5 Benefits of BigQuery for Marketers
BigQuery for Marketing: What Makes it Special?
Digital marketing is full of metrics that are part of the analytics routine. Metrics like KPI (Key Performance Indicator), CPC (Cost Per Click), and ROI (Return on Investment) are gathered through different types of media and exist to measure marketing campaigns. Big Data here is a fundamental part of the scenario as it enables the technical integration of data from all digital environments along the customer path.
Google BigQuery is a potent tool for working with Big Data, but surprisingly its distributed architecture makes it an equally attractive solution in cases of smaller data volumes. Due to its technological flexibility, the utilization of the reputed Google BigQuery for marketing purposes can take the form of data ocean navigation.
Last but not least comes its affordability. BigQuery pricing is a relief for marketers. You only pay for the resources you use. Whether it’s storage resources or computing resources, Google charges only based on how much of the tool you use.
BigQuery operation principles
Business intelligence projects presume collecting information from different sources into one database. Then, an analyst prepares them for reporting (via data visualization tools like Google Data Studio). The BigQuery tool was designed to be the centerpiece of data analysis.
The main principles that define the benefits of BigQuery for marketing purposes are:
- Once BigQuery has processed the loaded data, you can export it for further analysis.
- It lets you perform interactive batch queries and create virtual tables from your data.
- BigQuery helps you manage your datasets and tables. You can update or correct any data you enter.
Another essential feature of BigQuery is the possibility of data crossing. For instance, the first contact that users have with the ad should, consequently, direct them to a website or application where the desired conversion action will take place. Connecting Facebook Ads, Google Ads, or Linkedin Ads to BigQuery brings insights into how many clicks a user needs to convert or even the average time spent in each of these stages of the purchasing process.
Source: Google Developers
5 BigQuery Advantages for Marketers
In short, marketers can use BigQuery to accumulate various marketing data, such as clicks, impressions, navigation, and performance data. Then, marketers can exploit statistical and machine learning techniques to generate far-reaching insights and enhance their marketing activities such as ad campaigns, social media content creation, YouTube marketing, etc. In more detail, we advise paying attention to the following benefits of the BigQuery tool.
It is easy to set up and use
When operating a business, you want to aggregate all of your information fast. The most crucial advantage of BigQuery for marketers is that it is easy and quick to launch. A huge community and vast field of educational resources make the setup process even more pleasant.
Building a data center on your own can be expensive, time-consuming, and difficult to scale. It leaves you frustrated and can even waste your resources when trying to master data encryption on your own. It meets the universal SQL standard, which makes it compatible with all existing analytic apps for creating dashboards and reports to understand data better.
It has a flexible architecture
BigQuery dynamically distributes computing resources and has a flexible architecture to speed up performed queries. By using it, managers reduce the costs of creating the cloud system and gain more time to analyze data. That way, you won’t be trapped in rigid structures that were built around multiple compute clusters.
Google BigQuery supports the rapid distribution of computing power according to your needs. The scalable construction of the tool is carried out without a physical server. The design is decentralized to query petabyte-scale datasets.
Source: Stambia
It ensures data protection
Safety is the most valuable thing to every business. BigQuery protects data and maintains strong security on it. This process removes the burden of having a disaster recovery plan in place in case information is compromised or lost, although you should still have a disaster recovery plan.
BigQuery automatically replicates and stores your data, ensuring its safety. You can specify a list or table expiration option if you know you won’t need them afterward.
It preserves historical data for the further access
One vivid problem with marketing tools is that they place limits on the amount of historical data you can access. For example, Search Console offers 16 months of historical data in its native interface. By integrating with other applications and storing them in Google BigQuery, it is possible to have them forever, managing an extensive repertoire helpful in building reports and delivering insights.
It maintains a seven-day history of changes. For instance, you can compare your data at the beginning and end of the week and store the combination of those. Another BigQuery use case is storing previous years separately (because you rarely use it). You can then reserve one table every next year.
Real-time analytics
BigQuery has a high-speed streaming insertion API. With real-time analytics, you can grab your latest business data and analyze it right away. This feature is highly beneficial for your business as it helps you understand your data as you compile it. It also handles transactional databases and spreadsheets in Drive.
You may select the processing and storage options that are most effective for your company’s objectives. Unlike traditional architectures, BigQuery requires unparalleled computing power during the execution, which is a wise use of resources. From an economic point of view, it is revolutionary.
Conclusion
Indeed BigQuery responds to all the business issues relating to the world of data (or Business Intelligence). It is a relational and flexible database that is ideal for processing information for marketing needs. Thus, Google BigQuery helps in data mining and exploration, that is to say, all the necessary operations of the decision-making chain.
Originally posted on OpenDataScience.com
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