“Digital native” companies have no shortage of data. This data is often spread across different platforms and Software-as-a-Service (SaaS) tools. As more data is collected about the business, it becomes even more important to democratize access to that information. While many tools offer in-app statistics and visualizations, centralizing data sources for cross-platform analytics allows everyone in the organization to get an accurate picture of the entire business. With Firebase, BigQuery, and Looker, digital platforms can easily integrate dispersed data sources and integrate data into operational workflows — resulting in better product development and increased customer satisfaction.
How does Google Cloud Analytic Platform work?
In this architecture, BigQuery regularly takes data from various sources, becoming the only source of truth for analytics. Here we can take advantage of the vast Google ecosystem to retrieve data directly from Firebase Crashlytics, Google Analytics, Cloud FireStore and query data within Google Sheets. Furthermore, with data integration tools such as FiveTran, third-party datasets can be easily pushed to BigQuery.
In Looker, data analysts can use pre-built dashboards and data models, or LOOKML, through source-specific Looker Blocks. By combining these accelerators with custom, first-party LookML models, analysts can merge data sources for more meaningful analytics. By using Looker Actions, data consumers can leverage insights to automate workflows and improve overall application health.
Cross-Functional Analytics
When various data sources are centralized in BigQuery, members from different teams can use the data to make informed decisions. Managers may want to combine business goals in a Google Sheet with CRM data and understand how the organization is progressing toward revenue goals. When preparing for board or team meetings, business leaders can use Looker's integrations with Google Workspace, submit query results to Google Sheets, and create a chart within a Google Slide deck.
Technical program managers and site reliability engineers may want to combine Crashlytics, CRM, and customer support data to prioritize errors in the application that affect the highest value customers or are frequently raised within support tickets. Not only can these users easily connect to the Crashlytics console for a deeper review of the error, but they can also use Looker's JIRA action to automatically generate JIRA issues based on thresholds across multiple data sources.
Account and customer success managers (CSMs) can use a centralized dashboard to track the health of their customers using inputs such as usage trends, customer satisfaction scores, and crash reports in the app. With Looker alerts, CSMs can be immediately aware of problems with an account and proactively contact customer communications.
You can contact us to position Google Cloud products in your company and get detailed information about our business intelligence tool Looker.
İlginizi Çekebilecek Diğer İçeriklerimiz
Veri analisti (Data Analyst), verileri toplayan, analiz eden ve bu verilerden anlamlı içgörüler çıkararak işletmelere stratejik kararlar almalarında yardımcı olan bir profesyoneldir.
Makine Öğrenimi Mühendisi (Machine Learning Engineer), veri analizi ve yapay zeka algoritmalarıyla çalışan, makinelerin öğrenmesini ve veri odaklı kararlar almasını sağlayan sistemleri geliştiren bir profesyoneldir. Bu mühendisler, istatistik, programlama ve veri bilimi becerilerini kullanarak, iş süreçlerini otomatikleştiren ve optimize eden çözümler oluşturur.
