Introduction

Hi. I'm Nishiguchi, the manager of the KINTO Technologies Analysis Group. Some of you might think it's strange that a tech company has an analysis group. So, that's what I'd like to talk about today.

The Analysis Group's role

KINTO Technologies' business includes the following: information processing services such as designing, developing, and operating sales and information systems in the digital field; and work related to creating and proposing plans for and providing consulting services for corporate management and marketing strategies. The Analysis Group handles the latter half of this, and of course, KINTO's business in Japan makes use of data from, cooperates with, and participates in the various services run by Toyota Financial Services Corporation group companies, and aims to use data in order to give business value.

Giving data value (The scope of the Analysis Group)

Are you familiar with the DIKW pyramid? This concept, which stands for data, information, knowledge, and wisdom, *revolves around the idea of harnessing and processing vast amounts of information. At our core, we're committed to figuring out how to create value in business while transforming huge volumes of information into new systems. In order to make the DIKW pyramid a reality, we broadly divide our work into 4 areas.

1. Designing and carrying out data acquisition

Business data typically consists of the following: transaction data generated from actions such as user order flows; and master data from things like product management. We also view both transaction and master data as having to be generated by something. It's been getting increasingly difficult to acquire data in recent years, notably due to calls to protect personal information. There are data that can only be acquired at certain times (user registration data, for example), and if you fail to get them at those times, you won't be able to later on. We of course set up the right analysis tags on websites, and also work on planning systems to generate data via user actions.

2. Data storage

We need to be able to store the resulting data in a data lake or data warehouse quickly and accurately before moving on to the next step (analyzing it). The goal here is to make the analysts' job easier. We'd like to lower the workload that data acquisition and processing impose on analysts, so they can focus on the analysis work more. Not being able to find the data they're looking for easily can get interfere with their train of thought. It's important to create a system that, for example, will make it easy for team A to join forces with team B when they want to. I often tell our team members to put themselves in the receivers' shoes before they pass something on to them.

3. Data analysis

Analyzing the data consists of splitting them up based on certain perspectives, and comparing them with other kinds. I think the idea is to find differences by doing so. I also see it as a policy of ours to promote widening or narrowing those differences. It's our job to find those differences from right perspectives, inform business team members about them, and also suggest appropriate actions to take. I tell our members that there are 2 skills they need. The first is to have various perspectives. Specifically, we get them to develop the ability to see things as insects, birds, fish, and bats do respectively. The other is performing "why-why analysis." In order to grasp the essence of things, we have to repeatedly ask why, and in so doing, find the root causes among the correct causal relationships.

4. AI and machine learning

AI and machine learning are both a part of advanced data analysis. This is the domain of predicting the future using past data. In this field, we work with the Toyota Motor Mirai Creation Fund to create models using advanced algorithms while also gathering academic information. We're also constantly reviewing models for AI and machine learning in business fields. It's also essential that an MLOps environment be created that will make it easier to modify and implement these models, so we're also working on this together with the Platform Group

Giving value to data (Roles and Responsibilities)

To reiterate, acquiring data at the right time is critical to giving data value, especially in BtoC business. This is because some kinds of data can only be acquired at certain times. The next-most important point is to store the data so it's easy for people performing the next processes to use. To accomplish this, the Analysis Group has the following team member positions:

1. Data analysts

Data analysts at KINTO Technologies are more than just analysts. This is because they're involved right from the data acquisition design stage, where the aim is to acquire data in the right places, at the right times, and in the right forms via websites and apps. They're also responsible for setting up the tags. We believe data analysts should think in advance about what kinds of data the analysis is likely to require, then acquire it without missing any out. The resulting data is then stored in a data lake or data warehouse. From there, the data can be freely extracted using BI tools and SQL, and predictors found, countermeasures proposed, and so on by finding the root causes for things while drilling down from the right perspectives.

2. Data engineers

We also need highly skilled data engineers. We don't just store data from the backend database in a data warehouse as is, but also create data in ways that also aim to make things easier for members involved in data analysis. (I think this a unique approach only operating companies take.) KINTO is steadily launching new services, and our data engineers are also responsible for creating the development guidelines, common functions, CI/CD systems, and so on needed to develop things efficiently so that data analysis can start right from when the services are launched.

3. BI engineers

It's crucial to turn data into information and quickly inform business members about the business situation. Also, managers and staff have different preferences when it comes to the level of detail they prefer in the information they access. A BI engineer's job is to develop, modify, and maintain dashboards for conveying it in the appropriate forms.

4. Data scientists

A variety of services are going to be rolled out by KINTO, likely including more and more related to AI and machine learning. A wide range of support is going to be required for things like images as well as numerical data. No matter how accurate a model you construct, there's no guarantee that it'll actually get used in business. First, you need to convince the marketers, etc. that it's worth it. To that end, KINTO Technologies' data scientists need to be able to explain things using conventional statistical methods, etc., understand the businesses, and be interested in consumers. In other words, data scientists in KINTO's business play the role of marketing data scientists.

5. Analysis producers

This is a new position that was created in September 2022. The analysis producers' job is to cross-functionally coordinate the roles of each of the positions 1 to 4 above. They require the business abilities needed to get people to tell them what business issues they're facing, then replace those issues with appropriate analytical problems. Of course, they also need to be highly experienced and knowledgeable about the data involved in 1 to 4. They'll also play an extremely important role in enabling the KINTO Technologies Analysis Group to make its presence felt even more in the future.

Team dynamics and workflow approaches in the Analysis Group

1. Team members and atmosphere

In the Analysis Group, we confront very difficult challenges daily, with each of our members bringing a unique set of experiences to the table. However, what we all have in common is our high level of motivation and curiosity toward further broadening our understanding of data-related domains, while staying true to each core roles as data analysts, data engineers, data scientists, etc. Whenever a new team member joins, we always take the time to introduce ourselves, as each and every one of us has extremely unique hobbies and things we're into.

2. How we work

The Analysis Group is split between 3 locations: Tokyo, Nagoya, and Osaka. Each has teams consisting of data analysts, data engineers, BI engineers, data scientists, and analysis producers, and all the teams go about their work while deftly using online and offline approaches to freely consult with each other.

Future challenges we want to tackle

We'd like to take on the challenge of connecting and analyzing users across multiple KINTO services we're involved in, and other services besides. In order to do that, the functions and roles of the Analysis Group will need to be linked together organically. It's going to be very challenging, but we hope it'll give us an even deeper understanding of users.

KINTO aims to be a top runner in the mobility platform world, and can use GPS and other mobility data to understand the "where" and financial data to understand the "how much." By recording the "who" and "when" in logs of these, we want to gain a deeper understanding of users' preferences and lifestyles. Then, the challenge will be to create systems, predictive models, and so on that will enable us to anticipate their behavior.

In conclusion

In the Analysis Group, we aim to stay updated with the latest information, and keep working diligently every day, so that we can make our presence felt by contributing to the development of new KINTO services and supporting Toyota Financial Services Corporation.