Big data analysis

The era of big data and big data analysis has come. By 2025, the global data field is estimated to grow to 175ZB.

Of course, Internet traffic is only a small part of the data pie created and stored around the world, which also includes all personal and corporate data. Today, the total amount of data in the entire world is between 10 and 50 ZB. How do we deal with all this data? What are the benefits of continuously collecting data through the Internet, personal devices, Internet of Things, etc.?

The answer is: "Analyze to gain insights." Somewhere in the endless ocean of data, there are answers to questions that will drive the future decisions of companies, governments, and society as a whole. But with so much data, where should I start?

In this article, I will introduce you to the basics of big data analysis and help understand why it is so important. You will understand the benefits it brings, the challenges it faces, how to analyze data, and many issues in the field of big data analysis .

table of Contents

1. What is big data analysis
2. Benefits of big data analysis
3. Big data analysis challenges
4. Data type
5. Types of big data analysis
6. Data analysis process

1. What is big data analysis?

Big data involves the "three Vs": volume, speed, and type.

IBM defines big data as a term that applies to data sets whose size or type exceeds the ability of traditional relational databases to capture, manage, and process data with low latency.

Big data has one or more of the following characteristics: high capacity, high speed, and high diversity. Artificial intelligence (AI), mobile, social, and the Internet of Things (IoT) promote data complexity through new data forms and data sources. For example, big data comes from sensors, devices, video and audio, the network, log files, transactional applications, the Web, and social media, most of which are generated in real time and on a large scale .

Big data analysis uses advanced analysis techniques to process huge and diverse data sets. These data sets include various forms of data collected from different sources (structured, semi-structured and unstructured data), ranging in size from TB to PB .

Second, the benefits of big data analysis

Big data insights can bring significant benefits to the company's revenue and revenue. From helping to discover fundamental problems to better understanding customers and operations, to informing and communicating, the impact of big data insights on organizations is almost infinite.

1. The benefits of big data analysis in business

**Faster and smarter decision-making: **The ability to process and analyze data in real time means that companies can take immediate action to solve problems, adjust strategies or decipher market trends.

** Efficient operations: ** Many companies use big data analytics to gain insights about internal supply chains or services, allowing them to make changes and streamline operations based on the latest information.

**Reduce costs: ** Not only can companies reduce costs by improving operational efficiency, but the cost of today's big data analysis infrastructure is much lower than that of past data systems. With the cloud, companies no longer need to build entire data centers, manage hardware, or hire large IT talents to maintain their jobs. These cloud-based analytics "stacks" mean they can get more benefits from their data without spending a lot of money.

**Improved product or service development: **Real-time market, customer or industry insights can help the company build the next great product or create a service that customers need urgently.

2. The benefits of big data analysis in government affairs

The impact of big data analysis is not limited to the private sector. Today, the government uses big data to inform new policy agendas, make comprehensive improvements to infrastructure, and invest in new social programs. Here are some recent examples of big data analysis for public sector work.

**Public Education: **The Ministry of Education uses big data to improve teaching methods and student learning. Higher education institutions use analytics to improve the quality of services and thereby improve student performance.

**Economic regulations: **Big data analysis helps to create financial models from historical economic data to formulate future policies. The Securities and Exchange Commission uses big data to regulate financial activities, detect bad actors and detect financial fraud.

**Environmental protection:** For more than two decades, the Department of Energy has been using data analysis in its research to better predict weather patterns, forest fires, and other environmental risks.

3. Challenges faced by big data analysis

Although big data applications are ubiquitous in enterprises, companies and governments that deploy big data analysis strategies still face many challenges.

1. Data growth

As mentioned earlier, the speed of data creation is amazing. One of the biggest challenges that companies face when using big data analytics is storing and analyzing all the data collected every day. What makes this particularly difficult is the amount of unstructured data that must be analyzed (more on this later).

If a company wants to use data, it must be stored in some type of analytical database, such as a data warehouse. With the rise of artificial intelligence (AI) and machine learning (ML) applications, data lakes are also frequently used. Of course, storage is only one part of it, and maintaining a healthy database without errors, duplication, and outdated or "bad" data requires human resources to manage it. This is why some data-led companies today have large data teams composed of engineers, data scientists, and analysts. As the company expands and creates more data, the data infrastructure becomes more and more complex over time.

