Before discussing serious issues like Big Data Breakdowns, it is logical that we first understand what big data is. Sorry to break it to you but there’s no one-size-fits-all in big data. Ironic, I know. But you can’t identify big data problems without knowing what big data is to you first and foremost.

What is Big Data?

Big data is the term for information assets (data) that are characterized by high volume, velocity, and variety that are systematically extracted, analyzed, and processed for decision making or control actions. This is a term related to extracting meaningful data by analyzing the huge amount of complex, variously formatted data generated at high speed, that cannot be handled, or processed by the traditional system. Data Expansion Day by Day: Day by day the amount of data is increasing exponentially because of today’s various data production sources like smart electronic devices. As per IDC (International Data Corporation) report, new data created per person in the world per second by 2020 will be 1.7 MB. The amount of total data in the world by 2020 will reach around 44 ZettaBytes (44 trillion GigaByte) and 175 ZettaBytes by 2025. It is being seen that the total volume of data is double every two years. The total size growth of data worldwide, year to year as per the IDC report is shown below:

3 Vs of Big Data

The majority of experts define big data using three ‘V’ terms. Therefore, your organization has big data if your data stores bear the below characteristics.

There are other ‘V’ terms, but we shall focus on these three for now.

• Volume – your data is so large that your company faces processing, monitoring, and storage challenges. With trends such as mobility, the Internet of Things (IoT), social media, and eCommerce in place, much information is being generated. As a result, almost every organization satisfies this criterion.
• Velocity – does your firm generate new data at a high speed, and you are required to respond in real-time? If yes, then your organization has the velocity associated with big data. Most companies involved with technologies such as social media, the Internet of Things, and eCommerce meet this criterion.
• Variety – your data’s variety has characteristics of big data if it stays in many different formats. Typically, big data stores include

word-processing documents, email messages, presentations, images, videos, and more fundamentally, it may be characterized in terms of being structured, semi-structured, or unstructured.

Structured Data:

Structured data takes a standard format capable of representation as entries in a table of columns and rows.This kind of information requires little or no preparation before

processing and includes quantitative data like age, contact names, addresses, and debit or credit card numbers.

Unstructured Data:

Unstructured data is more difficult to quantify and generally needs to be translated into some form of structured data for applications to understand and extract meaning from it.This typically involves

methods like text parsing and developing content hierarchies via taxonomy. Audio and video streams are common examples.

Semi-structured Data:

Semi-structured data falls somewhere between the two extremes and often consists of unstructured data with metadata attached to it, such as timestamps, location, device IDs, or email addresses.

Big Data Challenges and solutions:

Data Governance and Security:

Big data entails handling data from many sources. The majority of these sources use unique data collection methods and distinct formats.As such, it is not unusual to experience inconsistencies even in data with similar value variables, and making adjustments is quite challenging. For example, in the world of retail, the annual turnover value can be different based on the online sales tracker, the local POC, the company’s ERP, as well as the company accounts.When dealing with such a situation, it is imperative to

adjust the difference to ensure an appropriate answer. The process of achieving that is referred to as Data governance. We cannot hide the fact that the accuracy of big data is questionable. It is never 100 percent accurate. While that’s not a critical issue, it doesn’t give companies the right to fail to control the reliability of our data.And this is for good reason. Data may not only contain wrong information but duplication and contradictions are also possible. You already know that data of inferior quality can hardly offer useful insights or help identify precise opportunities for handling your business tasks. So, how do you increase data quality?

The Solution:

The market is not short of data cleansing techniques. First things first, though: a company’s big data must have a proper model, and it’s only after you have it in place that you can proceed to do other things, such as:

• Making data comparisons based on the only point of truth, such as comparing variants of contacts to their spellings within the postal system database.
• Matching and merging records of the same entity.

Another thing that businesses must do is to define rules for data preparation and cleaning. Automation tools can also come in handy, especially when handling data prep tasks.

Furthermore, determine the data that your company doesn’t need and then place data purging automation before your data collection processes to get rid of it before it tries to enter your network. Also, secure data with confidential computing, which safeguards sensitive information within your network.

Although, you should note that these apply to data quality on the whole, without associations with big data exclusively.

Organizational Resistance:

Organizational resistance.Even in other areas of business has been around forever.Nothing new here! It is a problem that companies can anticipate and as such,decide the best way to deal with the problem.

