data integration / data integration platform

Glossary

"data integration / data integration platform"

This glossary explains various keywords that will help you understand the mindset necessary for data utilization and successful DX.
This time, let's think about what really matters in determining whether data and IT utilization initiatives will work well and produce results.

What is data integration / data integration platform?

data integration refers to a variety of IT applications that use data linkage processing, such as "connecting" different IT systems by passing data between them, or combining data obtained from multiple data sources.
The integration of different systems and data sources with each other is important for the successful use of IT systems installed in a company, for the successful use of data, which has become a popular topic in recent years, and for the introduction and use of cloud computing, for example. data integration platform is the infrastructure that is in place for such data integration utilization to work well.

Why is data integration necessary?

data integration" refers to the use of data linkage processing for various IT applications, such as linking different IT systems via data or combining scattered data.

I don't think there are many people who don't understand the meaning of the term "data integration," but when it comes to "why data integration is becoming such a hot topic," it may be difficult to imagine.

There are many needs for data integration in our daily lives

There are a huge number of situations in the world where data integration is required. It's just that it's not intuitively understandable and is often not even noticed, but there are many situations where data integration is needed even in everyday life.

Think about the IT environment at your workplace. It's unlikely that your company has only one type of IT system or cloud. If so, there is usually a need for data integration between those systems and clouds.

For example, if the sales department uses Salesforce as a system (SFA) to manage sales activities, and the accounting department uses a system to process orders and manage the company's finances, there is a need for data integration between these systems. For example, when a salesperson acquires a job, the customer information stored in the sales department's system must also be entered into the accounting department's system. It is often necessary to transfer data between systems, such as by linking data with a production management system to produce ordered items at a factory.

When there are multiple IT systems and clouds like this, there is often a need for some kind of data integration between them. And since different departments need different IT, it is natural for a company to have multiple systems.

The need for data integration also stems from the fact that "the data you want to use is scattered all over the place." For example, when preparing materials to be presented at monthly management meetings, it is common to collect data from various locations within the company, enter it into Excel, aggregate it, create graphs, and then paste it into PowerPoint to create the materials. In addition to the actual process of creating the materials, it is not uncommon for a lot of time and effort to be spent on "collecting the necessary data, pre-processing it, and aggregating it."

Common tasks like data analysis and monthly reporting often require the collection of data.

Even in situations where a company has just introduced the cloud, the need for data integration can arise. For example, let's say you decide to introduce kintone and use it in your department. Since kintone does not initially contain the data your company needs, you must first import the necessary data from an external source. Furthermore, if you want to use the data in kintone in another system, you will need to integrate that data with an external system. In other words data integration is also necessary when introducing and using kintone.

The introduction of cloud services also often creates the need for data integration.

There are many smaller, more familiar needs for data integration as well. When working on something using an Excel file, you often find yourself needing data in another Excel file, or the data you need is in an attachment you received by email. These everyday situations involve wanting to refer to data that you don't have at hand, and are in fact problems of "data integration."

When using data on a PC on a daily basis, such as struggling with Excel files, the essence of the problem is often the need for data integration

(But) why is data integration not a hot topic?

When you think about it, the world is filled with needs for data integration. However, I don't think we often think about it that much. The reason why data integration is not talked about even though there is a need for it is because it is often "left unconscious in an unhealthy way" as follows:

I'm doing it manually (double entry issue)

After reading the examples of data integration needs listed above, some of you may have thought, "Yes, that does take time and effort, and there are troublesome situations." In reality, data integration needs are often viewed as a "kind of chore (troublesome)" and are often handled manually as a matter of course.

In reality, it's common to see people outputting order information from a sales management system and manually re-entering it into a production management system, or poring over a huge amount of Excel files every month to create materials for management meetings with determination and determination, or exporting and importing CSV files from kintone multiple times. However, these are not things that essentially require people to do.

IT is supposed to be introduced to make work more efficient and convenient. However, if the introduction of IT results in an increase in manual chores, this is actually putting the cart before the horse. However, it is not uncommon for people to assume that "these things should be done manually" and fail to recognize that this is a problem.

