DEV Community: Henny Jones

Why Big Data Analytics Is In The Big Picture in Banking Market?

Henny Jones — Fri, 10 Jun 2022 06:18:31 +0000

Can you tell me what has been helping to change the banking market? It’s big data analytics. Experts are forecasting that between the period 2020 to 2027 the market will grow significantly. The growth is very high and substantial with the help of Big data analytics.

We are adopting technologies like the Internet of Things very fast and the need for real-time monitoring of data which is generated by the banks has made them launch something new and something enhanced.

Globally Big Data analytics have provided a drastic evaluation in the banking market. So what does this change offers to the banks and their consumers? Well, that’s the main question and the title of this article.

Using this technique, you can get an understanding of the actual segments, trends, drivers, restraints, competitive landscape, and other important factors for the banking sector.

So, now you know that Big Data Analytics is important and how it can be helpful. Let’s analyze these analytics with deeper analytics. But before that, we need to know what is big data anallytics?

What is Big Data Analytics?

Bank records are very confidential and transactions of millions and trillions occur on daily basis. With the rising population, the transactions are also rising. Using improved technology is essential for data banks to comply with the same privacy and security standards.

This is where Big Data Analytics comes into the picture. Well, some of the ground rules are now destroyed and banks have transformed the structure of financial services. With the help of the different types of models such as Data mining, artificial intelligence, and Big Data analysis banks are now coming up with innovative and less risky business ideas.

So, What Are The Scopes Of This Big Data Analytics?

Well, Big Data analytics is all about its insights, and with these insights, the banks will be able to understand the behavior, patterns, shopping trends, investment background as well as the personal and financial background of the customer.

Moreover, Big Data Analytics also helps banks understand the different trends of the market, allowing them to increase or decrease their interest rates accordingly.

Where has the data of Big Data analytics been stored?

Unlike, the paperwork, the Big Data Analytics can be stored in the electronic records of the respective banks. Well, this stored data can improve scalability, and also it can be very helpful to the environment.

Do you now understand the significance of Big Data Analytics in Banking and do you think that it is a real necessity? If banks are going to use Big Data Analytics effectively, they will have to be on top of the trends.

So, let me tell you some trends in the Banking market related to Big Data Analytics.

Surely none of us is unaware that banks operate within a highly regulated environment, so they have organized themselves well and have a good handle on monitoring all of this.

For information and to manage regulatory compliance, Credit Suisse uses big data analytics.

There are several uses of Big Data Analytics that could be helpful to the banks such as experience analysis, credit risk assessment, and customer segmentation.

Some other banks like BNY Mellon, Morgan Stanley, and Bank of America are working to build a strategy around Big Data in Banking. Not to mention that other banks are following them with great speed and great enthusiasm.

Now, let’s talk about the government. The banks and governments are both looking at Big Data Analytics in the big picture. Currently, the government is reforming its identity system to protect banks from fraudulent activities.

Danemark's largest bank, Danske Bank, implemented a modern enterprise analytics solution that reduced false positives by 60% and ultimately increased true positives by 50%. That was helpful for Danske bank.

According to the report of Commerzbank, there are some parts of Europe in which Big Data Analytics is not working properly.
Big Data analytics must require certain things, such as sustainable infrastructure, wide public cloud adoption, and deployment of 5G. Oooh! That is gonna be tough.
forecast of big data analytics in the banking market

Well, about the forecast, we sure can not see the future. But we can expect the number which can be true or around. So, Big Data Analytics is going to be in the big picture very soon and about numbers, it is gonna register a CAGR of 22.97% in just 6 years of the time period.
It’s been recorded in 2020 that the losses in the financial market were around 744 billion US dollars. The numbers will be changed soon.

Conclusion

In the end, Big Data Analytics is safer and less hectic than several banking frameworks of today’s world. But some things are needed and highly recommended for this Technology to get adopted. We will have to wait for some time and accept this. In nearer future Big Data analytics going to be a great part of our life and also it will make transactions smoother.

The Right Processes For Effective Data Migration Success

Henny Jones — Wed, 22 Dec 2021 10:27:55 +0000

Table of content
• Introduction
• Data migration
• The different types of data migration
• Strategies for data migration
• Best practices for effective data migration success
• Conclusion

Introduction
Data migration deals with the movement of data from one server to another. This data transfer involves a change in storage as well as database or application. The process is often complicated and it can take a long time to complete. It also requires a large budget, especially for companies depending on the volume. This is why data migration must be done in advance. Thus, it is essential to put in place the right methodology for it to be successful.

