Self-service BI (IT self-service)

Glossary

"Self-service BI (IT self-service)"

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 trend towards "self-service IT," which will be a key factor in the future use of IT, and through that, we will think about the essence of future IT use.

What is self-service BI (IT self-service)?

Self-service business intelligence (BI) refers to efforts to enable end users to perform data analysis and reporting tasks on their own.
When utilizing IT, not just data analysis, there is a tendency to ask or inquire about experts, but instead, there is a growing trend towards "IT self-service," which allows users to master IT on their own.

The problem was that analysis was something that had to be outsourced.

Old-fashioned "data utilization"

The importance of utilizing data in business has become widely recognized in recent years, but it has not been long since the practice of using data analysis materials in management meetings for discussions.When using the results of data analysis for discussions, there are times when additional analysis results are required, such as a report that analyzes the data from a different angle.

For example, even if you have analytical data on product sales, you will often need additional analytical results, such as how it compares for each product, or whether sales are different in Western and Eastern Japan. The more creatively you use data, the greater this need will become, but since it is not possible to perform every analysis in advance, you will inevitably need analytical results that are not included in the documents you have prepared in advance.

In the past, when this situation arose, the discussion would likely be postponed until the next meeting, with the explanation, "We'll bring the analysis materials to the next meeting." If further concerns were discovered in the next meeting materials, the discussion would be repeated again to analyze them. This would mean that several months would pass in monthly meetings, and even a month would pass in weekly meetings.

What we need to be aware of is that rather than a situation where an analysis report is published in a predictable manner and no particular opinions are expressed, concerns or unclear points arise, discussions arise, and additional analysis becomes necessary. This is a situation where data is being used appropriately, and in fact, this is what happens when data is used successfully.

If data utilization could be carried out smoothly,

What if we could perform additional analysis "on the spot"? When a person in charge of Western Japan commented that the shape of the graph didn't seem intuitive, we investigated and found that sales were indeed different in Western and Eastern Japan. Further investigation revealed that only Product A was selling very well in a specific region. We immediately asked the person in charge of Product A on Slack if they had any idea what this meant, and they replied, revealing that they had been accepting online orders on a trial basis. We decided that we should immediately try rolling out online ordering nationwide.

It's clear which organization is stronger: one that takes several months to get to this point, or one that can reach a conclusion on the spot by the end of the meeting. Therefore, companies are moving away from the traditional "time-consuming analytical work" approach and toward building a data utilization platform that can quickly analyze data.

The above is an issue of "time (speed)" when it comes to utilizing data, but in order to make data utilization smoother, it is easier to get results if the "trouble of asking other people" is also eliminated. This is because the process of summarizing what you want an analyst to do, requesting an analysis, and receiving the results is itself difficult and time-consuming, which can make people hesitant to do it.

For example, if a person in charge of Western Japan thinks, "Maybe," can they ask for additional analysis in the middle of a meeting? We've heard stories of situations where a data analysis platform has been introduced, but it's difficult to keep asking busy people for small things over and over again, making it difficult to move forward with data analysis. Being able to analyze things on their own allows for greater speed, making it possible to "immediately analyze anything that comes to mind."

Also, sometimes it's important to take note of things you wouldn't notice unless you analyzed the data yourself, or things that are difficult to put into words. For example, when a person in charge is creating a regular report, they may notice that "I'm not sure why, but the data seems different from usual," and by investigating what "seems suspicious," they may discover something unexpected.

Also, when the analyst is someone else, tacit or taken-for-granted knowledge may not be shared, leading to poor data analysis. For example, even after diligent analysis, mistakes that could have been avoided if the person involved had analyzed the data themselves can often be made, such as, "Everyone on-site knows that without being told," or "That conclusion is impossible to implement, so there's nothing we can do."

Therefore, in order to successfully utilize data, it is important not only to utilize advanced technology and have highly skilled data analysts, but also to createan environment where each person in charge can analyze data themselves, even if it is just basic data analysis, in other words, an environment where ``self-serviceBI'' is possible.

The advancement of "IT self-service"

This type of initiative to "enable people to use IT on their own" is attracting attention and is being promoted not only in the field of data analysis.

The increasing popularity of in-house development and no-code tools stems from the idea that users themselves know best what IT systems are truly needed in their workplaces, so it's better for them to develop them themselves. These also present the same challenges and concepts as data analysis.

In addition, efforts are underway to make product user support self-service, allowing customers to handle tasks that were previously handled manually (such as setting parameters on installed equipment) themselves.

Instead of calling a call center when customers don't understand something, you can allow them to search FAQs on the website or ask a chatbot to solve the problem themselves, which can often resolve their questions more quickly and accurately.You can also equip the product itself with advanced user assistance features, allowing customers to configure various settings and resolve problems themselves.