Reasons for stagnation in IT utilization

The effort required for data integration can sometimes hinder progress in IT utilization.

Let's say you introduce a new system (for example, Salesforce for the sales department). However, it doesn't mean you can immediately use it in your work. You need to re-enter business data and past data into the new system, and you also need to input data that was previously integrated with business procedures and recorded in various places (paper, Excel on-site, etc.) into the new system.

If data integration situation remains poor, introducing new IT can become a hassle, with people saying things like, "It's more work," "We have to enter data twice into the new system," or "It wouldn't be a hassle if we just kept things the way they were before," which can lead to the system not being put to good use even after it has been introduced.

Why IT use doesn't spread beyond small successes

I think that issues with data integration may limit the widespread use of IT.

For example, let's consider a situation where kintone has been introduced and is being used in the workplace. Many apps have been created within the department, and the usefulness of kintone has been well understood. However, despite widespread use, its use in initiatives that significantly change the entire business process may be lacking.

There is no problem if the usage method can be completed within the cloud service, but if you want to truly expand the use of IT, for example, if you try to implement an initiative that will change a wide range of business operations, "data integration" with various external data and IT systems will become an essential element for realization. In that case, the hassle of doing so can limit the expansion of usage.

To achieve results that meet the expectations of a company president who wants to "make a leap forward in business with IT," it is often necessary to essentially data integration with internal and external data and systems. The reason why small successes in IT utilization have not spread widely may be due to "data integration."

The same data is in various places, causing inconsistencies

When multiple IT systems or cloud services are used within a company, it is common for the same data to be stored in multiple systems. An easy example of this is employee list data.

For example, a sales management system should have information about everyone in the sales department registered. An expense reimbursement system managed by the accounting department should also have data about all employees, including who is there and which department they belong to. If you use Google Calendar for schedule management, all employee data should be stored there, and if the entire company uses kintone, all employee data should be stored there as well.

In this way, the same data may be registered in multiple places. For example, in a "List of Business Partners" or a "List of Products." This situation is unavoidable if there are multiple systems (because they cannot be operated without inputting data). However, the problem is what will happen if you continue to operate them as they are.

For example, with employee list data, every time someone joins the company or changes departments, the registered information must be updated across all systems. Not only is this a hassle, but it can also lead to input errors or forgetting to update data in some systems. As a result, data consistency between systems can become inconsistent.

If data is not consistent, accidents can occur. For example, if there is an error in the "List of Suppliers," products may be sent to the previous address and not arrive. If the data in Systems A and B is inconsistent and you don't know which one to trust, it may even become necessary for a person in charge to manually check the data before each shipment to prevent accidents.

This kind of data confusion can be said to be a problem that arises because data integration has not been properly established.

However, simply linking data creates new problems

As you can see, there are many needs for data integration, and they may not be easily noticed. data integration problems are often the cause of IT not working properly or IT being difficult.

So, does simply "linking data" solve these problems? In fact, this is not the case.

New problems that tend to arise when developing "automatic data integration"

What should we do if we can't "data integration" properly? The solution that immediately comes to mind is to "solve it through programming," since this is IT. If we write a program that "automatically data integration from system A to system B," it seems like the solution would be to create an "automatic data integration system."

This certainly satisfies individual data integration needs. However, the problem is that, as we've explained so far, there are many needs for data integration, and so it's easy to create a large number of data integration processes all at once. As a result, the various systems and data sources within a company can end up in a complex, spaghetti-like mess with numerous data integration.

Ironically, the more we try to understand and solve the many data integration needs in the field, the more likely this situation is to occur.When this happens, we can end up in a situation where it is impossible to understand "which system's data is linked to which system" without deciphering the numerous integration processes.

When this happens, it becomes difficult to understand how a change to one system or piece of data will affect others, which can lead to a situation where systems or data structures cannot be changed carelessly, resulting in an IT system that is unable to move forward.

