There are many visual tools for data analytics and AI on the market these days. They can be used for practically everything — from pipeline setups to data manipulation, model generation, and more. These tools are user-friendly, allowing you to interact with your data in a way that's more intuitive than text-based tools. In addition to visualization, they can help with data access and ingestion, data preparation, DataOps, and sharing your analysis and findings with stakeholders.

The development of many aspects of a sophisticated data project is time-consuming regardless of your coding ability. Visual data tools drastically decrease the time needed to construct prototypes and implement changes, allowing you the freedom needed for proper experimentation. Although these tools are not a replacement for code, they enhance your ability to accomplish more with less. Visual data tools can help non-coding analysts as well as coding experts tackle advanced analyses in a self-service manner.

1. Visual Tools Mean Less Control

While some data experts feel that visual tools give them less control over the process than crafting custom code, in actuality, visual tools abstract away the tedious aspects of manually coding data transformation and analyses, so you can focus instead on the accuracy and logic of the processes and whether the outcomes serve the larger business objectives.

2. Visual Tools Are for People Who Can't Code

One of the best things about visual data tools is that they’re accessible to everyone, whether or not you can write code. Graphical user interfaces allow you to focus on the desired outcome rather than on the complex steps involved in getting there. For example, a data scientist will often write scripts to extract information from different data sources like web pages and clean the data before converting it into a readable format for analysis. This can be a time-consuming process without the help of visual tools. When you use these tools, you extend your opportunities as a data analyst and get more done quickly.

In addition, the visual workflow and resulting artifacts make it easy to understand what the code is doing, so you can develop valuable processes.

3. Machine Learning Is Too Complex for Visual Tools

Chasers offers AutoML capabilities which enable data scientists to reduce repetitive tasks, speed model experimentation and evaluation cycles, and help explain results. These ML processes can help with everything from feature selection and engineering to model training and hyperparameter search to calculating performance metrics and running head-to-head model tournaments. They empower data experts to build and compare multiple models using a fraction of the time and resources.

4. Visual Tools Aren't Realistic for Large Projects

The role of a visual data science tool is to encapsulate complexity, leaving you to focus on the core of your work. And visual pipelines help you document and communicate the manipulations and transformations of your data. When working on large analytics projects, visual tools can help your team see the big picture, making it easier to understand how data has been transformed as it’s progressed from start to finish.

5. Visual Tools Are Challenging to Automate

Visual tools allow for the same automation as other approaches, except now you can see what’s happening. This is particularly useful when you have extensive pipelines that could benefit from automation of tasks such as data sourcing and preparation, building models, provisioning compute resources, or updating a pipeline that generates reports or a dashboard.

With Chasers, you can use an automated system to set up workflows that are sophisticated enough for experts but also easy to use by non-programmers.

6. Visual Tools Are Difficult to Collaborate On

Although developers commonly rely on frameworks like Git for project collaboration, visual tools are better if you have a team of people with diverse backgrounds and technical skill sets. Visual tools allow more people to work on a project — including those without a deep technical understanding. Everyone can see code-based or non-code-based elements and transparently follow what’s happening to data as it moves through the pipeline.

Chaser’s workflow makes it easy to see what steps you need to take next. For example, the Flow shows you a visual representation of your project status and is designed to help non-technical team members understand how their contributions affect the project as a whole.

Drive Business Value With Visual Tools

Visual tools can help you better understand your business value. You can use visual tools to make yourself more productive, saving you time and making it easier for you to focus on what’s most important: doing what you do best.