Data Analytics Design Patterns speed up time to value

Companies today are bogged down with vast amounts of data from various sources. This high amount of data is meant to benefit the company but often leaves data teams feeling overwhelmed, which can create data bottlenecks and result in a slow time to value. That is why Google Cloud created Data Analytics Design Patterns, cross-product technical solutions designed to accelerate a customer’s path to value realization with their data. These industry solutions bring together product capabilities alongside design methodology, open-source deployable code, data models, and reference architectures to accelerate your business outcomes.

With Data Analytics Design Patterns, you get access to more than 30 ready-to-deploy data analytics solutions. Design patterns leverage the best of Google and its rich partner ecosystem, including Technology Partners & System Integrators. Here are 3 examples of how a design pattern can be applied to unlock the value of data:

1. Improve mobile app experience with Unified App Analytics
2. Maximize digital shop’s revenue with Price Optimization 
3. Protect internal systems from security and malware threats with Anomaly Detection 

Unified App Analytics

If mobile apps are part of your go-to-market strategy, you have several data sources that can provide invaluable customer insights. In addition to tools such as CRM (e.g. Salesforce) and customer care (e.g. Zendesk), you likely use Google Analytics to log app events and Firebase Crashlytics to gather data about app errors. However, can you easily combine back-end server data with app front-end data to unlock customer insights? 

The Unified App Analytics design pattern makes it easy to plug all the disparate data sources into a single warehouse (BigQuery) and start analyzing it with a Business Intelligence tool (Looker). Once you have a complete and real-time view of your customer experience with your app, you can take action. For example, if you notice an increase in app errors, you can quickly combine your Crashlytics data with your CRM data to narrow down the crashes with the highest revenue impact and prioritize their resolution. Further, you can automate your issue resolution workflow by creating a rule for any future crash that impacts a subset of VIP customers.

With the Unified App Analytics design pattern, you’ll gain access to valuable insights about your user experience with your app so you can inform your future app strategy. 

Price Optimization

In a competitive and hectic global marketplace, strategic pricing matters more than ever, but often projects are consumed by the tedium of standardizing, cleaning, and preparing data—from transactions, inventory, demand, among other sources.

Price Optimization solution allows retailers to build a data-driven pricing model. The solution consists of three main components:

1. Dataprep by Trifacta: integrates different data sources into a single Common Data Model (CDM). Dataprep is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis, reporting, and machine learning.
2. BigQuery: allows you to create and store pricing models in a consistent and scalable way as a serverless Cloud Data Warehouse service.
3. Looker dashboards: surface insights and enable business teams to take action with enterprise-ready BI platform.

With the Price Optimization design pattern from Google Cloud and its partner Trifacta, you’ll be able to rapidly unify multiple data sources and create a real-time and ML-powered analysis, leveraging predictive models to estimate future sales. 

Anomaly Detection

Organizations need to anticipate and act on risks and opportunities to stay competitive in a digitally transforming society. Anomaly detection helps organizations identify and respond to data points and data trends in high velocity, high volume data sets that deviate from historical standards and expected behaviors, allowing them to take action on changing user needs, mitigate malicious actors and behaviors, and prevent unnecessary costs and monetary losses.

The Anomaly Detection design pattern uses Google Pub/Sub, BigQuery, Dataflow, and Looker to:

• Stream events in real-time. 
• Process the events, extract useful data points, train the detection algorithm of choice.
• Apply the detection algorithm in near-real-time to the events to detect anomalies.
• Update dashboards and/or send alerts.

The challenge of finding the important insights and anomalies in vast amounts of data applies to organizations across all industries and lines of business but is especially important to protect the security of an organization. 

There are more than 30 Data Analytics Design Patterns ready for you to use with more than 200+ more ideas in the pipeline, so be sure to check in regularly as new patterns will be added soon.