The failure of overhauling a "monolithic system"

When systems become intricately intertwined due to data integration, and modifications to each system pile up, making it difficult to understand the overall picture, the solution that is often suggested is to revamp the system into a "huge, monolithic system."

The essence of the problem is sometimes seen as "a failure to optimize IT overall," and proposals are made to solve the problem by quickly shifting to huge cloud services or IT packages that cover the entire business of the company.

However, the need for data integration is not something that can be eliminated by optimizing IT; rather, it is a need that naturally continues to arise as business and IT evolve and change on a daily basis. It is difficult to expect that everything will remain within expectations after an updated system is updated. The need for data integration must be continually addressed.

"All your data in one place" (unsolved by)

A similar proposal seems to be that if data is stored centrally in one place, there will be no need for data integration, making data use more efficient.

However, the reason there are so many IT systems within a company is because the nature of each department's work differs, and data is generated in essentially different ways and at different times within those systems. Even if the storage location is centralized, the essence of the data will not be the same.

It may be possible to consolidate long-term storage of analytical data in one place, but when it comes to live data related to business operations, or the very nature of the data itself, there will be different circumstances for each of the diverse data sources, and data integration based on these will still be necessary.

It's not just data clerical work (RPA boom)

I have mentioned that the need for data integration is often inappropriately perceived as a "manual chore on a PC." If this perception remains unchanged and people try to solve the problems caused by data integration, they will likely introduce "RPA (Robot Process Automation)," a method of automating PC operations that was popular for a while (but has since died down).

RPA was once a fad, but it has not yet become a standard part of IT systems. Of course, if RPA is used for the right purposes, it can be effective and produce useful results. However, in cases where the essence of the problem to be solved is "data integration" (solving data problems), I believe that it has often not been able to solve the problem effectively.

A "data integration platform" necessary to solve data integration problems

We introduced the fact that there are many needs for data integration in the world, and that even if you automate the integration process thinking that integration will solve the problem, it may not work well.

There is a need for data integration, but it is difficult to solve it effectively.When it gets to this point, it becomes clear that a "data integration platform" that can solve these various problems is desperately needed.

A dedicated platform for "connecting" (known as "EAI" or "iPaaS")

data integration is essential for utilizing IT, but as mentioned above, creating it individually according to needs can easily lead to confusion overall. To avoid such confusion, we will establish a "platform that specializes in integration processing" as shown below.

Introducing a collaboration platform that centralizes data integration
There, we will centralize the processing of connections to the company's IT systems and various data sources.

This allows collaboration processing to be consolidated on the collaboration platform, rather than being scattered across the company.

Ability to connect to a wide variety of systems and data sources

data integration platform can be realized using a variety of methods and technologies. However, methods that only allow connection to RDBs (configured only with RDB-based interfaces) or only allow integration using a set format of REST API (configured only with APIs) may not be able to fully accommodate the diverse needs of real-world IT.

In reality, you will likely need to read and write the contents of Excel files in the field, directly access data stored in traditional business applications, and refer to data stored in long-standing mainframes (such as AS/400).You need to be able to handle such a wide variety of connection needs without any problems.

Of course, it must also be able to handle a wide range of data access related to the latest cloud services and new technologies emerging every day (such as generative AI).

Ability to develop collaborative processes without coding

data integration processing arises from the reality of IT utilization. There are IT systems that handle a variety of tasks, and these are inherently diverse. IT itself is evolving every day as a technology, and above all, the tasks and businesses that IT is involved in are also changing every day.

As new clouds emerge, new integration needs arise, and as new business initiatives are undertaken on a daily basis, new integration needs arise. In other words, data integration must be continually reworked and improved to adapt to the ever-changing IT and business situations.

For these reasons, data integration platform are required to be able to develop data integration without coding. Having to compile requests and ask IT engineers to program every time something happens slows down business. Being able to quickly solve problems on the ground will be important in the future use of IT.

Performance and stability to handle your business

However, since it is an IT system that handles business operations, it needs to have high processing performance capable of handling full-scale data processing.Furthermore, it needs to operate stably without crashes, and be able to recover from hardware failures without becoming unmanageable, making it "safe, secure, and reliable" and "IT that can withstand professional use" so that it can be used with peace of mind to handle corporate operations.

