What is data integration? A comprehensive explanation of its purpose, benefits, and practical methods

Data Integration Definition and Basic Concepts

First, let's clarify the meaning of data integration and its basic concepts.

Data integration is the process of organizing and centrally managing data across multiple systems and formats so that it can be used effectively in business. It is needed in a variety of situations, regardless of company size or industry, and its significance lies in standardizing information of different formats and structures as much as possible and storing it in a state of high data quality.

The reason why data integration is attracting attention is that the amount of data fragmented across systems continues to increase. When data is not circulated properly between departments or organizations, duplicate management of the same information occurs, which makes analysis and decision-making time-consuming and labor-intensive. Data integration is attracting attention as a concept that can solve these fundamental problems.

Furthermore, in data integration efforts, it is important not only to collect data, but also to cleanse it to maintain consistency and eliminate duplicates. By thoroughly managing quality in this way, the subsequent analysis phase and operational processes will proceed smoothly, ultimately contributing to the promotion of digital transformation throughout the organization.

The background to the need for data integration and its relationship with DX

We will look at the background behind why data integration is considered essential in the context of promoting DX.

In recent years, many companies have been aiming to innovate their business models and improve productivity through digital transformation (DX). However, to make DX a reality, it is essential to understand where the necessary data is located and to create a system that allows it to be used quickly.

If data remains fragmented, information sharing between mission-critical system, core system and subsystems will be insufficient, and data utilization processes will tend to stagnate. As a result, the capabilities of new systems and AI introduced will not be maximized, and the true value of DX will not be fully realized.

Data integration removes these barriers and provides the foundation for smoothly linking information between departments and systems, enabling initiatives directly linked to DX, such as the development of new digital services and the automation of business processes.

The importance of data integration and interoperability

We will explain interoperability, which is the key to connecting multiple systems and tools, and the role of data integration that supports it.

In business, various systems such as ERP, CRM, and MA (marketing automation) operate independently, and in many cases, each system uses a different format and data structure. Interoperability refers to the ability of these systems to work together seamlessly and exchange information correctly.

To achieve interoperability, it is necessary to develop systems that can communicate in the same language. The key here is the concept of data integration, which involves incorporating data in different formats into a common platform and utilizing it while maintaining consistency.

With a data integration perspective, it is possible to maintain collaboration without major disruptions even when systems are added or changed, which results in increased efficiency in IT investment across the entire company and enables quicker responses to new business opportunities.

The benefits and effectiveness of data integration

Practicing data integration can bring about significant changes in how an organization uses information and makes decisions.

Data integration allows companies to consolidate information generated by various departments and projects in one place and handle it without duplication, which reduces internal communication costs, enables faster report creation, and significantly improves business efficiency.

Furthermore, analysis using integrated data is highly accurate and can be easily shared throughout the company as a single source of truth. Decisions based on accurate information not only improve the effectiveness of business strategies, but also play a major role in risk management.

Another major attraction is the ability to dramatically improve system efficiency. For example, consolidating multiple data management tools not only reduces costs, but also makes it easier to introduce analytical environments and BI tools, accelerating the creation of business value.

1. Centralized management of information

Previously, different systems were used for different departments and projects, which meant that the same customer information was often managed in multiple databases. Data integration eliminates this duplication and confusion, allowing you to find the information you need with a single search.

Centralized information management improves operational efficiency and makes it easier for personnel to obtain accurate and timely information when making decisions. This is especially true for sales and marketing departments, as it dramatically improves the speed and accuracy of customer approaches and the implementation of measures.

2. Improved accuracy and speed of data analysis

When data flowing in from multiple systems is managed in a unified format, it becomes easier to input data into analytical tools, enabling faster insights. The analytical process itself also becomes simpler, making it easy for not only data scientists but also field personnel to use, which is a major advantage.

Furthermore, data analysis using consistent information reduces the risk of misjudgments due to errors or duplications. Sharing reliable reports across the entire company will dramatically accelerate the speed of decision-making.

3. Reduced system operating costs

Data integration reduces the effort required to manage and operate multiple systems individually. By eliminating duplicate functions and databases, operational costs such as software license fees and maintenance costs can be optimized.

