Data Preparation
The Data Preparation Stage in the Data Analytics Life Cycle is a crucial step where raw data is transformed into a clean, structured, and analysis-ready format. This stage focuses on aggregating, organizing, and validating data through multiple collection methods, ensuring that analysts have the most relevant and accurate inputs for subsequent modeling and interpretation. Importantly, these methods do not need to occur in a fixed sequence and can be revisited whenever additional or updated data becomes necessary. The first process in this sequence is collecting the data. Following are thre primary methods of data collection:
Data Acquisition
Data acquisition involves gathering information from external sources relevant to the enterprise’s analytical objectives.
• This could include public datasets, third-party data providers, industry reports, market research outputs, or partner system feeds.
• The goal is to expand the analytical scope by integrating data that complements or enriches internal records, thereby offering broader insights into market conditions, customer behavior, or operational performance.
Data Entry
Data entry focuses on recording new data points directly within enterprise systems.
• It can be achieved through automated digital processes, such as CRM entries, ERP transactions, or HR system updates.
• In cases where automation is not fully implemented, manual data entry remains a viable approach, especially for field survey results, paper-based logs, or specialized inspection data.
• Accuracy and consistency are critical, so validation protocols are often embedded into the data entry workflows to reduce human error.
Signal Reception
Signal reception includes capturing data generated by digital devices and connected technologies.
• This encompasses information from industrial control systems, monitoring equipment, IoT-enabled sensors, and smart infrastructure.
• Real-time or time-series data from such sources can provide highly granular insights into operational states, environmental factors, or customer activity.
• Integration with analytics platforms often requires converting device outputs into standardized formats that align with the enterprise’s data schemas.
Overall, data preparation is not a one-time activity but a dynamic phase that adapts to the changing demands of the analytical process, providing the reliable input necessary for deeper exploration and modeling. The data preparation stage is inherently non-linear.
• The three methods—acquisition, entry, and signal reception—can occur in any order depending on project needs, resource availability, or data readiness.
• It is common to revisit these methods to incorporate new information, refresh outdated datasets, or address gaps that become apparent during later analysis stages.
• This iterative approach ensures that the data foundation stays relevant, accurate, and aligned with evolving analytical goals.