Boosting ServiceNow Productivity with Synthetic Data

Synthetic data was identified as a critical enabler of company productivity and efficiency in Gartner's "Emerging technologies and their impact in 2023" report. According to Gartner, synthetic data has the potential to revolutionise the way that organisations collect, manage, and use data.

Without the restrictions and biases of real-world data, businesses can build datasets that are ideal for their purposes. Using synthetic data, organisations can improve the effectiveness of their machine learning model training, software application testing, and data analysis.

What is Synthetic data?

Synthetic data is like "pretend" data that machines can use to learn and practice without using real data. It's similar to a game where you experience making decisions and carrying them out, except that the game is not representative of real life. Synthetic data allows robots to train without requiring access to sensitive or costly real-world data. They can use this fictitious information to their advantage, enhancing their knowledge and skills without risking anyone's safety or wasting any money.

How does it work?

Machines or computer algorithms generate synthetic data to act as a stand-in for the actual thing. To this end, we aim to generate a dataset similar to, but not constrained by, the real world's data. This renders it useful for numerous endeavours, such as ML/AI/DA. Organisations can get beyond the restrictions of real-world data by creating their own synthetic data, which they can then utilise for various purposes, including but not limited to training machine learning models, testing software applications, and doing data analysis.

How is it created?

Machine learning algorithms, data augmentation tools, and data integration and management instruments are all examples of the technical competence required to create synthetic data. With these tools at your disposal, you can generate artificial data indistinguishable from the actual thing but avoids the latter's restrictions and biases.

How can this be used in IT Service Management?

Synthetic data can be used to train machine learning models and verify IT service management procedures as an integral part of IT service management. For instance, you can train machine learning models for incident prediction and prevention using fake incident tickets. IT service management processes like incident, change, and problem management may be tested with synthetic data to ensure they function effectively.

Synthetic data and ServiceNow

To boost decision-making, enhance IT operations, and fuel innovation and growth, businesses may use ServiceNow to generate synthetic data that is high-quality, diverse, and representative. By generating artificial incident tickets and configuration items, companies can produce synthetic data for IT service management and other industries (CIs). Machine learning models can be trained, IT service management procedures can be tested, and IT operations management checks can be validated with these datasets. Other ways ServiceNow can benefit from synthetic data would be:

1. Testing and Validation: ServiceNow can use synthetic data to test and validate IT service management (ITSM) processes and workflows. Synthetic data can simulate real-world scenarios and help ServiceNow identify potential issues before deploying new processes or workflows.
2. Machine Learning and AI: ServiceNow can use synthetic data to train machine learning and artificial intelligence (AI) models that can improve its ITSM processes. By creating customised and diverse datasets using synthetic data, ServiceNow can improve the accuracy and performance of its machine learning and AI models.
3. Improved Data Analysis: Synthetic data can help ServiceNow improve its data analysis capabilities by providing representative and relevant datasets. This can help ServiceNow gain new insights into its ITSM processes and identify areas for improvement.
4. Data Integration: ServiceNow can use synthetic data to integrate data from external sources, such as third-party systems or external databases, into its ITSM processes. This can improve the accuracy and completeness of its data and provide a more comprehensive view of its IT operations.
5. Reduced Costs: Synthetic data can be generated more cost-effectively than real-world data, which can help ServiceNow reduce its data collection and processing costs. This can free up resources that can be used for other business activities.

And it is not FAKE data

Remember that synthetic data is not the same as fabricated data. When information is manufactured to deceive or mislead, it is considered fake data. For instance, it would be unethical and unlawful for someone to fabricate information regarding a company's financial performance to manipulate the stock market.

On the other hand, synthetic data is made by simulating actual data through computer algorithms or different computational approaches. Moreover, while synthetic data can be used to simulate real-world data, it may not accurately represent the complexities and nuances of real-world data. This means that synthetic data may be helpful for specific use cases, such as training machine learning models or testing software applications, but it may only be a suitable replacement for some types of data.