If it’s already happening in your organization, you should know that it is not unusual.Of the utmost importance is to determine the best way to handle the situation to ensure big data success.

The Solution:

Companies must understand that developing a database architecture goes beyond bringing data scientists on board. This is the easiest part because you can decide to outsource the analysis part.

Perhaps the biggest challenge entails pivoting the architecture, structure, as well as culture of the company to execute data-based decision-making.

Some of the biggest problems that business leaders have to deal with today include insufficient organizational alignment, failure to adopt and understand middle management, as well as business resistance

Large enterprises that have already built and scaled operations based on traditional mechanisms find it challenging to make these changes.

However, even without a CDO, organizations that want to remain competitive in the ever-growing data-driven economy require directors, executives, and managers committed to overcoming their big data challenges.

Big Data Handling Costs:

The management of big data, right from the adoption stage, demands a lot of expenses. For instance, if your company chooses to use an on-premises solution, you must be ready to spend money on new hardware, electricity, new recruitments such as developers and administrators, and so on.

Additionally, you will be required to meet the costs of developing, setting up, configuring, and maintaining new software even though the frameworks needed are open source.

On the other hand, organizations that settle for the cloud-based solution will spend on areas such as hiring new staff (developers and administrators), cloud services, development, and also meet costs associated with the development, setup as well as maintenance of the frameworks needed.

In both cases–cloud-based and on-premises big data solutions– organizations must leave room for future expansions to prevent the growth of big data from getting out of hand and in turn, becoming too expensive.

The Solution:

Whatever will save your company money is dependent on your business goals and specific technological needs. For example, organizations that desire flexibility usually benefit from cloud-based big data solutions.

On the other hand, firms whose security requirements are extremely harsh prefer on-premises any day.

Organizations may also opt for hybrid solutions where parts of their data are kept and processed in the cloud, with the other part safely tacked away on-premises. This solution is also cost-effective to a certain extent, so we can’t write it off completely.

Data lakes and algorithm optimizations can help you save money if approached correctly. Data lakes come in handy when handling data that need not be analyzed at the moment. Optimized algorithms are the way to go if you are looking for a way to minimize computing power by up to 100 times or even more.

In a nutshell, the secret of keeping the cost of managing big data as minimal and reasonable as possible is by analyzing your company’s needs properly and settling on the right course of action.

Data Scientists Shortage:

Only on infrequent occasions are the thinking of business leaders and data scientists as having the same status.

Analysts who are just beginning their careers are always deviating from the real value of business data, and, consequently, end up giving insights that fail to solve the issue at hand.

Then there is the problem of the limited number of data scientists capable of delivering value.

While surveys show that all professionals in the big data field are compensated exceptionally well, companies still have to deal with the difficulties of retaining top talent. Plus, training entry-level technicians is extremely expensive.

Solution: When There’s no Talent Available, Use Machines

To curb this situation, the majority of organizations are turning to self-service analysis solutions that utilize machine learning, AI, and automation to extract meaning from data by involving minimal manual coding. You can avoid this data scientist shortage by implementing data annotation in your business.

Those who haven’t resorted to this solution emphasize the importance of looking for talents where it is already present.

Instead of compromising and settling for under-skilled workers, be on the lookout for firms with a positive reputation, and as cruel as it sounds, poach talented workers who can be of assistance.

Otherwise, the adoption of automation, AI, and machine learning remains the most effective, inexpensive, and effective solution to the shortage of data scientists.

How Big Data Analytics Works:

Big data analytics is a process that uses data science with special software and algorithms to help businesses make sense of all this data. This software can partition the data into manageable chunks, which makes it easier to analyze. The algorithms then identify patterns and trends in the data that can help businesses make better decisions about their products and services.

Data Collection:

Data collection is the first and most important step, but the process looks different for every business.

Businesses can collect structured, semi-structured, and unstructured data from various sources such as cloud computing and storage, mobile apps, Internet of Things (IoT) gadgets, supply chain software, and other sources.

Some data will be stored in data warehouses where business intelligence tools and solutions can easily access it. Raw data that is too complex for a warehouse can be stored in a data lake and assigned metadata.

Data Processing:

After you’ve collected and stored data, you must organize it to ensure accurate results from predictive analytics and other queries. This becomes increasingly important as data sets become larger and are unstructured. The available data businesses have for decision making is growing rapidly, which makes data processing more challenging. Businesses can use batch processing, stream processing, or a combination of the two. The way you process data influences how useful the insights from it become.