Data migration
With data in various formats coming from different environments, an upgrade of the existing system to a newer server may be necessary. This process should take place when replacing an obsolete storage system. It is also essential for upgrading a storage solution in the private cloud or when upgrading a database.

For all of these different situations, you have to move data from one system to another. In this case, new hardware is required to perform the data migration. For businesses, the migration solution data is a way to expand their storage capacity or better use them in a more modern technological environment. There are a number of factors to consider when planning and executing a data migration, including:
• Data integrity
• The impact on the activity
• The cost
• Evaluating the data
• The quality of the source data
• Potential unavailability

The different types of data migration
• Storage migration: This migration involves moving files from one device to another new or different. Data integration is done on premise or in the cloud. This is the most direct migration. However, for cloud data migration to be successful, it must be planned and executed with care.

• Database migration: this migration is necessary for upgrading a database engine. It is also essential for moving the installation or the database files to a new environment. Compared to storage migration, database migration requires important steps such as backing up databases, migrating files and updating the database engine as well as restoring data migrated to the database.

• Application migration: this is often a combination of the two previous types of migration. In this case, it is possible that the application requires a database migration and to migrate the installation folders.

Strategies for data migration
Data migration strategies can be defined in different ways. In fact, they must adapt to the specific requirements and needs of every company. However, the data migration strategy is often either the “Big Bang” or the “Trickle”.

• “Big bang” migration: for this type of migration, the entire transfer will take place over a limited period. The advantage of “big bang” migration is that the migration process is relatively fast. On the other hand, data migration must be scheduled at a time that is the least blocking for the company due to the shutdown of a software resource. While this approach often appears to be the most efficient and appropriate for a business, it is essential to test the migration before launching it into production.

• “Trickle” migration: unlike other migrations, “Trickle” migration is done in phases. In the process, the new system and the old one are executed in parallel. In this case, downtime as well as operational interruption can be avoided. As the process runs in real time, it is possible to migrate data continuously. This migration method is a bit complex. However, if properly managed, this solution guarantees the quality of the migrated data. Performing a “trickle” migration reduces risk rather than increasing it.

Best practices for effective data migration success
Regardless of the data migration tools you choose, it is essential to adhere to the following practices for a successful data migration project.

• Analyse the complexity of data in its original environment: In a data migration project, unforeseen events can always arise. It is for this reason that analysing the complexity of data is important. Indeed, the difference between failure and success will depend on the approach chosen in the event of a problem. Thus, it is first necessary to evaluate the different forms of data to be migrated.

• Back up before transferring data: Sometimes problems arise during the transfer process. However, you cannot afford to lose data if there is an error during implementation. Therefore, it is essential to verify that a backup exists before starting the migration.

• Respect the strategy: during a migration project, it often happens that the manager establishes a plan and then abandons this plan in place when he notices a failure in a phase. As a result, data migration is often complicated and sometimes becomes impractical. This is why you have to prepare a well-established migration plan and always be ready to face reality whatever the outcome.

• Test: It is always essential to test the data migration to ensure that the results are suitable for the business plan. This test can be done during the planning or design phase, but it is essential that it be done before the implementation phase.

Conclusion
Data migration remains a technical operation and as such, the best advice we can give you is to seek advice from an expert who practices this type of operation on a regular basis. It is essential to have expert view when migrating from one server to another. You can meet with us at HData Systems.

Predictive Analytics: How to Make Digital Price Predictions

Henny Jones — Tue, 17 Aug 2021 10:35:36 +0000

Everything has its price. The only question is when and how high it will be. Because ordering at the right time saves costs. With predictive analytics, the most favorable moment can be predicted. Here you can find out how the method works and what advantages it offers shoppers.

Predictive analytics is a mathematical principle that uses algorithms and artificial intelligence (AI) to derive probabilities from historical and current data. In this way, patterns, relationships and trends can generally be discovered.

Originally coming from the field of statistics, companies now use the process for various types of forecasts, including price predictions.

This is how predictive analytics works
The method uses structured and unstructured data from internal and external IT systems (big data / data mining). Predictive analytics collects this information using text mining, among other things, and combines it with elements of game theory and simulation processes.

Thanks to machine learning, the algorithms independently draw findings from their own data processing and use this as the basis for automatic predictions.