It also leads to more efficient troubleshooting. Even if a problem occurs, the integrated platform makes it easier to grasp the status of the entire system at a glance, which increases the likelihood of reducing the time it takes for the system to malfunction.

4. Promoting Business Intelligence (BI)

The combination of data integration and BI tools makes it easier for companies to create more sophisticated dashboards and reports, creating an environment where everyone from management to field staff can share the same data and understand the situation in real time.

Visualization through BI also has the effect of instilling data-based decision-making into the organizational culture. Daily checking of accurate and easy-to-read indicators leads to faster strategy planning and problem detection, and builds the foundation for flexible response to rapidly changing market environments.

5. Using Generative AI in Business

Nowadays,Generation AIThere is also a movement to utilize it in business. However, one concern is the issue of hallucination. Grounding is considered an effective way to suppress hallucination.RAG (Retrieval Augmented Generation)RAG searches and retrieves highly accurate data based on queries given to the generative AI model, and generates content based on this data. By integrating internal data and building RAG using quality-guaranteed data, the quality of the generative AI output is significantly improved, enabling the provision of more useful and accurate information.

Typical issues and points to note when integrating data

Smooth data integration requires measures on both technical and operational levels.

1. Support for a variety of data sources

It is not uncommon for companies to have data sources in different formats scattered across different departments, such as Excel files, cloud services, on-premise mission-critical system, core system, etc. To handle all of this data together, flexible data import functions and conversion logic are required.

To reduce the difficulty of the process, it is important to define the scope of data collection and priorities in advance and proceed with the integration in stages. Trying to integrate everything at once increases the workload and the risk of quality control issues.

▼I want to know more about on-premise
⇒ On-Premises | Glossary

2. Maintaining data quality and accuracy

During the data integration process, various data irregularities such as input errors, duplications, and missing values may become apparent. It is important to create a system for cleansing the data before integration and eliminating incorrect information.

Furthermore, if the update frequency differs between mission-critical system, core system and subsystems, consistency checks are required to ensure that the latest information is always reflected. If this is neglected, the data used in the field will remain outdated, which could hinder decision-making.

3. Security and Privacy

The more data is consolidated on an integrated platform, the greater the risk of information leaks and unauthorized access, so measures to ensure security are essential. For example, access authority management, log monitoring, and encryption are essential measures.

In particular, when handling personal information, it is necessary not only to comply with legal regulations but also to clarify the company's security policy and thoroughly publicize operational rules. Sharing awareness of privacy protection at the company-wide level will also increase the reliability of data utilization.

4. Data silos between organizations

Within a company, differences in objectives and cultures between departments can easily lead to silos where data is not or is not shared. Before proceeding with data integration, it is important to establish cross-organizational governance policies and basic rules for information sharing.

Even if technological integration is advanced, sufficient results will not be achieved if there is a lack of cooperation and communication between departments. It is essential to establish operational flows and cooperation systems and create a system in which the benefits of data utilization are shared throughout the company.

▼I want to know more about siloing
⇒ Siloization | Glossary

Data integration process

Understanding the specific implementation flow will help you proceed with your plan smoothly.

1. Clarification of the purpose of integration and the target data

The key to success is to first clarify the purpose of integrating data. Set a scope that directly relates to specific business issues or goals, such as sales, marketing, or business planning, and then identify the data sources to be integrated.

If you clarify the use cases for the data and the required quality level at this stage, the cleansing and tool selection in the subsequent process will proceed smoothly. If you proceed with the purpose and scope unclear, there is a high risk that the scale of the integration project will increase unnecessarily.

2. Data cleansing and processing

Data cleansing removes input errors, duplicate records, unnecessary spaces, etc., preparing the data for analysis and sharing. In particular, in cases where there are duplicate customer names or product names, IDs are assigned and mapping is performed to ensure uniqueness.

During the processing stage, we integrate formats and convert data types to ensure compatibility between tools and systems. By doing this kind of advance preparation properly, the effectiveness of the analysis and BI implementation that will be carried out later will be dramatically improved.

