Analyzing big data optimizes decision-making in organizations.

Extracting information from data is key to succeed in highly competitive industries.


In every company an immense amount of data is produced. It is an advantage for organizations to become data-driven and make decisions based on extracted information from their data. Data Analytics is the process of finding trends and answering the existing questions in business to increase performance. It uses the gathered data to uncover actionable insights.
For example, a chocolate company that wanted to understand which Cacao Beans were highly rated in the past few years and if there are changes in ratings over time. Analysis of company’s data showed the Average Rate Trend of five different Cacao Beans that were used from 2006 until 2017.

Of course the chocolate company had much more data that could be analyzed. So, a dashboard with visualizations was created for them.

This dashboard allowed the company to track the best rated Cacao Beans, their origin, chocolate companies that use them, etc.


Before starting the analysis, data needs to be cleaned, integrated and structured. At cimt we have many certified data engineers who can implement necessary steps on data and prepare it for final analysis.

Do you have the ambition to make data-driven decisions and don’t know where to start? cimt has the expertise to offer you the whole trajectory of data cataloging, collecting, cleansing, analyzing and visualizing data in dashboards allowing you to quickly answer business questions.


Data Science 

Data science is used for solving complicated matters and it is an important element in data-driven decisions. Data science discovers new questions that you might not have realized you need to answer to. It focuses on developing statistical algorithms and predictive models through AI and Machine Learning to solve analytically complex problems.

Our experienced data analysts and data scientists at cimt can help your organization by extracting insights from raw data. At cimt, data visualization is done with advanced tools such as Tableau and Looker.


Looker & Data Modeling

Data models consist of entities (aka data tables) that we want to track information about. Data modeling is the process of defining relationship between these entities.
For example, if we want to track a shop’s popular items we can model our data by having three entities as customer, order and product. Then we define the relationship between them as below:

Now it is possible to track the product that appears in the orders of most customers.

At cimt we believe that Looker is a strong data modeling tool. Looker allows us to create a single source of truth for organizations. LookML is a language for describing dimensions, aggregates, calculations, and data relationships in a SQL database. Looker uses a model written in LookML to construct SQL queries against a particular database.

Looker is more than a data modeling and a data visualization tool. Some other features of this modern platform are explained as follows.


Scheduling & Gathering Feedbacks

If an organization makes data-driven decisions it is critical to often update the database and data visualizations based on the latest information. As a result employees have to be informed about the latest status of the company’s performance. Therefore, creating automated schedules to send out dashboards via emails, messages in Slack, Teams, etc. has a great importance.

Looker is a cloud-based software that is able to keep the visualizations updated and automatically inform employees about new insights.

For example, assume that a brand’s sales is captured in a database and this brand has multiple branches in various locations. Once the sales database is updated, dashboards are refreshed automatically and through a created schedule the latest sales’ dashboards will be sent to branch managers. In addition, we can customize this schedule in a way that each manager would be able to view only the insights of their own branch. Moreover, feedbacks could be gathered from branch managers about the current performance of their sales. These feedbacks would be stored in a database and could be visualized as well.

We would be so glad to help you not only with data modeling and data visualization, but creating scheduled announcements and gathering feedbacks about the new data changes with the use of Looker.


Version Control

Version control is the practice of tracking and managing changes to code. If a mistake is made, developers can turn back their clock and choose an earlier version of their code. Another major benefit of Version Controlling is the ease of collaborating with other developers. A developer can develop and test without affecting other users while working in an isolated version of a file repository.

Looker has GIT version control built-in. This allows your data analysts to work simultaneously on the same projects without the risk of loosing changes.



API is an acronym for  Application Programming Interface  that software uses to access data and enable communications between computer programs. APIs are an important part of looker.

You can use the secure “RESTful” APIs for managing your Looker instance and fetching data through the Looker data platform for other uses. With the Looker API, you can write applications or automation scripts to provision new Looker user accounts, run queries, schedule reports, etc. Just about anything you can do in the Looker application you can do via the Looker API.

This is a great feature for users that have development skills with programming languages ​​such as Python, Kotlin, C#, etc. Below is an example of a Looker API used in combination with Python to create a schedule for sharing a data-graph.


As you noticed, Looker is a powerful versatile tool with a long list of amazing features that we cannot cover all in here.

Feel free to contact us for more information and receive a Demo on your data.

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