{"id":38780,"date":"2021-12-17T10:30:58","date_gmt":"2021-12-17T09:30:58","guid":{"rendered":"https:\/\/clicktrust.be\/blog\/geen-onderdeel-van-een-categorie\/google-bigquery-for-marketing\/"},"modified":"2023-02-22T19:15:22","modified_gmt":"2023-02-22T18:15:22","slug":"google-bigquery-for-marketing","status":"publish","type":"post","link":"https:\/\/clicktrust.be\/nl\/blog\/analytics\/google-bigquery-for-marketing\/","title":{"rendered":"Build your marketing data warehouse with Google BigQuery"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” top_padding=”50″ text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” advanced_gradient_angle=”0″ overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text]Like many marketers, I\u2019m sure that your marketing data comes from several platforms and you have to juggle with a mix of siloed data to be able to analyze and understand your customer journey. Quite annoying, right?<\/span><\/p>\n

Siloed data coming from different sources rarely communicate with each other and make the interpretation of KPIs and important metrics complicated. In the end,\u00a0 it leads to situations where you don\u2019t know how to make decisions…<\/span><\/p>\n

That\u2019s why, more than ever, you need to take control of your data and gather it in one place. Generally, the main solution is to use spreadsheets, but spreadsheets can\u2019t handle huge datasets. <\/span>This is precisely where a data warehouse like <\/span>BigQuery<\/b> becomes interesting.<\/span><\/p>\n

BigQuery & how it will meet the needs of your business<\/h2>\n

What is BigQuery?<\/h3>\n

BigQuery is a data warehouse from the Google Cloud platform that helps you to store, visualize, analyze and ingest your data. The goal is to directly upload all your data in real-time from various sources: CRM, Google Analytics, Google Sheets, Google Ads, Google Optimize, and Facebook Ads,… The objective is to centralize all your raw data in the cloud.<\/span><\/p>\n

In terms of pricing, the model is quite simple. You\u2019ll pay both storage and processing costs. Storage costs only depend on the volume of data stored. For processing, you will pay for querying data, data storage, and streaming inserts. Loading data is free of charge.\u00a0<\/span><\/p>\n

BigQuery provides free tiers for storage (10 Gb) and processing (1Tb). You only pay above those thresholds. For more information about pricing, you can check the<\/span> overview of BigQuery pricing<\/span><\/a>.<\/span><\/p>\n

Google BigQuery advantages<\/h3>\n

By being scalable and fast, Google BigQuery enables you to only focus on analyzing your data and allows large-scale analytics. As it\u2019s connected to intelligence tools such as <\/span>Data studio<\/span><\/a>, it helps you to easily visualize your data in a clear and centralized report to get clear insights about your customer journey and get predictions about your clients for example.<\/span><\/p>\n

BigQuery ML<\/span><\/a> allows you to create and execute machine learning models directly on the data warehouse. You can create regression models, K-means clusters, decision trees, or time series just by a \u201cCREATE model\u201d query.\u00a0 It saves loads of time and makes it handy to have data and models managed in the same place. <\/span><\/p>\n

BigQuery alternatives<\/h3>\n

Of course, BigQuery is not the only data warehouse. Amazon ( Amazon Redshift) or Microsoft (<\/span>Microsoft Azure Synapse Analytics) <\/span>also offer similar services.<\/span><\/p>\n

If you use BigQuery or another solution, it will be a decision based on your project: type and structure of data, purpose (machine learning ?), or endpoint (dashboarding, data platform,..). But also current technology stack: are you already using Google Cloud Platform or Google data such as GA 4. \u201cMulti-cloud\u201d (aka using multiple cloud solutions) is often possible but brings development and sometimes complications.<\/span><\/p>\n

Why use Google BigQuery?<\/h3>\n

The main question is: why should Marketers start to store their data in this cloud and how to use it?<\/span><\/p>\n