If you’re here, it’s because you are a savvy marketer who’s doing their homework to figure out if BigQuery is a good fit for you. It's a great solution if you are aiming to gain deeper insights and provide more value for your team. But it doesn't mean you must invest a lot of time becoming proficient at it. There are some intermediary solutions offered for those who simply wish to get more data to analyze seasonality for example. Before you spend dozens of hours trying to become a BigQuery pro, let’s list the skills and knowledge you need to leverage it effectively.
Key Skills for Marketers Using BigQuery
SQL Proficiency
SQL is crucial for extracting, manipulating, and analyzing data with precision. It's a transferable skill that allows marketers to handle large datasets efficiently. For those new to SQL, resources like Robin Lord's SQL learning game is an awesome starting point. Disclaimer: I may be biased because I love anything octopus-related.
Free game to learn SQL: Lost at SQL
Understanding Data Architecture
BigQuery's structure—datasets, tables, schemas—demands mastery for effective querying. Understanding data architecture is crucial for marketers who want to be efficient. Think of it as learning how the "stuff is set up" so you can build better SQL queries. Here's the TL;DR::
Datasets: These are like folders that organize your data. Each dataset contains tables, and they help you manage and control access to your data.
Tables: Within datasets, tables are where your actual data lives. They are structured in rows and columns, much like a spreadsheet, and each table is designed to store data about a particular subject.
Schemas: A schema is like a blueprint for your tables. It defines the structure of your data, specifying what type of data each column can hold (e.g., numbers, text, dates).
Taking an hour to learn this means you can retrieve and analyze your data more efficiently.
Article you should read to get Started With GSC Queries In BigQuery
Data Integration Skills
Integrating data from various sources into BigQuery is essential. Tools like OWOX BI can facilitate this process by providing connectors for seamless data import.
If you are an SEO expert, you can benefit from the free Google Search Console Bulk Data Export option and the Serpstat BigQuery connector.
Windsor.ai offers 50+ integrations to let you get most of the marketing data you could need. I recapped it in a neat table:
Purpose | Companies |
Email Marketing | ActiveCampaign, Aweber, Mailchimp, SendGrid, ConvertKit, Drip |
Advertising | Google Ads, Facebook Ads, LinkedIn Ads, Twitter Ads, Pinterest Ads, Bing Ads |
Analytics | Google Analytics |
SEO | SEMrush, Moz, Ahrefs |
CRM | HubSpot, Salesforce, Pipedrive, Microsoft Dynamics |
E-commerce | Shopify, WooCommerce, Amazon Ads, Amazon Seller |
Project Management | Asana, Monday.com, Trello |
Customer Support | Zendesk, Freshdesk, Intercom |
Data Management | Google Sheets, Airtable, GitHub |
Billing | Chargebee, QuickBooks, Freshsales |
Insights | LinkedIn Pages, Facebook Insights, Instagram Insights |
Campaign Management | Campaign Monitor, AdRoll |
Owox BI offers these integrations:
Purpose | Services |
Email Marketing | Mailchimp, Klaviyo, Customer.io, Iterable |
Advertising | Microsoft Ads (Bing Ads), Facebook Ads, Instagram Ads, Google Ads, LinkedIn Ads, X Ads (Twitter Ads), AdRoll, Criteo, Amazon Ads, Apple Search Ads, Pinterest, Snapchat, TikTok, Outbrain, Taboola, Yahoo Gemini, RTB House |
Analytics | Google Analytics 4, Amplitude, Mixpanel, PostHog |
CRM | HubSpot, Salesforce, Zoho, Microsoft Dynamics Customer Engagement, Oracle Siebel CRM, Sugar CRM, Zendesk Sunshine |
E-commerce | Shopify, WooCommerce, PrestaShop, BigCommerce, Magento, Spree Commerce, Zencart |
Project Management | Asana, Trello, Monday, Notion, Smartsheets, Confluence |
Customer Support | Zendesk Support, Zendesk Chat, Zendesk Talk, Freshdesk, Intercom, Drift, Dixa |
Data Management | Amazon Redshift, Snowflake, Microsoft SQL Server (MSSQL), MySQL, Mongo DB, Oracle DB, Azure Table Storage, CockroachDB, Db2 |
Billing | Stripe, Braintree, Chargebee, Recurly, Zuora, Paypal, Square |
Insights | SearchMetrics, Qualaroo, Delighted, Zenloop |
SEO | Semrush |
Campaign Management | Campaign Manager 360, DV 360, Pardot, Marketo, Outreach, SalesLoft, Lemlist, Persistiq |
Data Analysis and Interpretation
Analyze and interpret data. It's about metrics, KPIs, and actionable strategies. Retrieving data is nice but marketers need to analyze and interpret the data to produce actionable insights. Using BigQuery allows you to manipulate and aggregate data to create custom metrics that aren't available in the standard Google Search Console interface. Another way to use BigQuery is to join multiple data sources together. Here are some examples:
You can match and analyze data from Google Search Console with data from Google Analytics, allowing you to correlate search performance with user behavior metrics.
You can join Google Search Console with Merchant Center data to identify which products are driving traffic organically and compare their performance with those featured in Google Shopping ads.
GSC + Merchant Center allows you to identify high-performing keywords and cross-reference them with product-related search queries from Merchant Center. This can help in refining product titles and descriptions to better match user search intent.
Coding Skills (Optional)
While not mandatory, coding skills in languages like Python or R can be beneficial when it comes to automating tasks and handling complex data operations. If you are working with large datasets, you should really consider learning Python as well. These skills can enhance your ability to perform advanced analyses and automate repetitive tasks. Reminder before you freak out: it's possible to use BigQuery effectively with just SQL knowledge.
Data Visualization
Dashboards provide visual representations of complex data, making it easier to understand trends, patterns, and insights at a glance. Dashboards make data accessible to non-technical users who may not be proficient in SQL or data analysis. This democratizes data across an organization. Once you have the data you want, you need to get it out!
Data Privacy and Compliance
Understanding data privacy regulations is crucial, especially when dealing with first-party user data. Be mindful of adhering to regulations like GDPR and CCPA to protect personal data, mitigating risks of breaches, and making informed decisions about data collection and usage.
Data Privacy in BigQuery
Secure environment: BigQuery offers features like encryption, access controls, and data masking to safeguard sensitive information.
BigQuery uses Identity and Access Management (IAM) to control who can access your data. You can restrict data visibility based on user roles and data sensitivity.
BigQuery provides tools for inspecting and protecting sensitive data. You can schedule inspections to identify sensitive data and apply de-identification techniques to protect personally identifiable information (PII).
Compliance Considerations
Regulatory Compliance
Marketers must comply with data protection laws such as GDPR in the EU and CCPA in California. Ethical data handling helps avoid severe penalties for non-compliance. Read: Introduction to data governance in BigQuery
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