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6 of the Best Big Data Analytics Tools and Software for 2023 | blog.damrilogistics.co.id site review
Big Data Tools and Software – In today’s business landscape, data is the key ingredient for long-term growth. However, specialized tools and software are required to translate data into actionable insights. Without it, the data is effectively worthless.
A recent report from Sigma Computing found that 63% of enterprise employees cannot gather insights from their data in the required time frame, meaning that data is more of a productivity damper than a productivity booster. The right tool will provide valuable information and meet your business needs without being too expensive.
What is big data? | blog.damrilogistics.co.id site review
Big data describes information that companies use to automate processes, uncover high-level patterns, and innovate products or services. It contrasts with traditional datasets, which are usually more homogenous, simpler, and less valuable.
At its core, big data consists of three concepts known as the Three V’s:
- Size : The collective amount of data from a wide variety of sources
- Diversity : How the data is formatted (structured vs. unstructured)
- Speed : The rate at which data is received and processed
Big data requires more advanced software and more computing power for processing than traditional data sets, so a company looking to capitalize on big data must be prepared to make significant investments in technology and the IT staff to manage it. However, big data is also more powerful and accurate, so it can provide more business value and opportunities in the long run.
What is big data analytics?
Big data analytics describes techniques and technologies used to analyze very large and diverse data sets. Organizations can use big data analytics tools to process structured, unstructured, or semi-structured data from multiple sources. Without these tools, it would be impossible to manage big data.
In addition to processing datasets, big data analytics methodologies and tools are involved in data visualization, business forecasting, and data-driven decision-making. Unlike traditional data technologies, these tools take raw data a step further by giving it context and meaning. Rather than just a repository of individual records, big data analytics tools help organizations look at the bigger picture that data creates.
What are the best big data tools?
There are a variety of big data tools that help organizations with analytics. Some are end-to-end solutions, while others focus more on a specific area such as data visualization or data integration.
1. Big data tools dashboard
Tableau is leading data visualization software used by data analysts and business intelligence teams to create attractive graphical representations of their data. It connects to data from a wide range of sources quickly and efficiently and has one of the most advanced feature sets on the market.
Although Tableau says its interface is designed to meet any user’s skill level, many customers have reported that casual users need more practice to get the most out of the platform. However, seasoned data analysts should have no problem navigating Tableau to set it up and start digging into the data.
2. Apache Hadoop
Apache Hadoop is an open source data analysis software framework available for download since 2006, and it is one of the most popular tools among data analysts. Hadoop’s storage component is the Hadoop Distributed File System (HDFS), which “provides high-throughput access to application data,” and its processing component is Hadoop MapReduce, “a YARN-based system for parallel processing of large data sets.”
Hadoop was designed with the basic understanding that hardware failures are inevitable, so the framework must be ready to detect and handle these problems at the application layer. Although Hadoop provides high availability and exceptional parallel processing capabilities, it does not support real-time processing or in-memory arithmetic operations – both of which are essential for efficient data analysis.
3. Apache Spark
Apache Spark is another open source utility that works similarly to Hadoop with one major difference: instead of a file system, Spark caches and processes data using the device’s underlying RAM. This means that Spark can bridge the gaps in real-time processing and in-memory computation that Hadoop cannot handle, making the Spark ecosystem more effective and efficient.
In fact, Spark’s data processing capabilities for small workloads are 100 times faster than MapReduce Hadoop. In addition, Spark is able to work with a wide variety of data stores and HDFS, making Spark a more versatile and flexible solution. However, Hadoop is still a more cost-effective option because it does not require large amounts of RAM.
4. Zoho Analytics
For small organizations, Zoho Analytics is an affordable and accessible solution for big data analytics. It has an intuitive user interface that makes it easy to create rich dashboards and find the most important information quickly.
While it’s a powerful, standalone product, one advantage of using Zoho Analytics is that it can be directly integrated with Zoho’s broader range of business tools, including CRM, HR, and marketing automation applications. What Zoho Analytics lacks in advanced features, it makes up for in ease of use and price.
5. MongoDB
MongoDB is a NoSQL database that uses document-based collections instead of SQL-based rows and columns. It was created by developers, so it makes app development a much faster and user-friendly process.
It’s also an ideal choice for data-driven organizations that are embarking on their digital transformation journey or those that want to start small and scale as the business grows. MongoDB’s processing speeds have been a drag for some customers, so this potential limitation is something large companies need to consider.
6. Explanation
Xplenty is a cloud-based data integration platform that helps streamline data from a variety of structured, unstructured, and semi-structured sources. It’s a low-code ETL platform, which means it cleans, enriches, and transforms each dataset before it’s sent to the data warehouse—all with minimal code needed to complete the process. It is often used in combination with other tools such as Tableau.
Xplenty integrates with a large number of applications, including Zendesk, Oracle, and Salesforce. Once connected, Xplenty automates the data integration process from all the tools you use to run your business and creates a single source of truth for all your data-driven insights.
How to choose the right big data tool | blog.damrilogistics.co.id site review
The right big data tool for your business will match your unique needs. Consider the applications you use, the types of data your business needs to manage, and the information you need to understand from your data when it comes time to make decisions. Next, find a tool (or combination of tools) that will help you with your analytics needs without breaking the bank.
