaning is needed. The data cleaning process is carried out to handle problems such as missing values, duplicates, or inconsistencies. Using tools such as SQL, Microsoft Excel, Python, and the like to clean and format data.

3. Descriptive Data Exploration and Analysis

As a data analyst, it is very important to have a strong sense of curiosity. Data analysts need to explore data in depth to find patterns, trends, and insights that can help solve existing business problems.

The steps taken are to conduct descriptive analysis using basic statistical techniques, such as mean, median, standard deviation, and data distribution.

This provides an overview of the state of the data and helps in identifying patterns or anomalies.

Data visualization tools such as Power BI, Tableau, or matplotlib in Python help depict data in graphical form that is easy for stakeholders to understand.

4. Hypothesis Testing and Inferential Analysis

Every analysis should be driven by a testable hypothesis. Data analysts do not just accept data, but they will look for ways to test existing theories and assumptions, and see if the results are valid.

They will conduct exploratory analysis by developing hypotheses that will be tested further. For example, "Does a change in product price affect sales?"

To test this, they use statistical tests (such as t-tests or ANOVA) to ensure that price changes significantly affect sales.

If necessary, they will use regression to identify the relationship between the dependent and independent variables.

5. Modeling and Prediction

The goal of data analysis performed by data analysts is to provide insights that are not only relevant, but can also be used to make better business decisions.

If previous analysis results show a significant relationship, data analysts will proceed to build predictive models using algorithms such as linear regression, decision trees, or machine learning (such as random forests or XGBoost) to forecast future business outcomes.

They will also validate the models using cross-validation techniques to ensure that they are robust and not overfitting to the data.

6. Visualizing Results and Delivering Insights

After gaining insights from the data, the next step is to present these findings in a clear and compelling way to stakeholders, especially those who may not have a technical background.

What data analysts do is use interactive dashboards with tools like Power BI or Tableau to visualize data in an easy-to-understand format.

Graphs and tables are created to simplify the complexity of the data and explain the findings directly. Reports are arranged in a format that focuses on business insights so that the management team can easily make faster decisions.

7. Follow-up and Implementation

The analyzed data is meaningless if it is not followed by real actions that support business goals.

Based on the insights found, the data analyst will work with related teams (for example, marketing or product development) to translate the results of the analysis into concrete actions.

For example, if the analysis shows that a particular marketing campaign increased customer conversions, they will recommend optimizing or doubling the investment in that area.

8. Continuous Monitoring and Evaluation

Data analysis is an ongoing process. Data analysts will always monitor the results of the actions that have been taken to assess their impact and identify areas for improvement.

They will continue to monitor the data to ensure that the changes implemented are producing the desired results.

If there is a change in market conditions or input from other teams, they re-analyze and update the predictive model. This process allows the company to remain agile in data-driven decision making.

The Right Data Analysis Leads to the Right Business Decisions

If you and your team follow the data analysis steps above, data analysis will not only provide deep insights, but also influence strategies and decisions that improve company performance.

The most important thing is the mindset, the mindset needed for successful data analysis is critical thinking, data-based, and communicative.

With a systematic and iterative approach, each analysis helps the company move faster, more efficiently, and more responsively to market changes.

If so far the data analysis that your company has done has not had an impact on the business, you need to include your data analyst team in the right training.

You can request customized training according to your company's data analysis needs. prasmul-eli provides corporate training programs based on client needs.

This is the era of data as currency, so don't let the data in your company just become dead numbers that are lifeless. Instead, make that data a strategy for your business success.