Reasons for Best Career Move in Big Data

This is a guest post by Madan Upadhyay.

Introduction

It is tough to neglect Big Data in today’s world. It is highly valued and the results from it. News media admires the evidence obtained from Big Data in economics as well. Industries are highly pleased with the commitments of Big Data. As a result of success government agencies have announced programmes of addressing the ability and strength of Big Data. But Perceptions of Big Data is not clear to many of us. At every stage, a lot of work and many competitions have to face with Big Data. Very Initial step is data acquisition. Data source produces incomplete or raw data which has to be filtered to get the appropriate results. Challenge is to select the right information in such a manner that it does not neglect the important information. The secondary challenge is to determine the sources from which the data is generated.  It is very useful in analysing further facts. Data analysis is more complicating than collecting. We considered Big Data to be always true but vit’s not the case. A number of data have to filter for extracting out the real report. There is a lot of work to do for the research community to work.

Panel Goals

According to ( Labrinidis & Jagadish. 2012)  “Data analysis is considerably more challenging than simply locating, identifying, understanding, and citing data”. Experts have many tasks to resolve. The very first task is to find out why this Big Data is different from enormous database techniques already present before and the complicating facts which are still to resolve. Secondly, to find out how can the data management team and scholars work together in breaking the critical information of Big Data. Ultimately it is the data management experts playing the important role. To perform this task the experts discusses the originality of the given evidence.

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  • Big Data is similar with resulted analytics.
  • Complication of Big Data is originated from application.
  • Big Data can be analysed on cloud-based platform.
  • Data management expert is at risk of losing the facts.
  • Impossible to conduct research on facts without including the people from outside the Data Management panel.
  • Big Data when reduced to a map, complication automatic get small.
  • The number of Big Data difficulty has to be mentioned by the affected industry.
  • Big Data problem is due to implementation.
  • The bulk of Big Data is highly taken into account.

Biocuration

The increase in the collection of Big Data requires a dramatic change in data management, analysing and access. Online databases have become an important source of the issue in Biological field. But biocuration failed in generating finances, expansion, and identification.

There is a need for prompt action in this medical field. Primarily Writers, professionals, and custodians immediately work together to enhance the exchange of information among publishing editors and keepers of the Big Data.

Secondary keepers, professors, and researchers should work in the direction of keeping the records on community wise. Lastly within the coming decade data keepers, scholars and scholarly institutions and funding bodies increase the exposure and scientifically securing information to be used by upcoming professionals. If these formal requirements will not be completed than sooner or later this out data will be of no use for researchers in experiments and in a proposed investigation.

When all the updated data will be secured with a high level of precautions and will be easily available for use results of the biological researches will be conducted in a totally different way. Experiments will be conducted with more input of knowledge all these processing require more experts in a panel to carry out the activities successfully.

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Come together

As according to (Howe, Costanzo, Fey, Gojobori, Hannick, Hide, … & Twigger  2008) “how information is presented in the literature greatly affects how fast biocurators can identify and curate it”. Taking out, fixing with meaningful words and presenting data out of the literature are few of the essential and time taking activities in biocuration. Online content information from the written work is considered as a gold-standard data. For analysing, high-quality maintenance and setting the highest level of standard of data exploration. Bio-curators go through the task of reading the article and transfer the useful information into databases. For example in the study of the molecular structure of a particular gene, information relating will include gene- expression pattern, mutant characteristics, complex behaviour of protein and author’s observation about the functions. As the report need different information and observing methods from other studies, collecting information in regular style need focussed thought and efforts. Limited people in the panel will not able to maintain all related information.

Recent effort has been conducted for communities that are developing bulk genomics data. This large amount of peer-viewed literature does not have a standard level of reporting. As all publication has been digitalised therefore records of publications and biological databases are getting similar records. Records are carefully referenced and indexed. Both of them are providing useful information to others. Such a manner of working together with data curators and publications will improve the results of researches and focus more light on the results. This will only be possible if a large number of experts in the panel will be appointed to manage and disintegrate the data present in bulk.

Conclusion

Biocuration will be adopted much faster as a career. Biocurator presently alien the data submission, automate the data-keeping and standardize the information and fasten the process of collection of information. To handle the bulk form of information or variety of data general publishers and researchers need to participate. Experienced experts in Big Data technology should come forth to establish more and better training programmes. In the next 5-10 years, database technology should include more courses as this is becoming a common activity for all database researchers. Attracting qualifies individuals into the database field will be challenging.

References

Labrinidis, A., & Jagadish, H. V. (2012).  Challenge and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032-2033.

Howe, D., Costanzo, M., Fey, P., Gojobori, T., Hannick, L., Hide, W., … & Twigger, S. (2008). Big data: The future of biocuration. Nature,  455 (7209), 47.


Founder – Kausal Vikash CEO at EmergenTeck | Intelligent Automation (RPA+AI) | Digital Transformation | Design Thinking, EmergenTeck.

With over 15+ years of experience in leading initiative related to RPA, Intelligent Automation, Data Science, Digital Transformation, Blockchain, which include Pre-sales, Capability building, Strategy, Product development and Innovation.

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