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Visualization and Analysis pipeline

"Visualization / analysis / utilization of business situation" is a solution that provides an environment that can be referred to in real time after shaping / summarizing various logs generated in business into the shape that the customer wants to see. The data that can be referenced provides not only the current data, but also a view that can predict the past and future.

Data Analytics

Why data analysis doesn't work



What can be solved


It takes time and effort due to factors such as "the means of collecting data has not been decided" and "the request for data extraction is troublesome".

​Refresh data

Due to the time and effort required for preparation, the freshness of the visualized data becomes outdated.

More efficient

It is difficult to create report materials, such as collecting data in spreadsheet software and incorporating it into report materials.

Unify the indicators

Different departments and analysts have different indicators and different analysis results



In various cases as described above

Thinking about the best solution together with the customer

We will propose and provide.

In the various cases mentioned above, we will consider, propose and provide the optimum solution together with the customer.

​ Efficiency of preparation = Data analysis pipeline


Data analysis pipeline configuration


​ Strengths of the next-generation BI platform "Looker"

Image by Dave Heere



We can flexibly propose and respond regardless of the scale.

Please feel free to contact us.

It is also difficult to collect data due to data extraction requests to other departments. Also, although there is big data, unstructured / semi-structured data such as audio, video, and images cannot be handled well and left unattended.

Cause 1 : Data collection is difficult

In data analysis, the most time-consuming part is data shaping.
Manual shaping and conversion tools are different for each system, which is too time consuming and costly.

Cause 2 : It takes time to convert and format data

Due to the above causes, it takes too much time and the freshness of the data becomes old. As a result, a vicious cycle occurs in which the effectiveness of data analysis cannot be shown.

As a result, the analysis cycle becomes longer!

After all, "data collection" and "data conversion / integration" are the "main bottlenecks", which makes it impossible to perform the original purpose of "continuous data analysis", and the data analysis itself is activated. No, it's causing the situation. Even with expensive analytical tools and AI, if the data preparation is inefficient, the "means" will become the goal before you know it.

The secret to the success of "data analysis"   = "Efficiency of preparation"

The "data analysis pipeline" is a mechanism that solves data analysis problems. The "data analysis pipeline" makes the process of "data collection, conversion and storage" in the data preparation process flow, reduces the "manual work" in the data analysis, and maximizes the efficiency of the time-consuming data preparation process.

POINT 01 Seamless flow from collection to analysis
POINT 02 Manual work ・ Release from different tools for each process
POINT 03 Data preparation-Analysis iteration cycle accelerates

Building a "data analysis pipeline" is not complete once it is created.

(= It is most important to be able to continuously and repeatedly review the analysis perspective in a way that meets the rapidly changing business needs)

Appropriate "data analysis pipeline" can accelerate iterations and focus on the original purpose of "visualization / utilization of live data​"

The following is an example based on Google Cloud Platform as the pipeline platform.

The above is just an example.

We can flexibly propose and respond regardless of the configuration and scale.

Please feel free to contact us first.

Do not have a database in Looker

Since it does not have data inside Looker and connects directly to the data warehouse, it is possible to dig deep into the latest data. This will lead to cost reduction on the data management side, and the collaboration between data users and managers (= DataOps) will accelerate.

Embedded analytics / collaboration

Looker can be embedded in various applications by API linkage. It can also be viewed on applications such as Salesforce and Slack that are used on a daily basis, reducing the trouble of opening the dashboard. In addition to Slack, it can also be linked with Box, Dropbox, Google Drive, etc.

Language "LookML" on Looker

"LookML", which is a language on Looker, is a unique language that allows you to use functions that can flexibly withstand business requirements while retaining the goodness of SQL. LookML is used to abstract low-level concerns and allow users to focus on their analysis. It is reusable and can be managed with Git.

Unification of "index" and "analysis quality"

All indicators are defined and centrally managed by Looker in "LookML". As a result, it is possible to avoid the problem that the definition of the index is different for each department in the organization, and the "quality" of the analysis is unified. An "object-oriented" -like configuration is possible in which a common index is defined and inherited in the LookML manifest.

Multi-cloud data platform

The data warehouse to connect to is not limited, and it is possible to connect to any SaaS service. (Amazon Redshift, Snowflake, Google BigQuery, Postgres, MySQL, AzureSQL, Oracle, DB2, SAP ...)

Browsing status / usage status can be tracked

I tried to make it as requested, but it is possible to grasp the report that no one actually sees. Usage tracking helps to close the gap with users.

Looker is more than just a BI tool, it's a data-driven "platform."
Looker is a tool for every employee to leverage data, rather than for analysts, and it's a different way of thinking about data and data operations.

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