What is Big Data? Explanation of usage examples and analysis methods

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What is big data?

The present age is said to be the dawn of the “Fourth Industrial Revolution,” a technological innovation driven by AI and IoT , and digitalization is progressing in every aspect of people’s lives. Big data is becoming increasingly important as digitalization progresses. First, let’s take a look at the definition of big data and the benefits that can be gained from its use.

Explaining the definition of big data

“Big Data” is a concept that refers to huge groups of data of various types and formats. Generally, big data is defined as a data group consisting of four types: “Volume,” “Variety,” “Velocity,” and “Value.’ ‘ Volume means the amount of data, Variety means the variety of data handled, Velocity means the speed at which data is generated, and Value means the added value of the data, and the inclusion of these elements is a characteristic of big data.

The amount of data is too large to be handled by traditional processing methods, including all types of data such as structured data such as CS V and Excel, semi-structured data such as XML and JSON, and unstructured data such as audio files and image files . The collective term for data groups can be called big data. The purpose of big data utilization is to quantitatively analyze this huge group of data and connect it to the creation of new market value.

Benefits of using big data

One of the benefits of big data utilization is the realization of “DX (digital transformation).” DX refers to initiatives that bring about changes in the management system itself through the use of digital technology, and its purpose goes beyond mere IT utilization and digitalization. The essential purpose of DX is to utilize digital technologies such as AI, IoT, and cloud computing to establish a competitive advantage in the market.

In order to realize DX, it is necessary to build a production system that utilizes the huge amount of data collected and accumulated within the organization. For example, by analyzing data obtained from sales activities and promotions in combination with market research and demand trends, it is possible to develop marketing strategies and products that accurately capture potential customer demand and consumer insights. It becomes.

In other words, by utilizing big data, all data related to business activities can be quantitatively analyzed, making it possible to make logical management judgments and decisions that eliminate ambiguous elements such as intuition and experience. In corporate management, the longer the lead time from decision to implementation, the slower response to the market. If speedy and highly accurate data analysis becomes possible through the use of big data, it will lead to the construction of a management foundation that can flexibly respond to market changes.

Points to note when using big data

A big data analysis platform is essential for utilizing big data in the management field and building a management system that does not rely on intuition or experience. Solutions such as data lakes, data warehouses, and BI tools are required, and the costs associated with establishing these IT infrastructures are considerable. You also need ETL tools to extract and transform data according to your purpose, and a strong security system to protect collected data from information leakage incidents.

What can you do with big data? Introducing usage examples

【Agriculture】

The agricultural sector, which is a primary industry, was said to be the least advanced in digitalization and data utilization. However, in recent years, the introduction of AI and IoT has made real-time data verification and weather prediction possible, and agriculture is becoming smarter. By utilizing digital technology in the agricultural field, stable production and management is becoming possible for anyone, reducing the dependence of production systems that often rely on experience and intuition.

[Manufacturing industry]

In the manufacturing industry , factories are becoming smarter through the introduction of IoT and AI, and big data is being used in a variety of situations. For example, by using IoT to collect all kinds of data throughout a factory, it is possible to understand the operational status of electronic equipment, drive equipment, etc. in real time. This has resulted in results such as optimizing the staffing of production lines and reducing the amount of labor required to maintain production equipment.

[Distribution/Retail]

The distribution and retail industry is also an industry where the use of big data is progressing. The competitive environment has changed drastically due to the explosive spread of e-commerce sites and smartphones, and it is becoming difficult to create added value in the distribution and retail business fields without the use of big data. Specifically, big data will be utilized in areas such as integrated management of inventory data held by e-commerce sites and physical stores, formulation of marketing strategies that link sales analysis and customer data, and highly accurate demand forecasting using machine learning algorithms. It has been.

Introducing big data analysis methods

From here, let’s take a look at the typical methods of big data analysis. We will introduce an example of an analysis method using BI tools and AI.

Analyze with BI tools

BI tools are solutions that visualize collected data in graphs, flowcharts, etc. to support management judgment and decision-making. Big data analysis basically follows the process of “collection” → “storage” → “extraction” → “processing” → “visualization” → “analysis.” Generally, all data is collected and stored in a data lake, ETL tools extract and process the necessary data, and structured data is loaded into a data warehouse.

BI tools are solutions that visualize structured data loaded into a data warehouse. By visualizing data that is difficult to understand with text and numbers alone in illustrations and charts, you can analyze data from a bird’s-eye view and visually. However, BI tools are just solutions that specialize in data visualization, and it is the role of data scientists and data analysts to make the final decisions based on the knowledge gained.

Analysis by AI

While BI tools specialize in data visualization, AI is a solution that specializes in autonomous information processing using machine learning, deep learning, etc. For example, in the manufacturing field, IoT monitors the operating status of production equipment, and AI analyzes that data to automatically detect equipment abnormalities and failures, and is used in the areas of equipment maintenance and anomaly detection . .

In the distribution and retail industry, automatic customer service systems using AI and technologies that make personalized proposals to each customer are becoming widespread. In addition, the use of AI and big data is progressing in a wide range of fields regardless of industry, such as autonomous driving control systems in the automobile industry and diagnostic support in the medical field. Another big advantage is that AI can help solve the shortage of IT human resources by performing speedy and accurate analysis.

The importance and future of big data utilization

As mentioned at the beginning, we are currently at the dawn of the “Fourth Industrial Revolution” driven by AI and IoT, and it is predicted that the importance of big data will continue to increase as technology advances and develops. Particularly in the manufacturing field, where the shortage of human resources is becoming increasingly serious, it will become essential to automate production systems by linking AI, IoT, and big data analysis platforms. For companies aiming to create innovation in the manufacturing field, why not consider introducing an AI-based anomaly detection solution like Impulse?

summary

Big data refers to vast collections of data of all formats and types. The purpose of big data analysis is to utilize the various data collected and accumulated by an organization to create new value and realize structural reforms for the company, thereby establishing a competitive advantage. Try using big data to create your own unique market value that your competitors don’t have.