Batch Processing:

Batch processing is a technique used in data processing to speed up the execution of a task by dividing it into a series of smaller tasks that can be executed concurrently. This technique is often used when the task involves I/O operations, such as reading or writing data, or when the task requires access to resources that are shared among several processors.

Batch processing allows tasks that are I/O-intensive to be executed on multiple processors simultaneously. This can improve performance by reducing the amount of time required to complete the task. Another benefit of batch processing is that it can improve resource utilization by allowing multiple tasks to share resources, such as memory and CPUs. Batch processing can also improve reliability by allowing tasks to be executed in parallel. If one task fails, the other tasks will continue to execute.

Stream Processing:

Stream processing is a type of data processing that deals with data streams as they are generated. In other words, the data is processed as it comes in, in real-time. This makes stream processing well-suited for applications that need to respond to changes in data as they happen, such as financial trading or fraud detection. Stream processing can also be used to quickly aggregate and process large amounts of data.

Data Cleansing:

No matter the amount of data you have, it requires regular cleaning or scrubbing to improve quality. Your data needs to be formatted correctly. Duplicate and irrelevant data needs to be removed or otherwise accounted for. “Dirty” data can result in poor insights that mislead you

Data Analysis:

• Data Mining:

Data mining is a process of extracting valuable information from large data sets. It is used to find patterns and trends that can help businesses make better decisions. Data scientists use various techniques, including statistical analysis, machine learning, and artificial intelligence, to extract insights from data.

Data mining can be used to identify customer trends, predict future behavior, and improve marketing strategies. It can also be used to detect fraud and other security threats. By analyzing large data sets,

data scientists can find correlations that would otherwise be impossible to detect.

The benefits of data mining can be seen in a wide range of industries. Banks use it to identify fraudulent transactions, retailers use it to determine what products to stock on their shelves, and healthcare providers use it to improve patient care

Predictive Analytics:

The term predictive analytics is used to describe a number of different analytical techniques that allow businesses to make predictions about future events.

Predictive analytics is made possible by advanced analytics techniques such as machine learning, data mining, and artificial intelligence. These techniques allow businesses to analyze large amounts of data in order to identify patterns and correlations. Once these patterns have been identified, businesses can use them to make predictions about future events

Predictive analytics helps answer questions like: “What will our sales be next month?” or “What are the chances that a customer will buy our product?” by providing probabilistic forecasts and insights.

Deep Learning:

Deep learning is a subset of machine learning that utilizes artificial neural networks to learn from data. It has been shown to be more effective than traditional machine learning methods in many cases.

Deep learning algorithms are able to learn feature representations of data that are much more accurate than those learned by other methods. This makes them better at tasks like classification and prediction.

Big Data Analytics Tools

• Hadoop:

Hadoop is a powerful big data tool that can be used to store, process, and analyze large amounts of data. It can be used for various tasks, such as processing log files, analyzing customer data, or creating machine learning models.

Hadoop is designed to scale to meet the needs of large organizations, and it can handle huge volumes of data. It also offers a variety of features and options that allow you to customize it to your specific needs.

NoSQL Databases:

NoSQL databases are becoming more popular as organizations move to big data solutions. These databases are designed for scalability and can handle large-scale data processing. They are also non-relational, meaning that

the data structure is not constrained by traditional relational database models. This flexibility makes them a good choice for big data solutions.

Apache Spark:

Apache Spark is a powerful open-source data processing engine built on the Hadoop Distributed File System (HDFS). Spark can run on clusters of commodity hardware and makes it easy to process large datasets quickly. Spark offers several advantages over traditional Hadoop MapReduce jobs. Spark can execute jobs up to 100 times

faster than Hadoop MapReduce, thanks to its in-memory data processing engine. Spark’s programming model is much more concise and user-friendly than MapReduce, making it easier for developers to write code. Spark also provides a number of built-in libraries for data analysis, including support for streaming data, machine learning, and graph processing.

Conclusion:

Managing, analyzing, and extracting insights from massive datasets involve overcoming significant challenges related to volume, variety, velocity, veracity, security, integration, and scalability. The advent of specialized tools and technologies has provided effective solutions to these challenges. Distributed computing frameworks, real-time processing tools, data quality management solutions, and scalable cloud platforms have revolutionized how organizations handle big data. Additionally, advancements in data visualization and analysis tools enable clearer and more actionable insights.