Automated data analysis is the key to scaling your data processing and reporting while overcoming bottlenecks.

Through automation, you can process multiple data streams simultaneously, in real-time, and avoid human error, which causes inaccuracies and time-consuming firefighting.

That’s just the tip of the iceberg, of course.

This chapter gives you an in-depth understanding of automated data analysis. This includes the benefits of data analysis automation, when to do it, and the best practices you should remember. 

The Fundamentals of Automated Data Analysis 

In data analysis, automation can be applied to various tasks—from data collection to data optimization.

Some specific examples include:

• Importing data from external sources 
• Extracting actionable insights from large datasets
• Cleaning up duplicate and erroneous data 
• Converting insights into readable data visualizations
• Distributing data analysis reports to stakeholders 

Data analysis automation requires data management tools, integrations, automation workflow builders, or a combination of these.

You can also use comprehensive Business Intelligence (BI) software that includes all the necessary features to automate data analysis processes.

(Must read: Fundamentals of Data Analysis)

Benefits of Automated Data Analysis

Check out the main benefits of using automated data analysis for your data analysis: 

• Save time. Automated data analysis frees you from the burden of deeply (and manually) engaging large datasets and extracting key insights. 
• Reduce human error. Since automated data analysis workflows run through strict rules and filters, you effectively take human error out of the equation.
• Improve data quality. Data management and dashboard tools can automatically clean up, sort, and present large datasets.  
• Focus on higher-level tasks. With repetitive tasks on autopilot, you can funnel more of your human resources into more impactful, higher-level data analysis activities. 

In addition to these benefits, remember that automated data analysis techniques also have positive, ripple effects on the productivity of your data team. 

When Should You Use Data Analysis Automation? 

Automated data analysis is best used for efficiency when handling large datasets. However, it's still a good practice to implement automation in time-consuming processes. 

If you have data analysis techniques or processes that are largely rule-based, conducted frequently, and don't require human decision-making, explore simple automation workflows that don't require expensive software. 

Using Google Sheets add-ons to automate data imports, for example, is an excellent way to leverage automation on a small scale. 

You should be able to find free-to-use integrations with popular software from the Google Workspace Marketplace. 

Image Source: docs.google.com

Automation workflow builders like Zapier and IFTTT also let you automate simple but repetitive tasks in data analysis. These tools let you connect two services in an automated workflow—without using code. 

When you're ready to go big with automated data analysis, subscribe to robust BI platforms like Polymer. Doing so lets you consolidate all your data into one environment. 

Here's a quick overview of the Polymer tools and features you can use for automated data analysis: 

• Embedded Analytics. Automatically pull data from Polymer into interactive data visualizations and embed them into your own platform. 
• Third-Party Integrations. Link your Polymer dashboard with several data sources for seamless and automated data collection. 
• AI-Generated Dashboards. Let Artificial Intelligence (AI) and Machine Learning (ML) take over your data analysis and quickly extract powerful insights. 

Automated Data Analysis Best Practices

Automating data analysis has many advantages for fast-paced, data-driven businesses. But it also comes with risks, especially if you inject automation into your workflows without a well-defined strategy. 

Now that you're familiar with the fundamentals of automated data analysis, it's time for some best practices. 

1. Set Specific Data Automation Goals

In data analysis, you need to automate with a clear purpose. 

Setting clear data automation goals will help you determine the processes, metrics, Key Performance Indicators, and tools you need. It will also highlight the important data analysis techniques you need to automate. 

Most importantly, well-defined goals ensure your automation results answer your data analysis questions.

Start with the leadership and move down to other stakeholders to identify pain points that can be solved with more efficient data management. 

For your reference, below are some simple data analysis automation goals you can build on: 

• Improve data accuracy and quality across multiple data sources. Help data scientists make better decisions by ensuring data quality. 
• Increase data analysis capacity to accommodate substantially larger datasets. Scale your data management without compromising efficiency to accomplish business objectives faster.
• Minimize data analysis costs by deploying AI solutions for repetitive, low-level tasks. Save time and money on data analysis to maximize data management ROI. 

