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Data completeness in the assurance of overall data quality

Earlier on, the phrase data quality was often connected with how “clean” or “dirty” a particular dataset was. Quality was quantified by how many wrong, incorrectly formatted, or absent values where there in a specific dataset. A primary component of data prep was data cleansing to enhance the quality of data. 

Over the course of time, the meaning of data quality has undergone expansion to factor in extra traits, such as consistency, recency (how updated data is), and reliability. When associated to data governance, data sets used to be labels with “trusted” flags to denote high quality, and at times data quality scoring would be leveraged. 

Lately, the phrases data usefulness and data completeness have been factored into the data quality mix. A lot of people say that data completeness is datasets that possess none or a restricted number of absent values. This pinpoints back towards the activity of data cleaning. 

We need to strive for a deeper meaning when we define data completeness. Data completeness is datasets that have all the required information to efficiently delve into and resolve an analytical matter in-depth, with complete context, and from all perspectives. This definition hinges more on how data sets can enhance analytics precision and depth. In the present market economy, every enterprise is required to delve into each detail in their analytics to make accurate and correct decisions. 

Data completeness, in essence, is data done correctly.  

The first stage in forging improved data completeness is to stimulate collaboration between your data engineering team and the analytics society. This does not mean social visits, but instead enabling them to collaborate with each other in an interactive fashion on their data pipelines to make sure that analytics specifications are met. 

Three critical data pipeline platform capacities can bestow massive utility to both data completeness and usefulness; collaboration, reuse, and extensibility. Both by themselves and in conjunction, the functionalities assist DataOps processes enhance the production quickness, output volumes, breadth, and quality of analytics. 

Data Enrichment 

Data enrichment is of critical importance yet often glossed over facet of data pipeline design. It is typically glossed over as several data pipeline utilities provided restricted data enrichment capacities. Data enrichment features and functionalities are critical to obtaining a high degree of data completeness. Enrichment is additionally a sphere where collaboration and extensibility are factored in by enabling analysts and data scientists the capability to enhance data in a self-service fashion. 

Take the example of a customer is title insurance, mortgage-connected services, property, had highly complicated and disparate datasets consisting of data coming from services partnerships. The diversity inherent to the data needed a large input of coding to normalize, improve, and categorize data for analytics. The need of the hour for this client is to eradicate their reliance on time-consuming, manual SQL code, and leverage data science tools for data engineering teams to normalize and categorize data; on the other hand, researchers improve data to their particular analytics requirements. 

Data Aggregation and organization 

Making datasets ready for consumption and analytics-ready usually needs the data to be depicted in aggregated views or organized in some other fashion. This enables data to be more easily interpreted in their context and summarized. This is a sphere often glossed over as a majority of data pipeline utilities, as well as for analytics tools, only furnish simplistic means to aggregate information, forcing researchers to author complicated SQL.  

A leader in market research and client trends, as an organization, records massive volumes of client purchase and behavior information, organizes and runs it through analysis methods. It then provides data and analytics to their various consumer goods and retail customers. The analytics provided feature diversity with unique requirements. The organization leverages the diverse set functionalities provided by Data Science software using the windowing, sessionization, and grouping functionalities to categorize the data, then bucket and aggregate it leveraging complex dimensions for more detailed understanding. 

Data Science 

A majority of machine learning and AI frameworks need data to be encoded and input in very particular formats. A small amount of data pipeline tools provide particular functionalities to shape and organize your data especially for data science analytics. With no particular formulae for data science encoding, readying data for AI and ML can be very cumbersome and time-intensive. 

One of the biggest multinational pharma companies in Asia has large volume, complicated, and unique datasets that drive their data science projects and operational models. The data science undertakings need broad and in-depth datasets that have improved and encoded columns particular to the framework.  

Prior to leveraging Data Science software, the data was enriched, blended, and encoded by coding on their data science references – a time-intensive and error-fraught procedure with restricted reuse and opertionalization. With Data Science software, the organization now has the capability in organizing, enriching, and encoding the data within their data pipelines in a reduced time with no coding leveraging the rich set of functions in the software. 


These are just a few real-life examples of how various enterprises are leveraging Data Science in maximizing data completeness, making their datasets more viable to their analytics societies, and drive increasingly efficient decision making. 

Individually, the previously mentioned capacities make datasets more viable and complete to make data engineering quicker and simpler for particular use cases. As a suite of functionalities, a wider array of use cases can be encompassed leveraging a singular data pipeline platform – obtaining improved ROI from your data engineering efforts and enhancing the cumulative ROI from analytics efforts. 

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