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Data sources have increased, and collection methodologies must adapt to the new age of multiple devices constantly aggregating data. Corporate data enrichment techniques allow firms to modify their decision-making processes through enriched data. This post explores the application scope and benefits of data enrichment. 

What is Data Enrichment? 

Data enrichment means you combine newly acquired datasets with already available company databases to minimize errors and gaps. Therefore, business leaders implement data aggregation services to improve their datasets for more precise business intelligence and analytics. 

Expanding on the previously collected intelligence enables innovation opportunities in marketing, sales, human resources, and finance. For example, you can learn about a competitor’s strategies in greater detail. 

Likewise, organizations improve their methods for personalized marketing and customer service policies by updating their datasets periodically. Well-maintained databases also facilitate broader and more complex applications of data analytics solutions

Benefits of Data Enrichment and Aggregation Services 

Raw datasets often have limited usability. However, data enrichment solutions help you collect disparate data from multiple sources using automation. So, businesses want the following advantages of data enrichment. 

1| Improved Customer Relationship Management 

CRM, or customer relationship management, is essential to foster positive brand perception. Still, an incomplete or obsolete database prevents you from creating meaningful customer experiences. Consider how lengthy forms confuse new clients during checkout on an e-commerce site. 

You want to increase the form submission rate using data aggregation services since they can help enrich consumer behavior data. After all, many enterprises use such datasets to promote popular components of customer experiences. They must learn what the customers want by gaining extensive insights and performing data explorations. 

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2| Eliminate Redundant Data 

Data sources often exhibit varying levels of authenticity and data quality. Besides, different sources have unique timelines for when new data becomes available. However, you want to avoid storing and using irrelevant or outdated datasets. 

Outdated data can skew the results of data analytics solutions. So, data enrichment activities require relevance and freshness filters. These sorting or filtering commands in the data aggregation services prevent old data records from burdening your data storage ecosystem. 

3| Optimization of Costs of Data Management Operations 

Corporations might be adopting cloud computing and storage through virtualized workspaces. Yet, they bear the financial burden of maintaining unnecessary or raw datasets. Companies require only the most relevant and accurate data for insight extraction and strategy evaluation. 

Therefore, spending capital resources on unwanted or redundant datasets is wasteful. The benefits of data enrichment also include the financially responsible management of aggregation and analytics solutions. So, you can utilize the resulting cost savings for more meaningful operations. 

Methods of Data Enrichment 

Data aggregation services incorporate the following techniques to provide data enrichment. 

  1. Data segmentation uses specified rules to group data objects. 
  1. Machine learning (ML) predicts reasonable values to bridge the data gaps. 
  1. Dataset manipulation replaces empty values in the available databases with the new estimates powered by ML. Doing so helps prevent misleading interpretations of trend analysis. 
  1. Entity extraction supports semi-structured data processing. So, you can automatically acquire meaningful insights into more qualitative data. 
  1. NLP means natural language processing, and its advantages include insight identification for unstructured data. So, corporations can efficiently summarize the implications of multiple descriptive texts using NLP. 
  1. Appending involves finding and modifying existing records to add newly acquired data while preserving the integrity of the native database. 

Conclusion 

Enrichment of business data benefits decision-making by expanding the capabilities of data analytics solutions. Therefore, data aggregation firms support the corporate efforts of enriching internal databases to improve products, services, customer experiences, and industry insights

Modern data enrichment and aggregation implementations leverage ML models and NLP. A reputable data partner also provides integrated facilities, combining open-source and proprietary platforms. 

SG Analytics, a leader in data analytics solutions, empowers organizations to enrich their datasets through high-quality aggregation and data cleansing. Contact us today if you require economic data pipelines to optimize business decisions. 

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