Big Data in Finance – Overview, Applications, Challenges
Big Data in Finance – Overview, Applications, Challenges – The large, diverse and complex collections of data that can be used to provide solutions to long-standing financial services business challenges.
What is big data in finance?
Big data in finance refers to large, diverse (both structured and unstructured), and complex sets of data that can be used to provide solutions to long-term business challenges for financial services and banking companies around the world. The term is no longer restricted to just the field of technology but is now considered a business necessity. Leveraging is increasingly being done by financial services firms to transform their operations, organizations and the entire industry. blog.damrilogistics.co.id site review
summary
- Big data in finance refers to large, diverse (structured and unstructured) and complex data sets that can be used to provide solutions to long-term business challenges.
- Big data is revolutionizing how stock markets operate around the world and how investors make their investment decisions.
- However, the inability to correlate data across organizational and management silos has become a major challenge for business intelligence, particularly in banks where mergers and acquisitions create countless and costly datasets.
How big data is revolutionizing finance
The exponential growth of technology and increased data generation are fundamentally changing the way individual industries and companies operate. The financial services sector is, by its very nature, one of the most data-intensive sectors, and presents a unique opportunity to manipulate, analyze, and leverage data in useful ways.
Traditionally, the crunching of numbers has been done by humans, and decisions are made based on inferences drawn from calculated risks and trends. Recently, however, computers have usurped this functionality. As a result, the big data technology market in finance offers huge potential and is one of the most promising markets. blog.damrilogistics.co.id site review
1. Real-time stock market insights
Big data is revolutionizing how stock markets operate around the world and how investors make their investment decisions. Machine learning — the practice of using computer algorithms to find patterns in massive amounts of data — enables computers to make accurate predictions and human-like decisions when feeding data and executing trades at high speeds and frequencies.
The business archetype monitors stock trends in real time. It includes the best possible prices. Allowing analysts to make smart decisions and reduce manual errors due to behavioral influences and biases. In conjunction with big data, algorithmic trading thus results in highly optimized insights for traders to maximize their portfolio returns.
2. Big data analytics in financial models
Big data analytics offers a great opportunity to improve predictive modeling. To better estimate rates of return and outcomes on investments. Access to big data and improved computational understanding lead to more accurate forecasts and the ability to effectively mitigate risks inherent in financial trading. blog.damrilogistics.co.id site review
3. Customer analytics
Today, customers are at the heart of the business where data, operations, technology. And systems insights revolve. Hence, the ongoing big data initiatives by banking and financial market firms focus on customer analytics to better serve customers.
Companies try to understand customers’ needs and preferences to anticipate future behaviors. Generating sales leads, take advantage of new channels and technologies, improve their products. And improve customer satisfaction.
Thus, by effectively establishing meaningful relationships with their customers. And improving their ability to anticipate customer preferences. And financial market institutions can introduce new customer-centric products and services to quickly seize market opportunities.
For example, the Overseas Banking Corporation of China (OCBC) analyzed vast amounts. The historical customer data to determine individual customer preferences to design an event-based marketing strategy. The strategy focused on a high volume of coordinated and customized marketing communications across multiple channels, including email, text messages, ATMs, call centers, etc.
4. Risk management and fraud detection
Financial institutions use big data to mitigate operational risk and combat fraud while significantly mitigating information asymmetry issues and achieving regulatory and compliance goals.
Banks have access to real-time data, which can be useful in identifying fraudulent activities. For example, if two transactions are made through the same credit card within a short time gap in different cities, the bank can immediately notify the cardholder of security threats and even block such transactions.
Additionally, in the case of insurance, the insurance company can access data from social media, past claims, criminal records, phone conversations, etc., beyond claim details while processing the claim. If she finds anything suspicious, she can report the claim for further investigation.
To effectively handle fraud, Alibaba has established a fraud risk monitoring and management system based on real-time processing of big data. Identifies bad transactions and picks up signs of fraud by analyzing massive amounts of user behavior data in real time using machine learning. blog.damrilogistics.co.id site review
Big data challenges facing the banking and financial industry
1. Meet regulatory compliance
Financial institutions must meet the strict regulatory requirements of the Basic Review of the Trading Book (FRTB) – set by the Basel Committee on Banking Supervision (BCBS) – which govern access to critical data and require prompt reporting.
2. Data privacy
Data privacy is another major concern associated with the implementation of cloud computing technologies. Companies are concerned about putting proprietary information in the cloud, and although some have built private cloud networks, such projects can be costly. blog.damrilogistics.co.id site review
3. Data silos
The inability to correlate data across departments and organizational repositories is a major challenge in the field of business intelligence, which leads to complex analytics and gets in the way of big data initiatives.
Additional sources
CFI is the official provider of the Business Intelligence & Data Analyst (BIDA) certification program, designed to transform anyone into a world-class financial analyst.
To continue learning and developing your knowledge of financial analysis, we highly recommend the additional resources below:
- Basel Accords
- Python (in machine learning)
- Fraud triangle
- Moving from Excel to Python
- See all data science resources
