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Big Data Curation? How is it Related to your Work Domain?

As technologies are in place in data management at organizational level and requirements for Data Curation Infrastructures are being defined by the emerging big data landscape, and curation infrastructures are evolving to meet all the challenges that are being faced in the processes and activities related to the organization and integration of data collected from various sources.

The current data environment (which is influenced by macro factor of business environment viz. Technology advancement) presents a growing array and volume of data. At the same time, a new mode of inquiry, problem solving skills, and decision-making is also becoming pervasive in our society. This new mode calls for the application of computational mathematical and statistical models to gather actionable information from large quantities of data or should say vital big data. This archetype, often called Big Data Analytics requires updated and advanced forms of data management to deal with the volume, velocity, variety and of Big Data. This new form of data management is called Data Curation.


Curation

Data curation: What is it?

Data curation encompasses all the processes needed for ethical and controlled data creation, maintenance, and management. Besides this, data curation also adds value to data. So data curation looks into:

  • Acquisition and care of data
  • Making decisions in regards what data to collect
  • Supervision data care and maintenance (metadata)
  • Conducting research based on the requisite data collected.
  • Ensuring accurate packaging of data for reuse
  • And sharing this data with the public
  • Ensure data should maintains its value over time

Curation

Data curation: what key insights?

The demand for data interoperability and reuse, and effective transparency by (in their large budgeted projects) eScience, IT, Automobile industry and eGovernment respectively, are driving data curation practices and technologies. These sectors are playing significant roles of innovators and visionaries in the data curation technology adoption lifecycle. Organizations in the biomedical domain, such as pharmaceutical companies, are one of the early adopters.
The main idea behind the data curation is to enable more complete and high-quality data-driven models for knowledge organizations. It provides complete models would support a larger number of answers. Data curation practices and technologies are facilitating organizations and individuals to re-use third party data in various contexts.

  • Acquisition and care of data with high accuracy
  • Making decisions regarding what data to be collected.
  • Forecasting or Overseeing data care and maintenance (metadata)
  • Conducting research based on the data collected in context to organization's needs.
  • Ensuring proper packaging of data for reuse
  • And sharing this data with the public
  • Ensure data maintains its value over time

Data curation: what key insights to draw?

The demand for data interoperability and re-use, and effective transparency by eScience and eGovernment respectively, are driving data curation practices and technologies. These sectors are playing the roles of innovators and visionaries in the data curation technology adoption lifecycle. Organizations in the biomedical space, such as pharmaceutical companies, are one of the early adopters. The main idea behind data curation is to enable more complete and high-quality data-driven models for knowledge organizations. Complete models would support a larger number of answers. Data curation practices and technologies are facilitating organizations and individuals to reuse third party data in different contexts. Emerging economic models, like the public-private partnerships, can support the creation of data curation infrastructures. Such an investment in the data curation infrastructure will lead to better quantification of the economic impact of high-quality data. In order to improve the scale of data curation, there needs to be a reduction in the cost per data curation task and an increment n the pool of data curators. For improving the automation of complex curation tasks, a hybrid human-algorithmic data curation approach and the ability to forecast and compute the uncertainty of the results of algorithmic approaches are fundamental. One more factor that plays an important role in scaling up data-curation is crowdsourcing, which permit access to a large pool of potential data curators. The approach of interaction between curators and data has a significant bearing on curation efficiency and reduces the barrier domain experts and casual users to curate data. Some of the key functionalities in human-data interaction include semantic search, natural language interfaces, data visualization and summarization, and intuitive data transformation interfaces. A standards-based data representation improves interoperability by way of decreasing syntactic and semantic heterogeneity. Such conceptual model standards and data model available in vary domains. With a tremendous growth in the number of data sources and decentralized content generation, a fundamental issue for data management environments is to ensure expected data quality. And data curation methods and tools do exactly that for you. Emerging economic models such as the public-private partnerships, can help in creation of data curation infrastructures. Such an investment in the data curation infrastructure will ensure quantification of the economic impact of high-quality data.
In order to get better scale of data curation, there needs to be a reduction in the cost per data curation task and an increment n the pool of data curators. For improving the automation of complex curation tasks, a hybrid human-algorithmic data curation approach and the ability to compute the uncertainty of the outcome of algorithmic approaches are fundamental. Another aspect that plays an important role in scaling up data-curation is crowdsourcing, which allows access to a large pool of potential data curators. The approach of interaction in between curators and data has a significant bearing on curation efficiency and reduces the barrier domain experts and casual users to curate data. Some of the key functionalities in human-data interaction comprise semantic search, natural language interfaces, data visualization and summarization, and intuitive data transformation interfaces. A standards-based data representation improves interoperability by way of reducing syntactic and semantic heterogeneity. Such conceptual model standards and data model available in various domains. With a growth in the number of data sources and decentralized content generation, a fundamental issue for data management environments is to ensure data quality. And data curation methods techniques and tools do exactly that for you and for your organization.


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