A Robust Tool for Big Data Analysis
Updated September 13, 2020

A Robust Tool for Big Data Analysis

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with Google BigQuery

Google BigQuery is being used to analyze click-stream data-set in conjunction with structured data-set. It is being used in the sales and marketing departments to essentially attribute new customer acquisition and existing case sales to specific sales representatives, sales divisions, and marketing campaigns. This attribution analysis using Google BigQuery tool has enabled my organization to measure return on investment of various sales and marketing initiatives. Such as training of sales representatives to help their customers adopt digital shopping tools, email, social, and online ordering banner campaigns to target specific customers and regions where we have distribution centers or stores and paid search ads. To accelerate the rate at which we are acquiring new customers who have never shopped before through our online food service business.
  • Google BigQuery serves as a complete big data warehouse solution to quickly access marketing and sales data in one place.
  • Google BigQuery enables analysts to pull correlated data streams by running SQL like queries, so they don't have to query multiple analytics tools.
  • Google BigQuery queries need to be optimized to avoid high costs when pulling data.
  • Google BigQuery needs knowledge of SQL coding to leverage its data analysis capabilities.
  • Google BigQuery has enabled my organization to connect the dots between online marketing campaigns and offline case purchases.
  • Google BigQuery has helped build a big data warehouse with lower cost and IT resources for smaller sales and marketing campaign requirements.
Google BigQuery bridges the gap between online or click-stream and offline transactional or customer data. So, it acts as a big data tool that enables the correlation between digital analytics such as ad clicks or impressions and business intelligence data such as invoice sales. While traditional web analytics tools such as Google or Adobe Analytics focus on collecting and analyzing online behavioral metrics, Google BigQuery focuses on so much more. Including visits, page views, or time spent on a landing page and have no insights on customer activity happening in a physical world like a brick and mortar store or distribution center. Hence, I recommended my organization to leverage Google BigQuery and get a 360-degree view of our customers.
I rated the overall support for Google BigQuery as a mediocre five because it has limited support from Google. Instead, it is heavily dependent on an organization's IT resources such as SQL analysts and Data Architects to run big data reports or maintain data quality. Additionally, if errors occur during a run of complex SQL queries or when sending data to Google BigQuery from other sources, Google provides basic email support which needs to be complemented with internal data warehouse support to fix the root cause of the database errors. Finally, due to constraints on the amount of data an analyst can query or pay the additional cost when exceeding the limit, basic Google support is not sufficient to meet data needs without interruption.

Do you think Google BigQuery delivers good value for the price?

Yes

Are you happy with Google BigQuery's feature set?

Yes

Did Google BigQuery live up to sales and marketing promises?

Yes

Did implementation of Google BigQuery go as expected?

Yes

Would you buy Google BigQuery again?

Yes

Google BigQuery works well for enterprise organizations that have sufficient IT resources to implement its integration and data governance requirements. For example, if an organization is a billion-dollar food distributor, and it wants to run quick queries against large data warehouses to pull correlated sales and marketing reports. So, it can show return on investment driven by training initiatives and marketing campaigns to lift new customer acquisition rates and incremental case purchases. Google BigQuery is less appropriate to use in small businesses where data volume is low, and IT resources are not enough to maintain data quality or run SQL queries. Example: If a company requires to report eCommerce sales from digital-only marketing campaigns where audience size is a few hundred customers, Google BigQuery may not be needed. Instead, Google Analytics or Adobe Analytics will suffice.

Google BigQuery Feature Ratings

Automatic software patching
7
Database scalability
6
Automated backups
6
Database security provisions
6
Monitoring and metrics
7
Automatic host deployment
6