Access invaluable data form the best sources through BigQuery. Effective data handling with custom options based on BigQuery implementation. Integrate all your invaluable data from numerous data sources; e-commerce, offline files, media and marketing, data destination for all your business needs. Scale from point-to-point integration up to complex workflow that can use conditional logic and can also process billions of tasks in just milliseconds. Become limitless in data handling with advanced BigQuery integration options that enhance performance in the data industry. dJAX DMP Manager offers advanced options and has access to all the features BigQuery has to offer and effectively manage modern data-driven marketing. An ideal solution for handling huge datasets in the absence of hardware and infrastructure. Numerous data sources including Data Management Platform, Google DV360, Google GA360 and other third party data can be exported to BigQuery without any hassle for better operation and visualization of obtained data.
Why export data into BigQuery?
The high-level answer is that the size, speed, and scalability of BigQuery opens a whole lot of possibilities for integration, advanced analysis, and optimization based on data on its own, and even more possibilities when exporting joinable Google Analytics 360 data to BigQuery. The specific answer for you depends on your needs. What do you need to understand customers’ behavior? Is there a specific product or discount most new acquisitions go for? Why is the retention rate higher in some locations than others? and so on.
- dJAX DMP Manager provides the perfect tool to handle the immense amount of data out there by offering the expert integration with Google BigQuery.
- BigQuery, the database analytics tool, is ideal for trawling through billions of rows of data to find the right data for each analysis.
- Its intelligent design and approach to columnar storage, it can create better aggregates and work across massive compute clusters. When paired with the right BI, it can be a powerful tool for any business.
- Businesses produce significantly larger amounts of data, it’s important to have the right tools in place to interact with it while deriving the insights more effectively and quickly.
- Simply storing it and organizing it is not enough, and it can become difficult to rapidly analyze millions of data points even in the most efficient data structures.
Integration with BigQuery
DMP Manager and other data sources in the industry handles a large quantity of data that is scalable and assists data analysts work at an increased speed and have better performance. There is no confusing infrastructure involved; the simple, familiar SQL helps analysts interpret data and make important conclusions without any hassle. Extensive capabilities in encryption and security to ensure your data can be automatically and efficiently backed up and recovered. No matter the number of users or the size of data, quickly access and analyze data.
- Instead of operating across multiple compute clusters and requiring individual management of each, BigQuery distributes its computing resources dynamically, decreasing both the time it takes to scan through data and the cost of building a system.
- Instead of being stuck with rigid structures that are designed around multiple compute clusters, can quickly distribute computing power where it’s needed most.
- BigQuery’s “serverless” build, a fully on-cloud design that prioritizes scalability and quickness in queries, means that you can easily scale and perform ad hoc analyses much faster than you would on cloud-based server structures.
- Tied to its unique serverless design, BigQuery offers a variety of options that are friendlier to small businesses and those companies that experience evolving analytics needs. The models are based on computing power and needs.
- dJAX makes it significantly easier to configure and start running new instances without having to pay the cost of full servers. The platform also promises 100% resource utilization, meaning, computing power utilization based on the need.
- For small businesses, the model delivers the benefit of being highly elastic and scalable, so can deploy the resources you need based on the task at hand without worrying about the cost of scaling back down.
Access Data on Demand
- When performing ad-hoc analysis and deriving real-time insights, using data that has been called and extracted from the warehouse can produce outdated results even in a matter of seconds. Solve this problem with cloud-native data warehousing model.
- This stems from two major tools that are designed to reduce friction: BigQuery’s batch ingests capability and its real-time ingest capacity. Load thousands of data points easily into analytics tools without having to incur a computational decrease.
- Instead of drawing from resources dedicated to analysis and management tools like SQL, batch ingest uses its own computing resources and does not impact real-time query abilities at all.
AI to Optimize Datasets
- Exceptionally useful aspects of BigQuery is its ability to optimize storage and datasets in the background. Utilizes Capacitor, a storage format, and system that uses AI to continuously evaluate data storage.
- Once it detects patterns, dJAX will use them to optimize datasets into structures that are better suited to the types of analytics and queries users are performing regularly. The operations are fully transparent and automated, so it handles these processes on its own, but it always informs users of what is happening.
- The result is a platform that makes queries faster as thge user make more queries thanks to machine learning algorithms. This also combines with BigQuery’s fully managed stack and its caching tools, which let users perform a free query if they’ve previously carried it out within the past 24 hours. All told, it reduces costs every 90 days, dropping storage prices nearly 50%.
Analysis and Insights
Once set up with Big Query, dJAX DMP Manager experts and data scientists are on hand to maximise the potential from your Google Analytics 360 and CRM data. Insights beyond those available in Google Analytics include
- Advanced conversion path analysis
- User navigation/flow analysis
- Advanced attribution modelling
- Customer modelling to identify high LTV candidates or churn propensity
- Audience identification
- Predictive analytics
- Visualization: BigQuery integrates with several powerful dashboarding tools for advanced analytics and richer insights. By joining quality data with GA data in BigQuery, you can tie up events or goal completion from GA with leads and opportunities.
- Targeted marketing: Analyze the market to understand customer trends, predict needs, and optimize promotional efforts.
- Understand customers’ behaviour: Using CRM with data, you can monitor how products are performing online and accordingly improve their sales and marketing strategies.
- High retention rates: By understanding and predicting potential abandonments or customer inactivity, you can provide better retention offers in a more timely manner.
- Advanced modelling: Using CRM data, you can build various models – like customer segmentation, loyalty and inactivity – for better targeting.
- Preserving data: Exporting to BigQuery will help save historical data for later analysis. And avoid losing any fields that are updated or deleted from Salesforce.