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ETL

  • Glossary

「ETL」

This glossary explains various keywords that will help you understand the mindset necessary for data utilization and successful DX.
This time, we will explain the "connecting" technology, which is often an important element in realizing data utilization, and one type of it, "ETL," and consider what you should keep in mind to successfully utilize data.

What is ETL?

ETL is an abbreviation for Extract, Transform, and Load. "ETL" is an abbreviation for the process of extracting data from various data sources such as IT systems and the cloud, transforming it as necessary, and then loading it into other systems. The software tools that make this possible are called "ETL tools" or "ETL" in general.
When it comes to utilizing data, the focus tends to be on data analysis and visualization tools, and people tend to only think about the need for a data infrastructure. However, in reality, a lot of work is required for "data integration" at the data collection stage, and ETL is a way and concept to eliminate that hassle.

If you decide to "use data," you may find yourself in a difficult situation.

Recently, an increasing number of companies are working to utilize data in their business. However, it is not uncommon for these efforts to fail to produce satisfactory results.

When you think of "working on data utilization," what kind of things do you imagine? Perhaps you imagine data scientists performing complex analyses, or internal team members learning how to use BI tools to visualize data from various angles, and then saying something like, "President, here's an opportunity!" to achieve business results.

However, in reality, data utilization is not like that. In real-life data utilization, the majority of work time is often spent not on the analysis itself, but on a large amount of data-related, tedious work, and doing such hard work is the true reality of "working on data utilization."

So, let's think for a moment about what it actually means to "engage in utilizing data."

1. Consider using data

Given the current trends, let's say your company has decided to start using data. Up until now, you've only created analytical reports in Excel for monthly reports, but nothing more. It's now company policy that you need to use data more properly, and you've decided to start using data.

2. Research and implement "BI tools" and "machine learning tools"

So you've decided to tackle this, but what should you do? Perhaps you'll look into data analysis tools and machine learning tools, or research other companies' case studies.

As a result, it is common for companies to decide to utilize data by introducing BI tools that can aggregate and analyze data along various analytical axes and visualize it in an easy-to-understand manner.

3. Realize that you need data to perform analysis using a BI tool

Let's say you introduce a BI tool that is easy to use even for people with low skills. It certainly allows you to perform various analyses. However, you then realize that ​if you don't have the data to analyze in the first place, you can't perform the analysis and you won't get any results.

They realized that in order to advance data utilization, they first needed to establish a data infrastructure (they gained a better understanding of what was more important). They therefore decided to introduce a data infrastructure such as a DWH (data warehouse) and a data lake, which are databases that store data for analysis.

⇒ DWH｜Glossary
⇒ Data Lake | Glossary

4. Once the necessary data is stored in a DWH (data warehouse), analysis should be possible, but the amount of work becomes too overwhelming and never ends.

By introducing DWH, we have established a data infrastructure within the company. By inputting data into the DWH, we can perform data analysis using BI tools and turn the findings into easy-to-read reports.

"All that's left is to input the data," I thought. However, this task never seemed to end. Data was scattered throughout the company and needed to be collected, and the data formats were all different. It couldn't be used for analysis as it was, so it had to be converted, which was time-consuming. Moreover, every time new data was generated within the company, it had to be collected and input. Furthermore, data was generated, updated, and deleted every day.

Examples of data conversion work:

  • Date data formats vary (Japanese and Western calendars, hyphen-separated and slash-separated dates, etc.)
  • Mixed full-width and half-width characters, mixed Japanese character codes, EBCDIC Japanese data
  • As is, amount in millions of yen, comma separated, in dollars, in yen
  • Address data with inconsistent notation rules
  • Aggregation based on different criteria, such as annual and fiscal year aggregate figures
  • The format of Excel files varies depending on the creator, and a mysterious macro is deciphered.
  • Discover what data is stored in which locations, machines, and clouds within the company
  • Ensuring access and permissions to where data is stored
  • Data that is unclear whether it is personal information that can be handled
  • There are only paper documents, or scanned images of paper documents
  • Customer numbers and product codes vary from branch to branch, and cannot be deciphered without inquiring.

