Edit Your Dataflow Instead of Your Code
In the new era of data, people have more data than they can process for actionable information.
There are too many barriers to data insights. The current process to distill data insights from multiple sources is laborious and the tools available to make connections between data require specialized knowledge in coding. People need a better way to access their data, and this is why Toric created an all-in-one dataflow workspace. Dataflows cut out complex time consuming manual coding and data processing and instead provide an intuitive way to interact with your data.
Most data tools are not keeping up, and simpler tools like dataflows are taking their place.
Dataflows enable teams to cut through the data noise and spend more time on actionable insights. In many ways, dataflows are the natural evolution for data and business intelligence and data storytelling. But before we dive into how- let’s get on the same page on what a dataflow is.
What Is a Dataflow?
Commonly a dataflow (aka data flow) refers to a model or diagram in which the entire process of data movement is mapped as it passes from one component to the next within a program or a system. Conventionally, dataflows were maps to take into consideration how data is processed and changed- this process has evolved.
In the current data landscape, dataflows enable the transfer of information from one part of a system to another.
What is Dataflow in Toric?
When we refer to dataflows in Toric, we refer to a major component of the Toric workspace. In this context, a dataflow is an interactive model or diagram which you use to map and transform your data using nodes. See an example of our Embodied Energy Calculator pre-built data app, in this workspace you can see the dataflow enables you to connect data from an Autodesk integration and blend it with an Excel file containing information from the EPiC database.
The Natural Evolution for Data and Business Intelligence.
Interactive dataflows enable a completely interactive birds-eye view of transforming data and discovering insights. By interacting with a dataflow directly, you interact with the data story directly. It’s easier to envision connections between data sets, pull relevant data, and create and share data visualizations.
5 Benefits of Editing Your Data in Dataflows
1. No complicated coding barriers.
You don’t need specialized skills to create a dataflow. By using an interactive dataflow chart you can conceptualize data connections and interact with data in ways spreadsheets and other programs can’t.
Conventional dataflows and data pipelines require various connections between programs. This conventional method created technical barriers and required specialized tools and knowledge (coding and data analysis) to create these connections.
Toric’s dataflows do not require programming experience and you only need to load in your data to interact with it directly; making data insights more accessible across experience levels.
Interactive dataflows enable you to pull your data from any source without code or specialized skills, this dataflow screenshot showcases Procore data within a Toric dataflow.
2. Enable easier data insights cross-departmentally.
Working together requires access to data, projects, and shared assets. But it also requires tools that make you more productive as a team. By removing complicated connection methods, you can easily blend data from any source, independent of the tools used by each department.
Data blending - Your insights are hidden in the connection of all the data.
Data blending is a major advantage to facilitate insights across different departments. For example if one team is using Autodesk, another is using Procore, but another team only cares about Sage300 finance data. Independently you only have access to one set of data, but if you have the ability to extract and format the data so it's compatible, then you can blend or enrich data the data to find new connections.
The ability to work in a cloud-based environment and connect to any source eliminates data silos and enables teams to access otherwise dark data. By leveraging integrations, you can gain access to data at the speed your team works, and even automate flows.
3. Reduce rework by replicating and automating dataflows.
Make better use of your data analysis team, instead of creating dataflows and data pipelines for each instant someone is looking for insights you can replicate and automate the work. Your time is spent on valuable tasks like data exploration instead of replicating and maintaining projects.
4. Replicate dataflows and create a team data app library.
Dataflows sit on top of your data. It is easy to create reusable data apps in which you can replace and tweak dataflows rather than creating a data pipeline from scratch for each report.
Create a team library of pre-built data apps that enable any team member to swap out source data and gather valuable insights.
5. Automate reports.
Create multiple smart data app views for various stakeholders from the same dataflow. Connect directly with your data and simply sync your dataflows to the latest files in your reports.
Critical decisions are made using data, but conventional data pipelines and manual coding make the process painfully slow, expensive, and inefficient. Use no-code dataflow to enable teams to work together using data by easily blending data and creating reusable and automated reports.