3. Introduction and verification of integrated infrastructure and tools

Select an integrated platform such as a data warehouse or data lake, and consider what tools to use for ETL (extract, transform, load) and data replication. It is important to choose between on-premise or cloud, taking into account the balance of cost, scalability, and security requirements.

After implementation, you will go through a pilot operation and verification phase before moving on to full-scale deployment. At this time, be sure to thoroughly check the operation of data integration in a test environment to minimize unexpected problems.

▼I want to know more about data integration
⇒ data integration / data integration platform | Glossary

4. Establishment of an operation and improvement cycle

Data integration is not something that can be implemented once and then finished; continuous operation and improvement are essential. We monitor daily operational processes, such as monitoring data quality, detecting errors, and reviewing update frequency, and optimize them accordingly.

Furthermore, integration targets and analysis requirements will change in line with organizational changes and shifts in business strategies. By regularly gathering stakeholders to share issues, review operational flows, and consider introducing new tools, the effectiveness of data integration can be continuously improved.

Data integration use cases and success points

Learn how data integration is being used across a variety of industries and departments.

1. Marketing Case Studies

In the marketing field, there are increasing cases where diverse data such as customer behavior data, purchase history, and reactions on social media are integrated to help plan comprehensive measures. An integrated environment also makes it easier to optimize the analysis required to launch effective campaigns for each segment.

For example, integrated customer data makes it possible to deliver personalized advertising and send tailored to each individual's preferences. Some companies are now able to significantly improve their marketing ROI by collecting information in a timely manner and analyzing the effectiveness of their initiatives in real time.

2. Examples of production activities

In production operations, efforts are spreading to centrally manage factory operating status, inventory information, equipment sensing data, and other data to optimize production plans in real time, which is expected to reduce material waste and improve line utilization rates.

Furthermore, an increasing number of companies are reducing the risk of operational downtime by analyzing data collected from manufacturing equipment and using it to detect signs of defects and calculate maintenance times. These efforts offer a major advantage in achieving both stable quality assurance and cost reduction.

3. Examples of Cross-departmental Data Utilization

In large organizations, multiple departments tend to hold their own independent data, which can slow down collaboration across departments.By introducing data integration and making it possible to view information from each department across departments, the speed of new product development and service improvements can be significantly increased.

For example, by combining order information from the sales department with customer support inquiry history, it becomes easier to discover customers' latent needs, creating added value that was previously invisible.

Data Integration FAQs

We will explain in a Q&A format the questions that many people have before introducing data integration.

Q : What is the difference between data integration and data integration?

A data integration refers to the passing of data between systems and focuses more on point-to-point coordination. Data integration, on the other hand, is a concept that builds an entire system for organizing, centrally managing, and utilizing information.

Q: Is it possible to integrate data in real time?

A: It is possible depending on the tools and infrastructure, but there are issues such as the load on the system, costs, and ensuring data quality. Selecting the appropriate architecture and defining requirements are essential.

Q: How long does a data integration project take?

A: It depends greatly on the scope of integration, the amount of data, and the implementation tools selected. A small-scale POC might take anywhere from a few weeks to a few months, while a large-scale implementation might take anywhere from six months to a year or more.

 

Summary | For successful data integration

Finally, we will summarize the key points for smooth and successful data integration.

Data integration is essential for increasing business competitiveness in the digital transformation era. Organizing and aggregating data across diverse systems and formats creates a foundation for maximizing the value of information.

The key to success is to set clear objectives, start small, and consider continuous operation and improvement as a set. It is best to steadily proceed with the integration process under a consistent strategy while also establishing governance and security systems for the entire organization.

The person who wrote the article

Affiliation: Marketing Department

Yumi Ogawa

After two years of experience as a copywriter at an advertising agency, she has been working in the IT industry ever since. Her experience at a variety of companies, from B2C to B2B, and from Japanese ventures to major foreign companies, is her strength. She has consistently worked in a variety of marketing-related roles, including public relations, branding, product marketing, and campaign management, and has been in her current position since May 2024. In her private life, she loves interacting with nature, hot springs, and public baths.
(Affiliations are as of the time of publication)

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