What is Looker?
Looker is an enterprise for BI- Business Intelligence tool, embedded analytics, and a data application platform. Looker became a part of google cloud in 2020, and from then, Looker provides users to create and share insightful visualisations of the data. It is a web-based or on-premise tool. It collects, visualises, and analyses data, but starting with Looker requires heavy effort as we have to format and model data in a particular way with LookML; it can not process data and create reports independently. Google cloud’s & Looker data analytics platform will provide options to deliver value with robust and new insights.
What are Looker Data Actions?
Looker actions is a data activation tool that analyses data in real-time, and data activation is a method to turn insights into actions. With Looker API calls, users can perform tasks in other tools, and LookML triggers an API call in a particular field. Data activation is generally done by picking up clean and modifying data from the data house and sending it back to business teams like marketing or sales.
Looker actions mainly focus on sending data back to business users, and they get real-time data and act accordingly. Looker tools support work within other tools too. Tools like slack, updating values, warning team members in tools, sending emails, and automating them are the popular ones that are used.
Why Data Actions?
Looker data actions helps in achieving the following things for an ease.They are:
- One can easily update salesforce records form a single page.
- You can easily manage the support tickets.
- You can easily tag an dpritoize the github issues.
- You can easily monitor the adword spend.
- Enable the trigger tailored emails on command.
Looker takes an advanced approach to analytics, making it simple to build dependable data applications that enable any user to explore, analyze, and comprehend the data they require.
Data Actions, which are based on our extensive APIs, allow users to perform tasks across nearly any other application from a single Looker interface. Stop forcing your team to switch between tabs and tools to complete routine tasks.
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Looker Data Actions:
Using Looker’s standard tools, you can move through your workflow quickly. Looker Actions enables us to create and act on your data. From interacting with your users to revamping records in any of the application domains you use, you can do it all.
Here is the list of looker data actions. They are:
Slack:
Notify your team of changes in activity directly from Slack.By directly injecting data into conversations, you can directly answer important questions.Custom commands that query Looker directly through Slack can be distributed to the rest of your company.
Segment
Looker email cohorts can be easily managed by sending lists to Marketo, Hubspot, Airship, and other services.With the click of a button, you can activate win-back and upsell campaigns.
Twilio:
Ad-hoc sends allow you to quickly send a text message to any phone number in your database from Looker. It doesn’t matter if you’re sharing your knowledge by sending data or simply creating a custom message on the fly.Schedule text messages – sharing insights with customers is an effective way to build relationships. Schedule data delivery to those who require it the most at your preferred interval. Use the Twilio Action to set up text alerts to easily notify customers when something happens, such as a delay in an order or an outage on their instance.
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Zapier:
With the Tray Action, users will be able to seamlessly integrate Looker queries into their daily workflows. Upload data to a cloud storage solution, distribute reports via an email list for the team, or even send reports to customers. Use this Action for a variety of scenarios with Zapier’s extensive integration list, the sky’s the limit.
Salesforce:
As you progress through the sales cycle, update the contract value of each new deal.
Twilio:
Use Twilio to send promotions, customer satisfaction surveys, and other notifications to customers.
Exavault:
Schedule the SFTP delivery of Looker dashboards, visualizations, or data to ExaVault. Avoid email size restrictions and ensure that your Looker data reaches the people and systems that need to process and analyze it. The following are some examples of use cases for this Action:
- Sending daily sales and inventory automatically
- Reports are automatically sent to colleagues and partners.
- Schedule data collection to ExaVault.
Amazon Sagemaker:
Using machine learning algorithms on Looker data, use Amazon Sagemaker to predict, forecast, or classify data points.This Action allows you to send the results of a Looker query to XGBoost or Linear Learner to train a model for regression or classification, or to perform predictions on the results of a Looker query using a previously trained model. The Action is made up of three parts:
- Amazon Sagemaker Train: XGBoost – uses the output of a Looker query to train an ML model with the XGBoost algorithm for regression, binary, or multiclass classification.