2. Data integration

Today, data is collected from a variety of different sources, including enterprise applications, third-party software, social media, email servers, etc. This makes it difficult to centralize the data into a single database for analysis.

Since data integration is still a challenge for companies, modern ETL and ELT tools continue to emerge that simplify data pipelines by automating data collection and transmission to data warehouses. This technology makes data centralization possible and eliminates data silos that cannot be accessed by business teams.

3. Timely insights

Like most things in this world, data will also expire. With the increasing speed of creating new data today, teams must use the latest information to make decisions, which is not only necessary but also a priority. Otherwise, they risk operating according to outdated assumptions.

Due to the relatively short shelf life of data, organizations must analyze the data in real time as it is collected. This requires a powerful data system to collect the data immediately after it is created, transform it and store it in the analysis database so that it can be queried within a few minutes.

4. Governance

Managing business data can be challenging. As mentioned earlier, it is constantly changing, aging and moving between multiple systems. This may make it difficult to ensure data integrity, availability, accessibility, and security for the entire organization. This is the ins and outs of the governance process. With the correct big data governance strategy, data can be centralized, consistent, accurate, usable, and secure. Big data governance (and data modeling) also allows the use of a common set of data formats and definitions.

Data governance is essential. If the data is unavailable or inaccurate to business units, they will not be able to make informed decisions. The increase in data privacy regulations also requires other governance practices to meet compliance. These regulations are driving a large number of future governance strategies.

5. Security

Data security will always bring challenges to enterprises. Data is very valuable, and as the amount of sensitive information collected increases, there will always be opportunities to mitigate security risks.

Some of the more common challenges come from the need to keep up with rapidly changing regulations and security situations. This requires updating security patches and updating IT systems when new threats emerge. The inherent vulnerabilities in today's distributed technology framework can provide bad actors with opportunities to disrupt the system. Fake data or counterintelligence information is also commonly used, which can be used to destroy databases and prevent companies from deciphering facts from fictitious information.

Four, data type

1. Quantitative data and qualitative numbers

Quantitative data:
Quantitative data consists of hard numbers, which can be counted as things. Quantitative analysis techniques include:

• Regression: predict the relationship between the dependent variable and one or more independent variables.
• Classification (probability estimation): predict or calculate the likelihood that an individual belongs to a certain category.
• Clustering: Grouping individuals in the population based on similarity.

Qualitative data:
Qualitative data is more subjective and less structured than quantitative data. In the business area, you will encounter qualitative data from customer surveys and interviews. Common analysis methods include:

• Content analysis: used to classify different types of text and media.
• Narrative analysis: Analyze content from various sources, including interviews and field observations. When performing analysis, make sure that the indicators are in the format that the company has used. For example, if the company budgets on a quarterly basis, the indicators should reflect the same content.

2. Structured data and unstructured data

Data (whether quantitative or qualitative) can take a variety of shapes according to the nature of the information, how the information is collected, where it is stored, and whether it is created by humans or machines. There are two main levels of data structure to consider: structured data and unstructured data.

Structured data:
Structured data is strictly formatted information, so it can be easily searched in relational databases. Usually quantitative information. Examples include names, dates, emails, prices, and other information we use to view stored in spreadsheets.

Structured data is organized and read through machine code, making it possible to easily add, search or manipulate structured data in relational databases using SQL. For example, the information collected by e-commerce at the point of sale may include product name, purchase date, price, UPC number, payment method, and customer information, all of which can be easily searched or analyzed later to find trends or answer questions.

At first glance, it is difficult to extract insights from structured data alone. But using analytical tools can decipher interesting trends. For example, customers in Boston tend to buy specific products at higher prices in February and March. This insight may remind you to increase the inventory of the product in the retail store during those months to meet regional demand.

Unstructured data:
Unstructured data is the complete opposite of structured data. It is usually qualitative data, using traditional databases or spreadsheets to search, operation and analysis are challenging. Common examples include images, audio files, document formats, or someone’s social media activities.

Unstructured data lacks a pre-defined data model, so it is not easy to read or analyze in a relational database, which means that a non-relational (or NoSQL) database or data lake is required for searching. To extract insights from such data, advanced analysis techniques such as data mining, data stacking, and statistics are required.