2. Analyze Your Current Data Infrastructure

Automation isn't done in a vacuum, and multiple factors like existing data sources, reporting, capabilities, and integrations will have a huge impact on your outcomes. 

Create an inventory of your existing data sources and the relationships between them. At the same time, preemptively identify potential data quality issues that may affect automated workflows down the line. 

You should also identify and assign team roles with specific access levels to your data. You may grant access to certain personnel from each department, build a dedicated team from scratch, or limit access to IT specialists and data scientists. 

3. Establish a Data Governance Policy 

Once you identify your data automation goals and roles, weave them into a data governance structure. Use it as the groundwork for your data governance policy, which develops seamless and standardized processes for data collection, cleaning, optimization, analysis, and verification. 

Your data governance policy should also include the following: 

• Data Privacy and Compliance. Make it a point in your data governance policy to comply with existing data regulations, like the GDPR, HIPAA, and CCPA. 
• Critical Data Priorities. Highlight specific data with crucial regulatory or operational relevance. 
• Data Classifications. Create classification levels that sort data based on importance, sensitivity, and accessibility. 
• Catalog Your Data Infrastructure. 

Remember, every organization may require unique specifications in their data governance policy. Invite your data stakeholders to chip in their thoughts and ideas to build a policy that benefits everyone.

4. Map Out Your Automated Workflows

Before execution, visualize and explain your planned data automation workflows. 

Start with the relevant data sources, data types to be collected, and metrics to consider. Next, elaborate on the transformation, cleanup, and optimization processes before plotting the desired destinations for your data. 

Mapping out your automation workflows also enables you to determine the data analysis tools you need. This should include tools for tracking the performance and reliability of your automated tasks.

(Must read: 9 Data Analysis Examples)

5. Plan for the Future

Build a well-documented data automation strategy to help track your objectives and improve maintainability. 

If you're building with automation tools like NumPy or SciPy, write standardized code and formatting. This allows you to reuse these assets in future datasets instead of creating fresh code. 

It's also important to schedule periodic data updates and manual verification checks. Make sure these tasks occur more frequently than your scheduled reports. 

To maintain long-term efficiency, document all data processing, analysis, and standardization steps. Keep them handy and accessible to your data team to avoid disruptions in productivity. 

6. Consider a One-Platform Approach

Finally, review your data governance policy and automation workflows to evaluate if your needs can be met by a single platform. 

Consolidating your data analysis efforts into one tool leads to numerous benefits. Not only will it streamline your data management and collaboration efforts, but it also unlocks more cohesive reporting capabilities for your organization.

This is where all-in-one BI platforms like Polymer come in.

Automate Your Data Analysis Reports with Polymer

Polymer is a powerful and extremely versatile BI platform that irons out the most challenging aspects of data analysis automation. 

For one, you don't need to write a single line of code to build automated dashboards using the Polymer block editor.

It also natively supports integrations from popular data sources, like Google Analytics, Jira, Zendesk, Salesforce, and Shopify. 

To demonstrate its intuitiveness, here's a quick walkthrough on how to use Polymer to automate data analysis. 

1. Effortlessly Connect Data Sources

Connecting data sources to your Polymer account is as easy as providing login credentials and granting app permissions. 

You can immediately connect a data source when creating a new board. If you're working on an existing board, just go to 'Data' from the main menu, select the "plus" icon, and click 'Add Data Source.' 

In the pop-up window, select the data source you want to connect and follow the on-screen instructions. This whole process should take no more than five minutes to complete. 

2. Build Readable Data Visualizations in Seconds

Polymer features multiple ways of presenting data, and you can utilize all of them through "PolyAI." This is a built-in AI assistant that can extract insights from your data with text prompts. 

3. Kickstart Data Analysis with Customizable Dashboards

Polymer's intuitive interface ensures that anyone can compose professional-looking data dashboards with little to no training.

The platform also provides in-app tutorials and tooltips that smoothen the learning curve. To kickstart the setup process, use customizable templates tailored to specific data connectors. 

Try Polymer for Free

Automated data analysis doesn't have to be complicated. 

With a one-stop Business Intelligence solution like Polymer, you can breeze through most of the heavy lifting and build automated workflows with ease. 

Get started today by creating a free Polymer account.