What is the Difference Between Raw Data and Aggregated Data

Unprocessed data requires proper care and transformation techniques to streamline data lifecycle management. Corporations realize this necessity and employ data aggregation solutions to convert raw data into a more structured intelligence resource. This post will explain the difference between raw data and aggregated data.

What is Raw Data?

Raw data results from extensive data collection exhibiting a hybrid structure due to the differences between data sources. Machine or human has yet to process such data, making it unsuitable for advanced data analytics. Therefore, data engineering services exist to convert the raw data through extract, transform, and load (ETL) operations.

Some of the noticeable features that quickly highlight the difference between raw data from the aggregated data include an absence of logical organization of data points and inconsistency of presentation. So, these properties can confuse the viewer. For example, 1000 photographs, media reports, and interviewee responses related to a natural disaster “event” are raw datasets.

However, modern tools, application programming interfaces (APIs), and data engineering strategies facilitate report summarization. Professional data engineers will also help you and your employees customize the visualized dashboards that simplify categorization and pattern recognition.

What is Aggregated Data?

Aggregated data indicates an environment of manual or computer-aided processing to centralize, reformat, and summarize datasets. Commercial enterprises and IT consulting firms use data aggregation solutions to generate aggregated data using the following technologies.

1. Artificial intelligence (AI) facilitates dynamic data operations for ETL and database optimization. 
2. Machine learning (ML) develops more efficient processes based on self-learning algorithms. Moreover, repetitive usage of ML models enhances the output quality. 
3. Natural language processing (NLP) can extract meaning from unstructured data like descriptive consumer feedback or videos obtained from social media platforms. 
4. The Internet of Things (IoT) increases the scope of data aggregation solutions using sensory nodes and cluster networks. These tools are electronic gadgets operating steady web connectivity. 

Businesses want aggregated data to reduce time spent on number crunching and manually inspecting data points. For example, the sales department might record weekly lead generation and deal closures. Later, aggregated data can convert such extensive sales databases for improved reporting clarity during the quarterly performance reviews.

The Difference Between Raw Data and Aggregated Data

1| Data Lifecycle Stage

A data lifecycle represents a logical and ordered set of activities that affect the data throughout its useful service life. Data lifecycle management (DLM) comprises the strategic implementation of digital applications and hardware resources to improve how organizations use data.

Raw data differs from aggregated data because it belongs to the initial stages in the data lifecycle. Meanwhile, data engineering services process the raw data to provide their clients with aggregated data. Therefore, it belongs in the later stages of data lifecycle management.

When the intelligence gathering is over, you will get the raw data, and data engineers will create an unprocessed data repository. However, most consultants deliver simplified reports to save the client’s time and enable faster decision-making. These reports contain aggregated data.

2| Data Structure

The difference between raw data and aggregated data lies in their structure. Consider a data point specified by the data engineering services. Data points are numerical values or text strings describing an event like scrolling a webpage, receiving orders, or changes in sales revenue.

Raw data is unstructured, depicting data points with an inconsistent format. So, identifying the trends or applying analytical tools can be daunting. However, data aggregation solutions process the raw data to facilitate ease of organization, trend discovery, and business correspondence. The corresponding ETL operations standardize how data points appear in the reports.

Data engineering services will emphasize developing and maintaining the infrastructure vital to transforming raw data into aggregated data to rectify the inconvenient difference between identical data points.

3| Logical Categorization

Categorization and labeling will increase the productivity associated with report generation. After all, users can skip manual navigation efforts by requesting the most relevant intelligence. Nevertheless, raw data often exhibits data quality issues like duplicate entries or irrelevant labeling.

Consulting firms offering data aggregation solutions are familiar with such issues. Therefore, they leverage AI and ML models to recategorize raw data according to the context. Logically categorized data points remove ambiguity from reporting dashboards, making aggregated data qualitatively superior to raw data for analytical purposes.

Conclusion

Raw data belongs to the initial stages of data lifecycle management. Aggregated data will become available in the latter phases. The extent of data categorization in both cases highlights the difference between aggregated and raw data.

Still, corporations require experienced data engineers to convert raw data into more organized summaries. Otherwise, they will experience reporting delays and data quality complications. Access to advanced analytical tools can mitigate these data transformation risks.

SG Analytics, a leader in data analytics solutions, supports enterprises in collecting and processing strategically important data on consumers, competitors, and policies. Contact us today for the latest database management, visualization, and engineering services to realize extraordinary business growth.