Hi,

One of the biggest Big Data challenges is to store and analyze all the information. Other hand, the wide variety of data makes the integration of one of the major Big Data challenges. Data validation also.

Big data is more a market than a technology. It is determined by the demand and supply.

Management,storage and utilisation of cloud based technologies.

The credibility of data via strong data validation approaches is crucial. Coupled to this is the challenges with the processes of synthesis of the data, and more importantly, the storage and reusability of the data!

One unforeseen challenge is data protection laws.

For years, business models have relied on aggregating numerous amounts of data generated by users and the general public.

But presently, data protection regulations are going influence the means of gathering such data for analysis. Hence, presenting a challenge.

It is determined by many factors, this is a fact. Yet, in each field of study (healthcare, education, cybercrime...) myriad steps are needed as a basis before getting to analytics. The management, storage and processing techniques are mendatory so as to build a consistent framework, which can help you by then process analytics easily. In other words, security, transfer and communication issues should be handled earlier while conducting the study about the storage and processing models.

When dealing with these issues at a very late stage, it becomes very complicated and the challenges rise more and more.

Thank you very much for your participation in the discussion. Your remarks, conclusions, theses, research results, scientific considerations are very interesting and enrich the discussions in this topic. Your conclusions are very inspiring to continue the discussion, because you pay attention to the key and particularly important issues of this complex issue. I invite you to continue the discussion People interested in this subject.

Once of the Biggest challenge would be Data Governance.

The issues of the use of information contained in Big Data database systems for the purposes of conducting Business Intelligence analyzes are described in the publications:

https://www.researchgate.net/publication/323244120_The_Big_Data_technologies_as_an_important_factor_of_electronic_data_processing_and_the_development_of_computerized_analytical_platforms_Business_Intelligence

https://www.researchgate.net/publication/327035102_The_Technological_Solutions_Big_Data_and_the_Importance_of_Business_Analysis_According_to_the_Business_Intelligence_Formula

I invite you to discussion and cooperation. Greetings

Thank you very much for your answers, for comments and interesting scientific information.

The discussed issue in the above discussion is particularly important, so I invite you to continue the discussion on this topic.

What are the main determinants of the development of Big Data database technologies?

Please reply. I invite you to the discussion.

Will the development of Business Intelligence analytics and data processing technology collected in the Big Data banking database systems improve the credit risk management process or will it contribute to the development of Shadow Banking and the use of unethical practices for the surveillance of potential borrowers?

Large commercial banks generate high financial surpluses allowing for the implementation of modern integrated teleinformatic internet banking systems, Business Intelligence data analysis systems, data processing platforms in Big Data database systems, etc.

There were already situations of unethical use of modern ICT solutions, analysis of comments on social media portals, during which the bank verified the customer's data entered into the loan application by also scanning information that the potential borrower types in social media portals.

This informal verification took place without the knowledge of a potential borrower and could then be the basis for suing the bank.

However, the bank's client is not always aware of the fact that it can be invigilated in such a way by the public trust institutions that the bank should be.

Of course, these types of cases, which we know from the media is supposedly a margin of entire banking, which can be one of the categories of a new type of unethical practices typical of the so-called Shadow Banking.

However, only part of this type of information gets to the media.

Maybe this is just so-called "the tip of the iceberg" of this problem.

The situation is similar in the situation of cybercriminals' attack on bank IT systems or electronic banking platforms.

If it is possible to keep this type of events secret, then customers do not find out about it.

This is because media only receive information about some of these types of events.

Does any of you conduct research in this area?

If so, I invite you to cooperation.

I am asking for comments

In view of the above, I turn to you with the following question

Will the development of Business Intelligence analytics and data processing technology collected in the Big Data banking database systems improve the credit risk management process or will it contribute to the development of Shadow Banking and the use of unethical practices for the surveillance of potential borrowers?

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

When will artificial intelligence be implemented into the analytical Business Intelligence systems and what will be the consequences?

The Business Intelligence systems currently being developed by IT technology companies and sold for companies support business management processes or a specific part of it. These systems, if they are connected to the Big Data database system and integrated with logistics, accounting, production, sales departments, inventories, relationships, contractors, financial institutions, etc., can significantly improve business management systems. If the whole system of obtaining and processing collected in Big Data information concerns all external and internal determinations of the company's operations and takes place in real time, the company has an excellent tool supporting decision-making processes. This type of tool allows you to make better decisions in the strategic and ongoing management of a company, corporation, financial institution or other type of business entity. However, in this area there is constant progress determined primarily by the creation of new ICT solutions, new data processing technologies, etc. Therefore, the following question arises: When will artificial intelligence be implemented into the analytical Business Intelligence systems and what are the consequences?

Will business management become even easier after implementing artificial intelligence in Business Intelligence business intelligence systems? And if so, for what kind of business entities? or only for large corporations and financial institutions, in which millions of different categories of information are being processed on a daily basis, or such new-generation systems supporting decision making, will also be available for smaller companies and enterprises from the SME sector?

I am asking for answers, comments. I invite you to the discussion.

Will the information collected and processed in the Big Data database systems in future be able to accurately check future climate disasters?

Information collected and processed in Big Data database systems from climate change

In my opinion, information obtained from various research centers, meteorological centers, satellites, etc., then collected and processed in Big Data database systems should help in more and more precise prediction of new, unfavorable weather phenomena, including climatic cataclysms and others. In this way, earlier and in a more planned way, crisis management systems can be organized in the situation when the predetermined flood disaster becomes real and will happen. Gradually increasing computing power of powerful computer servers managing platforms of Big Data database systems, implemented artificial intelligence, increasing number of verified historical data on the overall climate phenomena on Earth will allow in the future more precisely, more accurately determine the level of threats, risk value, predict time, place and scale adverse weather events, i.e. also cataclysms that threaten people's lives.

Therefore, the question is still valid:

Will the information collected and processed in the Big Data database systems in future be able to accurately check future climate disasters?

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

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 avoid this issue and garantee the performance of the application we can use Oracle DB