High-Level Blueprint for Synthetic Data Generation

5 Steps to Map Out Your Synthetic Data Approach

It’s tempting to want to take the synthetic data plunge by jumping straight into the deep end, so to speak, but starting conservatively and thoughtfully in “the shallow end” is what we recommend. 

It helps you get a greater understanding of your current data approach so that you can reap the full benefits and potential of synthetic data generation across your entire organization.

These are five initial steps you can take to create a workable blueprint for synthetic data adoption.

STEP 1. Assess Your Company’s Level of Data Risk

Before determining how generating synthetic data can help your organization better protect information, it’s important to get an accurate idea of your current security exposure and requirements. 

Ask yourself:

• What type and volume of data does your organization collect and store?
• In what ways does your organization use this data?
• What privacy and compliance regulations apply to your industry?
• What data security measures do you currently have in place?

Revisit and update existing programs, and document your findings, with your internal teams so you can reference them later as you build out your synthetic data strategy. And consider reaching out to external data experts if you’re in any way uncertain about your compliance responsibility around existing or emerging data privacy regulations.

“There’s an old saying ‘when everyone is responsible, no one is responsible.’ But when it comes to synthetic data, it truly takes a cross-functional team to evaluate and optimize potential use cases. Educating and engaging internal and external stakeholders is a first step towards developing a strategy that delivers benefits across the enterprise.”-S.i. Systems SME

Step 2. Identify the Best Use of Synthetic Data for Your Organization

Once you have a better idea of your company’s data risk exposure and the legal regulations you must follow, you’re ready to consider where synthetic data will be most useful to your organization in terms of your workflows and the value it can deliver. 

Popular synthetic data applications include:

• Business Analytics & Insights: giving developers and programmers safe access to all organizational data for more accurate analysis and more impactful data-driven decision making
• AI Model Training: using high-quality, privacy-compliant synthetic data sets to generate more effective machine learning models
• Application Development & Testing: efficiently generating large, realistic, compliant test data sets without the need to use production data

This is by no means an exhaustive list of synthetic data use cases. Be sure to do your own research into all the ways synthetic data can be applied. Your decision of how and when to generate and leverage synthetic will help you determine which tools and methods are the right ones to meet your needs.  

Step 3. Take Future Success into Account

Synthetic data generation can benefit your organization for years to come, if you fold scalability and agility into your strategy from the start. As you begin to evaluate specific solutions, do it through a long-term lens.  

Ask yourself:

• Will this solution grow with me as my data needs evolve?
• Will this solution continue to comply with regulatory frameworks as they change or expand?
• Does this solution align with my existing technology stack?
• Can this solution seamlessly integrate into current workflows?
• Is this solution intuitive to use?

Err on the side of asking more questions now to ensure your synthetic data investment pays off down the road.

STEP 4. Choose the Right Approach for Your Organization

Consider your entire ecosystem—your teams, your workflows, your data collection processes, your tech—when narrowing down, and selecting, your best-fit synthetic data solution. This helps ensure faster adoption, minimal disruption, and more seamless enterprise integration. 

The right solution will:

• Ensure compatibility with current cloud platforms, databases, and security protocols
• Efficiently leverage automation to streamline data generation, access, and analysis
• Ensure synthetic data is easily discoverable via a self-service portal

STEP 5. Consider a Partner to Simplify the Process

Embracing synthetic data can be daunting, depending on the size of your organization and the scale of your data analysis needs. 

So while you could go it alone, you might find it easier to consult with a company that can take some of the weight off your shoulders and help you:

• Implement industry best practices to avoid starting from scratch 
• Reduce implementation risks by learning from others who’ve already rolled it out 
• Adopt synthetic data faster across your organization

Start Planning Your Synthetic Data Journey

At S.i. Systems, we see every day how a measured approach to technology adoption offsets potential issues. This is especially true where synthetic data is concerned. 

It takes a comprehensive plan AND the skilled IT talent to achieve success. We can help you with both. Reach out today to get started.