Introducing No Code Data Automation - What Is It And How Does It Work?  

In today's data landscape we have more data than we know what to do with and much of it is not ready for analysis. According to the IDC only 0.5% of data is analyzed, while the percentage of tagged data is a bit higher at 3%

But how would you collect, clean, and put it together for data management, analytics. The whole process is tremendous task, but thanks to no-code the process has become much simpler.

In this article, learn about no code data automation and when it's time to move to a real-time data pipeline. 

What is Data Automation? 

Data automation is the process of extracting data from a source, transforming, or loading data into a targeted system (like a data warehouse or data lake) using automated tools instead of manually performing all these tasks. 

In some instances this process also includes updating reporting and visualizations through automations rather manual processes.
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What is Toric's No-code Data Automation?

Toric is a platform created first for the complex world of construction data equipped to handle a variety of data and data blending for model data, finance data, project data and more. No code data automation is automating any part of the data pipeline but without writing any code. By connecting to data sources including integrations, data warehouses or data lakes directly it's possible to perform repeatable automations in Toric.

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Watch Toric's Fireside Chat Introducing Data Automation for Construction to learn more

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How does no-code data automation work? 

Through integrations and connections it's possible to push 1000s of models data and other data sets directly into Toric, set up and run custom automations and and create workflows that feed your data into Toric's Data Apps or into your existing reporting and other systems.

Step 1: Connect to your data source

The first step to any data analysis is to access data, but as easy as it sounds this is not usually an easy step in data analysis. However with no code you remove all barriers and connect to your data sources in just a few clicks (and then run automations for data extraction.)

With Toric we have simplified this processes by building connections for anyone to access their data sources or data storage directly and process the data in our cloud based tool. Use our API or direct connections to access your data, with new integrations available every few weeks.

Step 2: Set up a Dataflow (or start with a Data App template!) 

Use Toric's Data Apps to find a template that suits your use case or build your own dataflow to clean your data.  Check out our dataflow library to learn more about helpful nodes such as Toric's Diff node - a node create to help instantly identify the differences between data sets in analysis.

Step 3: Set up one of more automations using triggers

Setting up an automation trigger is simple. Watch how easy it is to setup a trigger in this walkthrough of data automations from Toric's data scientist Varun Sewal.

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Why do you need data automation? 

Automations make data available to business users faster than any other data delivery methods. By implementing data automation your team can access data and adopt a real-time ETL data pipeline.

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1. Get data you can use now by reducing infrastructure complexity.

Create self-serve access to data for all. Some data automation is completed using coding, but now there is a simple no-code way you can automate the ETL process and beyond. Often, these triggers remove barriers to data that IT teams otherwise perform manually so access to data is instant rather than blocked within a ticketing system. Instead you can use Toric to make data accessible from your data source in just a few clicks and automate the process using a variety of triggers.

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2. Facilitate data science and supports business insights with big data.

Data automation aims to save time and improve efficiency across all business processes. An added benefit is the reduction of errors and so data analysts can help their business users focus on insights rather than preparing data themselves.

By instituting automations you save your data team time and allow them to focus on solutions rather than getting access to data or combing data to understand what has changed between data sets or over time.

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3. Continue to build on top of the automation.

Once you've automated one part of the data process, you can automate other aspects as well. For example, you have started a data extraction automation and this data feeds from one source into another so that it can be used for insights. Great, now you have data available. What's next- building more automations on top of it to save your team even more time and get actionable insights fast.

What are some options? 

Data Extraction and Ingestion

We've already discussed some of the triggers available for data automation extraction and ingestion

Diffing Data Sets

This can be done within the triggers to ingest the data but even if you are doing a time based data ingestion trigger you can build on top of this automation within Toric's dataflows. From this point you can use the Diff node to tell the differences between data sets over time if you want to compare previous versions of the data. You can even apply this node to model data extracted from Revit

Data Cleanup and Transformation

Any dataflow created can be reused in Toric. This feature enables you to use a dataflow that you have created and run any data through it. Meaning you can transform and reformat the data to make data sets easier to blend and compare for any particular purpose.

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Data Warehouse

You can feed data from Toric into a warehouse or create a data warehouse in Toric. Use automations to feed data into a warehouse to make it accessible for use in analysis.

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Automated Reporting and Visualizations

From Toric you can export your data to the reporting sources you already have available such as in Power BI or Tableau.

In Toric you can take reports a step further natively within Toric. As you perform your analysis you can utilize Toric's Data Apps.

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3. Scale data to your team.

By letting your team pull data from an automated warehouse or interact with reports that showcase up to data data you can help make data self-serve for your team. See an example of our model comparison app below to see what kind of data your team can work with and automate. Interactions such as cross highlighting and filtering make the data interactive. This way your team can easily get the insights that they need fast.

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