Top Big Data Challenges With Solutions : A Complete Guide with Best Practices

• In this article you will learn:
• 1.What is Big Data?
• 2.Where Big Data Comes From?
• 3.Big Data and Its Three V’s.
• 4.The Origins of Big Data.
• 5.The Benefits of Using Big Data.
• 6.Problems Associated with Storing Big Amounts of Data.
• 7.Assessing and Choosing Appropriate Technologies for Big Data.

What is Big Data?

The term “Big Data” refers to collections of data that are very extensive. We often interact with data that is either megabytes (MB) or gigabytes (GB) in size (movies, codes), but the term “big data” refers to data that is petabytes, or 1015 bytes, in size. According to some estimates, over 90 percent of the data used today was created during the last three years.

Where Big Data Comes From?

These statistics originate from a wide variety of sources, including as Social networking sites such as Facebook, Google, and LinkedIn create a massive quantity of data on a daily basis due to the fact that they have billions of members all over the globe.

E-commerce website: Websites such as Amazon, Flipkart, and Alibaba create a large number of logs, which may be used to determine the purchasing patterns of its customers.

Weather Station: All of the weather stations and satellites offer extremely large amounts of data, which are then saved and modified in order to make weather predictions.

Telecom company: Telecom giants like Airtel and Vodafone examine the tendencies of its users and publish their services in accordance with those findings; in order to do this, they save the personal information of their millions of customers.The everyday transactions that take place on stock exchanges throughout the globe produce a massive quantity of data.

Big Data and Its Three V’s:

• The amount of data being collected is rapidly expanding at an astounding pace. It is anticipated that the quantity of data would increase by a factor of two every two years.
• Data are no longer kept in rows and columns as was common practise in the past. There are both organised and unstructured forms of data. Unstructured data includes things like log files and CCTV recordings. Structured data, such as the bank’s transaction data, are examples of the types of data that may be recorded in tables.
• The volume of the data that we work with is measured in peta bytes, which is a very huge number.

The Origins of Big Data:

Data from the Black Box:

• This is the data that was produced by various types of aircraft, such as jets and helicopters. Data from the aircraft’s black box contains cockpit conversations, microphone recordings, and information about the aircraft’s performance.

Social Media Data:

• This information was compiled by many social networking websites, including Twitter, Facebook, Instagram, Pinterest, and Google+.
• Data from Stock Exchanges The information presented here comes from stock exchanges and focuses on the purchasing and selling choices made by clients.
• Data Obtained from Power Grids These figures were obtained from the power grids. It stores information about specific nodes, such as use information, among other things.
• Data Relating to Transport This data contains the maximum capacity that may be accommodated, the type of the vehicle, its availability, and the total distance travelled by a vehicle.

Search Engine Data:

• This is one of the most important sources from which large data may be obtained. The information that search engines use comes from very large databases.

The Benefits of Using Big Data:

• The modern customer has extremely high expectations. Before making a purchase, he communicates with previous clients through social media, checks into other alternatives, and so on. When a person makes a purchase, they anticipate being appreciated and treated like an individual in return for their business. Big data will provide you with actionable data, which you will be able to put to use in order to interact with individual clients in real time. Big data makes it possible for you to accomplish this goal in a number of ways, one of which is the ability to check the profile of a dissatisfied client in real time and get information on the product or products about which the consumer is talking. Once this is accomplished, you will be able to control your reputation.
• You are able to redevelop the items and services that you are marketing thanks to big data. You will find that having information on what other people think about your goods, such as via the unstructured material on social networking sites, is helpful when developing new items.
• The availability of large amounts of data enables the testing of several iterations of CAD (computer-aided design) pictures to identify the impact that even minute modifications have on a process or product. Because of this, large amounts of data are very important in the production process.
• You will always be one step ahead of your rivals if you use predictive analysis. This may be made easier with big data by, for instance, collecting and analysing feeds from social media platforms as well as stories from newspapers. You may also conduct health checks on your customers, suppliers, and other stakeholders with the use of big data, which can assist you in lowering risks such as default.