The underlying software has become more accessible and user-friendly over time thanks to user interfaces that are suitable for specific departments. This and its increasing accuracy make the method interesting for shopping.

Benefits of predictive analytics
Demand and price forecasts are among the most important elements of strategically designed procurement processes because they have a strong influence on decisions.

That is why predictive analytics are used for supplier management, controlling, product group management, sales and expenditure management. These and other areas can use the procedure for the following purposes:
• Payment analysis
• Billing analysis
• Procurement
• Risk assessment
• Service control
• Compliance rule monitoring

Predictive analytics is particularly interesting for price predictions and - closely related to this - sales volume calculation. Different suppliers, production processes, transport routes as well as political circumstances and legal requirements that differ from country to country make manually calculated forecasts difficult.

Software for predictive analytics has advantages here because it is significantly faster and more precise. A typical predictive analytics software offers:
• Control of prices in real time
• Development of scenarios
• Long-term price predictions for new products

In practice, this means that companies with predictive analytics keep an eye on the market and the competition and, thanks to the software, can assess future demand and price developments.

This makes it possible to order your own needs at the most favorable time. You can contact us at HData system for excellent predictive analytics solution.

Predictive Analytics in Practice
• The capabilities of a predictive analytics software make it essential for a whole range of application areas. Therefore, numerous industries use the method for different purposes. Some of which include:
• Financial services: Financial institutions use machine learning techniques and quantitative tools to predict credit risk.

• Automotive industry: Companies that develop autonomous vehicles analyze sensor data from networked automobiles and thus improve driver assistance algorithms.

• Medical technology: An asthma management device records the breathing sounds of patients, analyzes them and offers immediate feedback via a smartphone app to make it easier for those affected to cope with asthma and the lung disease.

• Aerospace: To improve aircraft uptime and reduce maintenance costs, an engine manufacturer created a real-time analytics application that predicts the performance of the oil, fuel, aircraft takeoff, mechanical condition, and control subsystems.

• Automation and mechanical engineering: A plastics and film manufacturer saves thousands of dollars a month with an application for condition monitoring and predictive maintenance that reduces downtimes and minimizes waste.

• Energy supply: Advanced forecasting apps use models that monitor the available capacity of power plants, the weather and seasonal consumption.

Applications of Predictive Analytics
1) Predictive maintenance
Predictive analytics is also used in mechanical engineering and automation processes - this is called predictive maintenance. Predictive maintenance is based on maintenance-relevant data, i.e. sensor data that is generated during the production of goods.
These collected sensor data help to correctly determine the maintenance status of a machine or system. For this purpose, predictive maintenance is used to calculate a forecast for the future and thus to make maintenance plannable. Repair measures can thus be arranged in good time.

The methods are basically the same, but the application is different. Machine breakdowns and downtimes lead to high costs, as they cause entire productions to fail. Thanks to predictive maintenance, however, it is possible to analyze and predict machine failures.

2) Fraud detection
The investigation of fraud cases often requires the analysis of very large amounts of data. But it can also be automated! With the help of predictive analytics methods, criminal behavior can be detected in data. Consequently, these appear as an anomaly in the data patterns.

In this way, the company optimizes its own security and increases the trust of its customers in the company. As a rule, this is used in the insurance and financial sectors to uncover misuse of assets, corruption or bribery and also falsification of financial data.

3) Medicine
What is the best therapy? Statistical methods and machine learning can also be used in medicine to derive additional knowledge and identify patterns. Similar to predictive maintenance, sensor data, images or structured data are also used in medicine to understand relationships or to make predictions with predictive analytics.

Another area of application in medicine is personalized medicine. With the help of studies and a large amount of data from other patients, decisions can also be made regarding therapies and treatments for individual patients.

The treatment of diseases is often challenging and each patient shows an individual behavior pattern to certain therapies. In this respect, an algorithm can also be a decision-making aid in medicine and ensure that the optimal therapy is found.

4) Climatology and meteorology
Know what the weather will be like tomorrow. That would be nice, but as it is with the weather, it usually turns out differently than you think. But in the meantime, the predictions have improved because the models can be simulated better and more precisely.

Statistical processes and machine learning techniques help to make forecasts about the weather even more precise.

Similar to the weather, certain currents and dynamics can be calculated in the ocean.

In order to know where a particle will be in the ocean tomorrow, the influences must first be understood and analyzed in order to then create forecasts using predictive analytics.