That is a great list of actions. I would also add that data needs to be cleansed of errors before it is usable, by which I mean more than "transform" in ETL; and periodically refreshed from its dynamic source. Perhaps that is what you intended by "managed".

Thanks, Chris Kelly! Very true, and alongside there is receipt of unstructured data that has to be used for business intelligence and analytics applications in big data. This also, as you say, needs to be cleansed periodically and made usable.

Dear Suja Ravindran Nair, Thank you very much for asking this very important question.

Dear Suja Ravindran Nair, Thank you very much for making very important inquiries on the Research Gate portal and inspiring important discussions on particularly important issues.

In my opinion, it is good that there was a discussion in the question of this query.

This is a very interesting and scientifically important topic.

Because the topic is very important so I still read the answers, I follow the interesting discussion.

Question:

How Big Data from multiple sources can be linked, matched, transformed and managed across systems to become usable?

this is a very important issue that requires scientific research.

It seems to me that it is good that there was a discussion in the question of this question.

The answers given are very inspiring. Therefore, I am still reading the answers, following the interesting discussion.

I invite you to further considerations, to continue the interesting discussion.

I wish you all good and best regards

The Big Data database technology is finding new applications in business.

Multi-criteria processing of huge data sets collected in Big Data database systems allows preparing reports in a relatively short time according to given criteria.

The report development time depends mainly on the computing power of Big Data servers.

In processes of complex economic and financial analyzes, risk management, etc. for the purpose of determining the economic and financial situation of business entities, they are increasingly carried out in computerized analytical platforms of the Business Intelligence type.

Perhaps in the future, artificial intelligence will also be involved in this field of analytics.

In some countries, IT companies have been operating for several years, which develop Big Data database technology for commercial and business purposes.

Some technology companies produce Business Intelligence platforms for business entities.

Research centers of the largest technology companies on the Internet develop technologies of artificial intelligence.

It is only a matter of time to combine these various analytical and database technologies in computing cloud computing.

I invite you to the discussion.

The above discussion inspired me to the following considerations:

What will be the directions of development of analytical processes of sentiment analyzes on data collected in Big Data database systems in the future?

In order to analyze the sentiment on downloaded data from social media portals (such as Facebook, Tweeter, LinkedIn, but also YouTube, Instagram ...) and aggregated in Big Data database systems, it is necessary to use specialized software for extraction and analysis of these data.

The quality of the data transferred to, for example, excel sheets depends on the quality of the extraction process carried out with the help of specialized software.

Then, the quality of data analysis software in Excel sheets or in systems of computerized analytical platforms depends on the result obtained, the answer to the question given to the collected, initially unstructured data in the Big Data database system.

In the future, artificial intelligence may be used for this purpose, and the whole process of purposeful analysis of collected data will proceed in a much more effective, automated manner, less probable errors, will be a cheaper research process and will be carried out much faster even on much larger information collection than current.

In view of the above, the current question is: What will the directions of development of analytical processes carried out sentiment analyzes on data collected in Big Data database systems in the future?

Please, answer, comments. I invite you to the discussion.

In order to analyze the sentiment on downloaded data from social media portals (such as Facebook, Tweeter, LinkedIn, but also YouTube, Instagram ...) and aggregated in Big Data database systems, it is necessary to use specialized software for extraction and analysis of these data.

The quality of the data transferred to, for example, excel sheets depends on the quality of the extraction process carried out with the help of specialized software.

Then, the quality of data analysis software in Excel sheets or in systems of computerized analytical platforms depends on the result obtained, the answer to the question given to the collected, initially unstructured data in the Big Data database system.

In the future, artificial intelligence may be used for this purpose, and the whole process of purposeful analysis of collected data will proceed in a much more effective, automated manner, less probable errors, will be a cheaper research process and will be carried out much faster even on much larger information collection than current.

I invite you to the discussion.

In addition, I note the interesting discussion inspired me to the following considerations:

Are there any ongoing work to implement artificial intelligence for the analysis processes of large collections of information collected in Big Data database systems?

Business entity management processes are more and more often supported by computerized Business Intelligence platforms that facilitate multi-criteria analysis and reporting.

Probably in the future, the business analyst will be supported by artificial intelligence.

It would be a great advance in the field of automation and objectification of multi-criteria economic analyzes of business entities.

Complex, multi-criteria analyzes regarding the verification of large companies' operations require aggregation and analytical processing of large data sets in Big Data database systems.

However, in what direction will technological progress be realized in this field?

In the future, as part of the progressing computerization of analytical processes, it will be possible to implement artificial intelligence to the processes of analyzing large collections of information collected in Big Data database systems.

In view of the above, I am asking you: Are there any ongoing works to implement artificial intelligence to the processes of analyzing large collections of information collected in Big Data database systems?

Please, answer, comments. I invite you to the discussion.

Information Engineering can help on that!!

Following

Thanks a lot Dariusz Prokopowicz for taking the discussion on Big Data Management forward.

You have rightly voiced the concerns on data sourced from multiple sources including social media, the quality of data processed and suggested usage of artificial intelligence (AI) in analyzing big data.

Yes, ideally business analysts should ably be supported by AI. Given that using of AI calls for dependency on huge complex operating systems, security will have to be closely and constantly monitored. Additionally, operating big data management systems requires huge investments. So, a question which comes to my mind is what steps need to be taken so that small and medium organizations can also be involved and gain from big data management?

From a strategic management perspective, big data will enable organizations to obtain and analyse data faster but this will also enable access to data that could compromise(either inadvertently or deliberately) an individual's privacy. In either way, using of Big Data raises concern, and prompts one to consider the ethical issues regarding the sharing and usage of data. How to address the moral issue concerning Ethics and Big Data is to be thought of?

Efficiency in Data analytics is crucial for organizations indeed, for creating insights for the achievement of the business 'goal.

I think in order to achieve effective use of Big Data, we must consider the whole life cycle; a cross-sectional solution by the help of data scientist, db manager, engineer etc...

@ Mahamadou Kante , yes the efficiency and effectiveness of Big data management will be visible only if taken with a long term perspective.

This, as you have stated should involve cross sectional (and also cross functional) data scientists, to work in collaboration with all the stakeholders (Organization/ management, employees, customers, institutions, governing bodies, public, etc.)I would like to add, so that a platform is built on which all the stakeholders could be involved, informed and educated on the opportunities and challenges of big data management. For, collaborative partnerships usually help in taking positive strides

I agree with you @ Suja Ravindran Nair.

Collaboration is key, I will be glad to participate in such projects if you have something in mind.

Regards

Dear Mahamadou Kante,

I invite you to visit https://www.inderscience.com/jhome.php?jcode=ijbdm for details regarding our new journal titled "International Journal of Big Data Management" (IJBDM), to be published by Inderscience Publishers. The first issue is to be published in June 2019. You may consider submitting a paper or article for consideration for publication in IJBDM.

Best Wishes,