- Amazon Sagemaker Train: Linear Learner – uses the output of a Looker query to train an ML model with the Linear Learner algorithm for regression, binary, or multiclass classification.
- Amazon Sagemaker Infer : operates a batch inference job against the output of a Looker query using an existing Sagemaker ML model for target prediction.
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SendGrid:
- Ad-hoc sends allow you to quickly send an email from Looker to any email address in your database. Whether it’s sharing your knowledge by sending data or simply creating a custom message on the fly.
- Schedule emails – sharing insights with customers is an effective way to build relationships. Deliver scheduled data to those who need it the most at the interval you specify.
- Email alerts – using the SendGrid Action to easily notify customers when something happens, whether it’s a delay in an order or an outage on their instance.
High touch :
Reverse ETL has features lacking in Looker Actions and reduces the barrier between them. High touch is one of the alternatives to Data Actions, and reverse ETL copy’s data from analytics platforms or data warehouses to operational systems of record. Hightouch clears the problem by leveraging Reverse ETL, which transforms data from the data warehouse and synchronises it back to the native tools of businesses like Marketo, amplitude, Hubspot, iterable, salesforce, Google sheets, etc.
With hightouch, users can map attributes like purchases and emails to any field. It saves money and time by synchronising data at specific locations and ensures that no duplicate data is present. In Looker Actions, there is a limit on updating end tools, but in Hightouch, we are free to update any field and can send data in batches, unlike in looker Actions. High touch directly integrates with LookML and Looker, and it benefits companies to connect directly with Looker and view their reports.
Auger.AI:
This Action also reduces the workload of each data scientist because anyone in a company can run and deploy a predictive model with a few clicks. To create an accurate predictive model, use this Action with any labeled dataset, such as:
- Forecast inventory to better balance supply and demand Predict equipment failures to perform preventative maintenance
- Estimate headcount and employee turnover, as well as customer churn.
- Determine the credit risk of customers for loans and financial transactions.
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DataRobot:
This Action also reduces the workload of each data scientist because anyone in an organization can run and deploy a predictive model with a few clicks. Utilize this Action to:
- Determine which customers are likely to be repeat buyers.
- Learn what user characteristics make certain account profiles a churn risk.
- Investigate which factors, such as region and age, lead to higher sales.
Airtable
Looker Airtable Action transfers data from Looker to your Airtable spreadsheets. Using this Action, you can create and update Airtable spreadsheets for a variety of purposes, including:
- Developing and maintaining lists of customer segments.
- Every order and its details should be listed on your eCommerce site on a daily basis.
- Keeping track of any backend infrastructure issues as they arise.
- Keeping a list of customers who have been affected by high-severity issues.
What are the problems with Looker Data Actions?
Looker’s premium features created a revolution in Business Intelligence tools and have provided many solutions in BI. Every software has its drawbacks, and Looker is not exceptional, but its advantages make it one of the best tools in BI and have a strong premise.
Large data volumes can not be handled upto the mark by Looker Actions. Data differencing or diffing will not take place in Looker Actions. Diffing is a method used to check if there are any changes in data before sending it to another system or application. Looker’s Data modelling, unique coding language (Look ML), and data matching capabilities are constrained to use. Companies have to duplicate their data into LookML, and companies with native tools or current data models cannot transform their data.
Many companies rely on SQL to modify their data. LookML is partially built on SQL, and users are needed to learn an entirely new language. It is expensive and time-consuming for businesses that are not yet using Looker. There are also some effective and easy tools to transform data ex. DBT, which is fully developed on SQL and automatically updates models. Developers and engineers can quickly transform, orchestrate and model their data.
Developers and engineers can use it to orchestrate, transform, and model their dataLooker Actions lack batching capacity for many destinations. Looker Action will send all the records irrespective of their duplicates. If a massive amount of data is to transform, it may fail because of the rate limit issue.
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Conclusion
In the above blog post all the looker data actions are explained, you can select your interested action to perform the business operations. Had any doubts, please drop your queries in the comments section, our experts will get back to you shortly.
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