When people hear about working on data utilization, they often imagine the work of analyzing the data itself, but in reality, the time spent on the analysis itself is only a part of the total time it takes to utilize data.The reality of data utilization is that the majority of the actual work involved in data utilization is the difficult task of finding and collecting the necessary data, processing and preprocessing the data, which comes in a variety of file and data formats, organizing and storing the data in a DWH, and preparing the data before starting the data analysis itself.

Moreover, this type of difficult work is not something that can be completed in one go. If you want to analyze data from a new perspective, you may need to start over from preparing the data, and since the collected data will not be up-to-date, you may need to collect it again.

"ETL" is a means to solve "thorny problems" in data utilization

You may be thinking, "Yes, it's necessary to collect data, but is it really worth introducing dedicated tools?" Unfortunately, in many cases, the process of collecting data and preparing it for analysis takes so much time (it's said that this accounts for 80% or even 90% of the overall effort required for data utilization) that it becomes difficult to tell whether you're analyzing the data or collecting it.

It is inherently true that preparing data takes more time than analyzing it. So, it can't be helped. However, even so, if possible, you should try to reduce the amount of work time you can and use it for analysis work instead of preparation.

This is why specialized tools were developed to efficiently retrieve data from a wide variety of data sources and perform the necessary data conversion processes. ETL tools make the process of "collecting and processing data," which is actually necessary before actually using the data, very efficient.

The task of "collecting and processing data" that most people have experienced

If you're reading this, you've probably experienced common situations like "generating a monthly report" where ETL would make the process painless.

I'm sure we've all done the time-consuming task of extracting and transcribing data from various sources, then pasting it into Excel for tabulation. At that time, you may have had to do preliminary work to align the data formats (such as converting full-width and half-width characters, or if the data formats are not consistent). Then you have to paste the analysis results into PowerPoint, and finally create a report. This time-consuming process is quite common.

I had never questioned the process of monthly data collection, but when I think about it, it's not a very productive task. All I do is collect data, convert it, and paste it, and I'm not spending time on the actual work of analyzing and thinking, which is not desirable. ETL and other "connecting" technologies can reduce the amount of work that was previously thought of as "natural work."

ETL is now in full swing: as a means of utilizing the cloud and achieving digital transformation

The importance of establishing "connection" methods such as ETL is not limited to creating the data analysis infrastructure we have discussed so far. data integration is often a key factor in various aspects of cloud utilization efforts, as well as in business automation and DX, which have become popular topics recently.

As a means to successfully utilize the cloud and multiply the results of implementation

Let's say you want to introduce and utilize a new cloud service. There are many benefits to introducing the cloud. It eliminates the need to spend time and money developing your own IT system, and it can be introduced quickly and at low cost. That's certainly true if you're just going to introduce it, but introduction is not the goal. You have to utilize it, put it to good use, and produce results.

When you first introduce a cloud service, it will have no data stored on it. To utilize it, you need to prepare and store the necessary data. The necessary data is likely scattered throughout the company in various formats, so you need to gather the necessary data and connect it to the cloud to enable the cloud service to function effectively.

Even after successful implementation, data problems can still occur. For example, suppose you start using an email send service and receive a request from within the company to send emails to people who attended a seminar. The participant list is stored on a different cloud service used for seminar registration, and the data format is different. What's more, the seminar is held every week. Without a way to automatically data integration, you would have to go through the trouble of inputting and outputting data every week.

Moreover, these problems tend to occur more the more the cloud is used after it has been introduced, and problems continue to arise even if another cloud service is introduced after the introduction of the cloud (for example, introducing Salesforce after introducing kintone). If the more you use the cloud, the more manual work that hinders its use increases, and you will not be able to fully utilize the potential of the cloud.

A means to solve the problem of linking "old and new IT" with IT other than "new IT such as cloud"

One of the problems that often arises when adopting cloud computing is what to do with the "IT that was used before."Typically, this is the issue of what to do with business systems such as old mainframes, or business processes that have been handled using Excel.

While some companies take the easy approach of moving to the cloud and eliminating old IT all at once, the reality is that replacing old IT is not easy. Furthermore, if you try to utilize the cloud only after creating a plan that meets these prerequisites, you may not even be able to get started.