• Big data is useful in keeping data secure. You may map the data landscape of your firm with the aid of big data technologies, which is beneficial when conducting an analysis of internal dangers. As an example, you will be aware of whether or not the sensitive information you have is protected. A more concrete illustration of this would be the fact that you would be able to flag the storing or sending of 16-digit numbers (which could, potentially, be credit card numbers).
• The use of big data enables you to generate many sources of money. The examination of large amounts of data may provide trend data, which, when combined with creative thinking, can lead to the development of an entirely new source of income.
• If you want your website to be successful in the cutthroat environment that is the modern internet, it has to be dynamic. The analysis of large amounts of data enables you to customise the appearance, content, and overall experience of your website to cater to each individual visitor depending on factors such as their country and gender, for example. Item-based collaborative filtering, or IBCF for short, is used by Amazon to power its “People you may know” and “Frequently purchased together” services. This is an example of this kind of filtering.
• Big data is essential for those who manage factories because it eliminates the need to replace components of machinery depending on the number of weeks or years they have been put into service. This is not only expensive but also unworkable due to the fact that various components wear at different rates. The use of big data makes it possible to identify equipment that are malfunctioning and will anticipate when they should be replaced.
• Big data is becoming more significant in the healthcare business, which is one of the few remaining industries that is still mostly guided by a generic and traditional strategy. If you have cancer, for instance, you will first undergo one treatment; if that therapy is unsuccessful, your physician will suggest that you undergo another therapy instead. The availability of large amounts of data makes it possible for cancer patients to get treatment that is tailored to their DNA.

Problems Associated with Storing Big Amounts of Data:

The storage of the huge volumes of data that are created each day inside legacy systems is the biggest problem, particularly when the data are presented in a variety of forms. Standard databases are unable to accommodate the storage of unstructured data.

Processing:

• Processing large amounts of raw data involves activities such as reading, processing, extracting, and formatting relevant information from the data. There are still challenges to be faced when it comes to the entry and production of information in uniform forms.
• Safety and security are major concerns for businesses and other organisations. Information that is not encrypted is susceptible to being stolen or damaged by cybercriminals. Therefore, specialists in the field of data security need to strike a balance between maintaining stringent security standards and allowing users access to data.
• Locating and Addressing Problems with the Data’s Quality.
• There are certainly many of you who are dealing with issues linked to the poor quality of the data, but there are solutions accessible. The following is a list of four strategies for addressing issues with data.
• Information that is correct in the database from the beginning.
• To correct any errors in the data, it is important to make any necessary repairs to the primary data source.
• In order to determine who someone is, you have to employ procedures that are quite exact.

Scaling Big Data Systems:

There are a number of efficient scaling strategies available, including database sharding, memory caching, migration to the cloud, and the separation of read-only and write-active databases. The use of all of these strategies in conjunction with one another will take you to the next level, despite the fact that each one of them is wonderful on its own.

Assessing and Choosing Appropriate Technologies for Big Data:

The market for such tools is growing at a fast rate, and businesses are investing millions of dollars in developing new big data solutions. However, in recent years the information technology sector has begun to recognise the promise of big data and analytics. The following are examples of technology that are now on trend:

• Apache Spark inside the Hadoop Ecosystem.
• R Software Support for NoSQL Databases.
• Analytics that are predictive and analytics that are prescriptive.

Big Data Environments:

An extended data set is distinguished from a data warehouse by the fact that it receives data on a continuous basis from a variety of sources, making it more dynamic. People who are in charge of the big data environment will quickly forget the origins of each data collection and where they got their information from.

Insights Available in Real Time:

The process of executing analyses on data at the same time as a system is gathering it is referred to as “real-time analytics,” and the word itself characterises the activity. Real-time analytics technologies, which utilise logic and mathematics to swiftly give insights on this data, allow decisions to be made with more precise information and in a more efficient manner.

Data Validation:

Validation of the data’s integrity, correctness, and structure is required before it can be used in any kind of business operation. The results of a data validation technique might be put to use for further analysis, business intelligence, or even the training of a model for machine learning.

Challenges Facing Healthcare:

There are many different sources of health-related big data, some of which include electronic health records (EHRs), genetic sequencing, medical research, wearables, and medical imaging. These are only a few examples of the numerous sources.Obstacles in the Way of an Effective Application of Big Data in HealthcareThe cost of putting this plan into actionPutting together and perfecting the data Security Disconnect in communication.