Unstructured data insights can help companies understand content such as customer sentiments and preferences, buying habits, and more. Analyzing these types of data is more difficult. However, with the right resources, intelligence that can give you a competitive advantage.

Semi-structured data:
Semi-structured data is between structured and unstructured data formats. The data has clearly defined characteristics, but lacks a strict relationship structure. It includes semantic tags or metadata that can create a classification hierarchy, making it easier to machine readable during analysis.

The most common everyday example that most people encounter is a smartphone photo. Ordinary photos taken with smartphones contain unstructured image content, but are time-stamped, geo-tagged, and carry identifiable information about the device itself. Some common semi-structured data formats include JSON, CSV and XML file types.

Semi-structured data constitutes most of the data generated in the world today. Think about all the photos taken every day. Semi-structured data is often associated with mobile applications, devices, and the Internet of Things (IoT).

V. Types of Big Data Analysis

There are four main types of analysis, each of which varies in complexity and the degree of insights that can be generated for the organization. Despite these four categories, each category is interrelated and can be used in conjunction with each other to unlock a deeper and more meaningful understanding.

1. Descriptive analysis

Descriptive analysis can help you answer the question "What is happening?" It is the most common form of analysis and the basis for all other types of analysis.

Anyone who has seen real-time dashboards or read quarterly reports should be familiar with descriptive analysis. Usually related to tracking key performance indicators within the organization. In practice, this may include measuring marketing and sales metrics, such as the number of qualified potential customers in the fourth quarter.

2. Diagnostic analysis

Once you know what happened, you will naturally trace a question: "Why did it happen?" This is the highlight of diagnostic analysis.

This type of analysis requires deep digging "behind the dashboard" to better understand the root cause of a particular result or continuing trend. In practice, diagnostic analysis can help marketing teams understand which campaigns attract qualified potential customers.

3. Predictive analysis

Predictive analytics can help answer "What is most likely to happen in the future?"

Based on past trends, this type of analysis uses historical data to predict future results. Predictive analysis is based on insights gained through descriptive and diagnostic analysis, and uses statistical models to predict the most likely future.

4. Normative analysis

Normative analysis helps organizations understand "what should we do next?" to solve current trends or problems. It is more complex than other forms of analysis, which means that most companies lack the resources to deploy it.

Prescriptive analysis usually requires the use of advanced data science and artificial intelligence to digest large amounts of information and propose decisions to solve existing organizational problems.

Six, big data analysis process

Without the right process, it will be difficult to obtain analytical insights from the organization's data. The process of collecting, processing, and analyzing data is as important as just raw data. The right process can ensure that the insights derived from the data are accurate, consistent, and that there are no false trends.

1. Understand the data goals and requirements

A clear understanding of the company's goals and needs will help you conduct big data analysis from the start. What type of data will you collect? How will you store it? Who will analyze? All of these issues are important, and ultimately determine not only the data infrastructure you need to build, but also what type of analysis tools you need.

2. Collect and centralize data for analysis

After you clearly understand your goals, you need to extract data from systems and applications and transfer it to a data warehouse or data lake. This is where ELT and ETL solutions come into play. They help copy data to the cloud warehouse for analysis. This centralized data storage allows you to have a more comprehensive view of the entire company and eliminates any data islands that may exist along the way. Data can be captured from applications, e-commerce events, other databases, etc.

3. Model data for analysis

Once the data is placed in a central data store, it can be analyzed technically. But before opening the door to data, you may need to consider the data model first. Data modeling defines how data is related, its meaning, and how to link together. An effective model can make data easy to access and use, and ensure that people use the correct information in the appropriate environment, and it requires close collaboration between data and domain experts.

4. Analyze the data

After collecting, processing, storing, and modeling data in a queryable data warehouse, you will need an analysis tool that can complete a search of all data and return actionable insights to guide business decisions. It is important to fully understand your needs from real-time analysis tools. Every company is unique and needs will vary. We recommend evaluating internal needs and aligning purchasing decisions with these goals.

It should also be noted that not all analysis tools are the same. Companies usually deploy multiple tools for different teams or business units. With this in mind, here are some guidelines to consider when choosing an analysis tool.

5. Explain insights and inform decisions

Using various types of analysis methods, you can discover various insights from company data. You can analyze the past, track operations in real time, and even predict what might happen in the future. These trends can improve competitive advantage, help create better products and services, provide a better customer experience, and more.