In reality, this often results in endless manual data transfers between legacy IT and the newly implemented cloud. This is not a desirable situation. In many cases, it is not realistic to completely eliminate legacy IT, so the real solution to the problem is to find a way to automatically share data.

An excellent means of achieving "business automation" and "business efficiency"

Many companies are working on "business automation" to promote the use of IT in their companies. However, for example, when they try to automate business processes using RPA, they often fail to achieve good results, for example, when they initially seem to work well but then quickly become unstable.

Essentially, business automation involves data integration processes such as input, output, and processing of data. Furthermore, many data integration tool can not only read and write data, but also call the functions of the integrated system.

By utilizing data integration tool, you can achieve good results in business automation, as they operate stably and can process large amounts of data quickly.

"Connecting" technology that enables efficient and effective data utilization

Now that we understand that data integration can solve a variety of problems and that many benefits can be expected from both data utilization and new IT utilization such as cloud utilization, the next question is "how to make this happen" and "can your company use it effectively?"

ETL or automatic data integration itself can be achieved in a variety of ways. It can be built in using regular programming, or you can simply integrate the data using simple tools. However, serious programming efforts take too much time and cost to achieve, and there are limits to what tools can do, so many people think there is no good way.

There is a way to efficiently develop such data integration needs using only a GUI."EAI", "ETL", "iPaaS These are technologies that connect data, such as DataSpider and HULFT Square. By utilizing this, automatic data linkage processing can be achieved smoothly and efficiently.

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, if development can be done using only a GUI, it becomes possible for on-site personnel to proactively work on cloud integration themselves.

The people who understand the business best are the people on the front lines. They can rapidly develop the necessary data utilization, cloud utilization, and business automation themselves, which is an advantage over a situation where they have to explain things to engineers and ask for help every time something needs to be done.

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 execute a full-scale process it couldn't process and crashed," or "it didn't have the high reliability or stable operating capacity to support business operations, 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 the proven track record and full-scale capabilities for professional use.

What is necessary for a "data infrastructure" to successfully utilize data?

Of course, the ability to connect to a wide variety of data sources is necessary, and high processing power is also required as large amounts of data may need to be processed.However, trial and error is often essential when utilizing data, so it is also necessary to be able to create or recreate data integration flexibly and quickly at the on-site level.

Generally speaking, if you want high performance and advanced processing, the tool will tend to be difficult to program and use, while if you want ease of use in the field, the tool will tend to be easy to use but have low processing power and can only perform simple processing.This dilemma may be seen as a trade-off where you have to accept one or the other.

In addition, they must have advanced access capabilities to a wide variety of data sources, especially legacy IT systems such as mainframes and non-modern data sources such as on-site Excel, as well as the ability to access the latest IT systems such as the cloud.

There are many methods that meet just one of these conditions, but to successfully utilize data, all of them must be met. However, there are not many methods for achieving data integration that are both usable in the field and have the high performance and reliability of a professional tool.

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)

  • BI tools
    • It is a tool that can aggregate and analyze data along various analytical axes. It is a means of analyzing data and obtaining analytical results, and has a function that allows you to report the analytical results in easy-to-read formats such as graphs.
  • DWH
    • A database for storing data to be analyzed. It has specialized performance for analysis, and is often suited to storing large amounts of data and executing analytical processing.
  • 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.
  • Cloud integration
    • Using the cloud in conjunction with external systems and other cloud services. In order to successfully introduce and utilize cloud services, achieving cloud integration is often as important as introducing and utilizing the cloud itself.
  • Excel Link
    • Excel is an essential tool in the use of IT in the real world. By effectively linking Excel with external IT, you can make the most of Excel's strengths while smoothly promoting IT use.

DataSpider trial version and free online seminar

"DataSpider," data integration tool developed and sold by our company, also has ETL functions and is data integration tool with a proven track record.

Unlike regular programming, development can be done using only a GUI (no-code), without writing code, and it offers high development productivity, full-scale performance that can serve as the foundation for business (professional use), and ease of use that can be used by business personnel (even non-programmers can use it).It can smoothly solve the problem of "connecting disparate systems and data" that hinders the success of various IT utilization such as data utilization and cloud utilization.

We offer a free trial version and hold online seminars where you can try out the software for free, so we hope you will give it a try.

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