There are many no-code, easy-to-use products on the market, but when it comes to IT products that can be trusted to run your business even in situations where operations will come to a halt if the IT system goes down, the options are quite limited.

"Connecting" technology as a means to realize data integration needs

I explained that there are many needs for data integration in the world, that in order to successfully advance data integration, it is necessary to establish a "data integration platform," and what is required of data integration platform. You may understand this, but wonder how to prepare data integration platform that meets such requirements.

In fact, there are already data integration platform products that can meet the requirements I have written about so far.

Please utilize "connecting" technology

There are methods that allow you to efficiently develop data environments that connect to a wide variety of systems and data on the cloud, read, process, and transfer data as needed, all with just a GUI.These are "connecting" technologies such as "DataSpider" and "HULFT Square," also known as "EAI," "ETL," and "iPaaS."

Can be used with GUI only

Unlike regular programming, there is no need to write code. By placing and configuring icons on the GUI, you can achieve integration with a wide variety of systems, data, and cloud services.

Being able to develop using a GUI is also an advantage

No-code development using only a GUI may seem like a simple compromise compared to full-scale programming. However, being able to develop using only a GUI allows on-site personnel to proactively work on cloud integration themselves. On-site personnel are the ones who know the business best.

Full-scale processing can be implemented

There are many products that claim to allow development using only a GUI, but some people may have a negative impression of such products as being too simple.

It is true that things like "It's easy to make, but it can only do simple things," "When I tried to perform serious processing, it crashed without being able to process," "The data processing couldn't be finished by the time work started in the morning," and "It didn't have the high reliability or stable operating capacity to support business, which caused problems" tend to occur.

"DataSpider" and "HULFT Square" are easy to use, but also allow you to create processes at the same level as full-scale programming. They have the same high processing power as full-scale programming, as they are internally converted to Java and executed, and have a long history of supporting corporate IT. They combine the benefits of "GUI only" with full-scale capabilities.

No need to operate in-house as it is iPaaS

DataSpider can be operated securely on a system under your own management. With HULFT Square, a cloud service (iPaaS), this "connecting" technology itself can be used as a cloud service without the need for in-house operation, eliminating the hassle of in-house implementation and system operation.

Related keywords (for further understanding)

Keywords related to data integration and system integration

EAI
- It is a concept of "connecting" systems by data integration, and is a means of freely connecting various data and systems. It is a concept that has been used since long before the cloud era as a way to effectively utilize IT.
ETL
- In the recent trend of actively working on data utilization, the majority of the work is not the data analysis itself, but rather the collection and preprocessing of data scattered in various places, from on-premise to cloud.
iPaaS
- A cloud service that "connects" various clouds with external systems and data simply by operating on a GUI is called iPaaS.

Are you interested in "iPaaS" and "connecting" technologies?

Try out our products that allow you to freely connect various data and systems, from on-premise IT systems to cloud services, and make successful use of IT.

The ultimate "connecting" tool: data integration software "DataSpider" and data integration platform "HULFT Square"

"DataSpider," data integration tool developed and sold by our company, is a "connecting" tool with a long history of success. "HULFT Square," a data integration platform, is a "connecting" cloud service developed using DataSpider technology.

Another feature is that development can be done using only the GUI (no code) without writing code like in regular programming, so business staff who have a good understanding of their company's business can take the initiative to use it.

Try outDataSpider/ HULFT Square 's "connecting" technology:

There are many simple collaboration tools on the market, but this tool can be used with just a GUI, is easy enough for even non-programmers to use, and has "high development productivity" and "full-fledged performance that can serve as the foundation for business (professional use)."

It can smoothly solve the problem of "connecting disparate systems and data" that hinders successful IT utilization. We regularly hold free trial versions and hands-on sessions where you can try it out for free, so we hope you will give it a try.

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I want to automate data integration with SaaS, but I want to confirm the feasibility of doing so.
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