Big Data: It is very huge, quite large or abundant amount of data, information or the co-related statistics collected by the big organizations. Most of the software and data storage developed and prepared, as it is tough to evaluate the big data, manually. It is used to find out patterns and trends to make decisions concerning human, and interactive technology.
Applications of Big Data
1. Banking and Financial Services
All Credit card companies, retail banks, private wealth management services, insurance companies, and institutional investment houses use big data analysis for their financial services. The problem among them is that the massive amount of is multi-structured data stored in multiple systems, which big data can solve in quick time to make decisions. Big data is used in many ways, such as:
• Customer analytics
• Compliance analytics
• Fraud analytics
• Operational analytics
2. Big Data in telecommunications
Gaining new customers to subscribe, retaining the customers, and expanding within current customer base are top priorities for telephone communication companies. The solutions to these challenges is in the ability to collate and analyze the customer-generated data and/or machine-generated data that is being created day by day.
3. Big Data for Retail marketing
Whether the company is an online retailer or offline construction company, They all want to understand the demand of the customers and change in their needs. This need is to analyze all different data sources (data-mart) that companies deal day to day, including the customer transaction data, weblogs, social media, credit card data, and reward/coupon program data.
Bigdata challenges and solution
1. Lack of understanding of Big Data
Many organizations fail in their Big Data initiatives due to lack of understanding. Employees might not be knowing what data is, its storage methods, operations on data, importance, and data sources. Data professionals may know what needs to be done, but others may not have a clear view.
For example, if an employee do not understand the significance of data storage, he may not keep the backup of confidential or sensitive data. They might not use database systems properly for storage. As a result, when this data is required and needs to be accessed, it cannot be retrieved, easily.
Solution:
Big Data workshops and hands-on practice must be conducted for everyone. Basic training programs must be conducted for all the employees who are handling data, daily and as a part of the Big Data projects. A basic understanding of concept of Bigdata must be inculcated by all organization.
2. Data growth issues
One of the most complex challenge of Big Data is storing all these voluminous data, properly. The abundance of data being stored in data marts and databases of companies is growing, rapidly.
As these data grow rapidly with time, it will be difficult to handle in the future. The data is unstructured and comes from documents, audios, videos, text files and other sources. It means that you cannot search them in databases.
Solution:
In order to maintain these large data sets, companies are going for present techniques, such as compression, tiering (level-wise storage), and de-duplication. Compression is used for reducing the redundancies in the data, thus reducing its overall size upto some extent witout changing the meaning of data. De-duplication is the process of eradicating duplicate and unwanted data from a data. Data tiering allows companies to store the data in different storage tiers to ensure the data is residing in the most appropriate storage space. Data tiers can be private cloud, public cloud, and flash storage, depending on the data size and significance.
3. Confusion in selecting Bigdata tool
The companies sometimes get confused while selecting the best tool for Big Data analysis and storage. There are many questions arises like;
Is HBase or Cassandra the best technology for storage?
Is Hadoop or MapReduce good enough or Spark be a better choice for data analytics and storage?
Above questions bother companies and often they are unable to find the answers. They end up making poor decisions and select a technology which is not suitable. Therefore, money, time, and efforts are wasted.
Solution:
The best way to seek professional assistance. You can either hire experienced Bigdata professionals who knows much more about the tools. Another way is to go for Big Data consultancy for proper advice. Here, consultants will give some advice and recommend best tools, based on the company’s scenario. Based on their advice, you can make a strategy and then select the best tool for the betterment of the company.
4. Lack of data professionals
To utilize these novice technologies and Big Data tools, companies need to have skilled data professionals. These data professionals include data scientists, data analysts and data engineers who are experienced in working with the data handling tools and making sense out of voluminous data sets. Companies face lack of Big Data professionals in current scenario. This is because data handling tools have evolved, rapidly, but in many cases, the data professionals have not evolved as compared to.
Solution:
The companies are investing more and more money in hiring skilled professionals. They also have to offer free training programs to the existing staff to get the most out of them.
Another significant step taken by companies is to purchase the data analytics software/tools that are powered by artificial intelligence and /or machine learning. These tools can be used by professionals who are not data science experts but have preliminary knowledge.
5. Securing the data
Securing the huge data is one of the challenges task of Big Data. Often many big companies are also busy in collecting, understanding, storing, and analyzing the data that arises data security for later stages. But, this is not a good move as unprotected data repositories may become breeding grounds for hackers. Companies can lose the data with their revenue.
Solution:
Companies should recruit cyber-security professionals to protect the data. Other steps taken for securing data; such as:
• Data encryption
• Data segregation
• Identity and access control
• Implementation of endpoint security
• Real-time security monitoring
• Use Big Data security tools
6. Integrating data from a various sources
Data in company comes from a variety of sources or data marts, such as social media pages, ERP applications, MIS applications, customer logs, financial reports, e-mails, presentations and data reports created by employees. Combining all these types data to prepare a single reports is a challenging task. This is field often neglected by firms. But, data integration is important for analysis, reporting and business intelligence, so it has to be worked out.
Solution:
Companies have to resolve the data integration problems by buying the right data handling tools. Few of them are mentioned below:
• Talend Data Integration
• Centerprise Data Integrator
• ArcESB
• IBM InfoSphere
• Xplenty
• Informatica PowerCenter
• CloverDX
• Microsoft SQL
• QlikView
• Oracle Data Service Integrator
