1. Collecting Data
The first stage of any data analytics process is data collection. This involves gathering of all the information that your business require, both internally and externally. The data can come from a variety of sources, including:
• Operational systems: This includes data from systems such as ERP, CRM, and HRMS.
• Transaction data: This includes data collected from point-of-sale systems, e-commerce platforms, and financial databases.
• Web and social media data: This includes data from web analytics tools, social media platforms, and online surveys.
• Machine data: This includes data from sensors, RFID tags, and other connected devices.
2. Analyzing Data
After the data is collected and cleansed, it is ready for analysis. Data analysis is the process of using statistical techniques to examine the data and extract useful information. The goals of data analysis vary depending on the type of data and the business objectives. For example, data analysis can be used to:
Identify patterns and trends: Data analysis can help you in identifying customer behaviour or market demand. This information can be used to make better decisions about products, pricing, and promotions.
Predict future outcomes: Data analysis can be used to build predictive model, that can forecast future events. This information can be used to make decisions about inventory, staffing, and marketing.
Detect anomalies: Data analysis can help you identify unusual patterns that may indicate fraud or other problems. This information can be used to take corrective action to prevent losses.
3. Reporting results
After the data has been analysed, the results need to be reported. This step is important because it allows you to share the insights with others and make decisions based on the findings.
There are a variety of ways to report data analytics results, depending on the business and the audience. Some common methods include:
• Presenting findings in a dashboard
• Generating reports
• Creating infographics
4. Improving processes
Data analytics is an ongoing process, not a one-time event. After you have collected and analysed the data, you need to take action to improve the process.
This step involves making changes to the way data is collected, processed, and analysed. It may also involve changing the way decisions are made based on the data.
5. Building a data-driven culture
Data analytics is not just about the data. It’s also about the people who use the data to make decisions. It also involves creating a culture of accountability in which everyone is responsible for using data to make better decisions.
Here are some tips on how to build a data-driven culture:
• Make data accessible.
• Train employees on how to use data.
• Encourage a data-driven mindset.
• Create a culture of accountability.