Such efforts not only improve convenience for users, but also reduce labor and costs because end users are able to solve problems themselves. Complete self-service will dramatically increase the efficiency of user support. For example, it will become possible to realize a highly efficient business where only 10 people are responsible for providing service to 1 million people.

Self-service practices

So how do you go about making IT self-service?

Self-service BI

First, we need to be willing to do data analysis ourselves. We need to change the (tacit) idea that data analysis is something that should naturally be outsourced to someone else and not something that we do, and understand the significance of doing the analysis ourselves.

However, it is not realistic to expect everyone to write data analysis code in Python, so it is necessary to provide data analysis tools that anyone can use. This could involve providing a BI tool that is designed for self-service use and emphasizes ease of use, or having everyone work on basic data analysis tasks in Excel.

It is also important to note that "analysis is impossible without the necessary data." Even if a company implements BI tools, the results may not be as good as they should be. This can be because the data is not organized properly, or the data is there but in a format that is not useful for the needs of the field.

Furthermore, just like with analytical work, the type of data that needs to be prepared and in what form may only be known once the analysis is needed, or by the people involved themselves. In other words, it is necessary not only to make data analysis work self-service, but also to consider making the data used for analysis self-service.

Utilizing self-service IT

When using cloud services to utilize IT on your own, it is likely that cloud services, which are managed services that require no operation and are easy for anyone to use, are the preferred method. This is because it is often difficult to develop something in-house from scratch. For example, instead of operating a database server in-house, companies may use kintone on-site.

However, when engaging in such cloud utilization, you will need to master the functions of cloud services that were not designed for your own use or purpose, and this may require adjustments and customization. Also, since it is rare for a single cloud service to meet all of your needs, it may be necessary to combine and utilize multiple cloud services.

In addition, unless these initiatives are something that can be done by the employees themselves, it will be difficult to create a system in which the employees themselves can utilize IT.

Implementing self-service with "connecting" technology

Even if you try to in-houseIT or utilize data across the company, you may encounter problems such as those mentioned above, where things don't go smoothly unless the peripheral areas are made self-service.

In the case of BI, data preparation is required, and in-house IT requires a means to effectively combine and utilize cloud services.No-code "connecting" technology is useful for meeting such needs.

To prepare data for analysis

With "connecting" technology, it is possible to link a wide variety of systems and data simply by operating on a GUI, and it is also possible to perform data reading, writing, and data processing.

By linking with a wide variety of systems and data, we can achieve the processes of retrieving data from data sources, storing data in databases, data lakes, DWHs, etc., processing and maintaining the stored data, and even automatic processing that links analysis results to business actions.

To master cloud services

If you use "connecting" technology, you will probably be able to connect to the cloud service just by operating it on a GUI. Missing functions can be made up for by creating automated processing by calling processes from outside, or by inputting and processing data from outside.

Even when combining multiple clouds, you can develop your own system that spans different clouds by using technology that "connects" those cloud services, linking cloud functions as needed, and linking data between clouds.

For example, you could be notified via Slack when new data has been added to Salesforce, or you could automatically link data on kintone with data in Salesforce to combine the best of both systems.

"Connecting" technology

It's common for people to think they can master IT themselves, but then run into troublesome and difficult details. However, if you think carefully about what needs to be done, you'll often find that it's self-service, including the behind-the-scenes processes that support the use of IT, such as "connecting" various systems and data.

By utilizing software products such as "EAI" or "ETL" or cloud services such as "iPaaS," you can connect and utilize a wide variety of data and systems, from cloud to on-premise, simply by placing connection icons on a GUI and configuring various settings. Of course, it can be used effectively as a no-code, self-service IT solution, and is also fully suitable for professional use.

Please try out the "connecting" technology, a means to successfully implement IT self-service.

Related keywords (for further understanding)

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 around, from on-premise to cloud. This is a means to carry out such processing efficiently.
iPaaS
- A cloud service that "connects" various clouds with external systems and data simply by operating on a GUI.
SaaS
- When people generally think of the "cloud," they are referring to an initiative to provide software usage as a service.

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 is hindering successful IT utilization. We offer a free trial version and online seminars where you can try it out for free, so we hope you will give it a try.

Free trial seminar here

Why not try a PoC to see if HULFT Squarecan transform your business?

Why not try verifying how "connecting" can be utilized in your business, the feasibility of solving problems using data integration, and the benefits that can be obtained?

I want to automate data integration with SaaS, but I want to confirm the feasibility of doing so.
We want to move forward with data utilization, but we have issues with system integration
I want to consider data integration platform to achieve DX.

PoC Program | HULFT Square

Self-service BI (IT self-service)