Those following our work and published content will know our direct argument that artificial intelligence (AI) systems are only as useful and functional as the data they are trained on. It is literally a “garbage-in-garbage-out” scenario if your data strategy is not updated regularly, keeping up with the fast-evolving, post-digital transformation era. Hence, as a company involved in AI, we frequently advise our customers that the last thing they need to focus on during their AI journey is AI itself. This point is particularly poignant in 2023.
With the rise of interest in large language modes and generative AI, we expect businesses and government institutions to increase their investments in AI. Thus, high-quality data, not just quantity, becomes even more critical in achieving a functional AI system. Yet, gathering high-quality data is arguably the biggest challenge in data science, and this is where synthetic data play a vital role in your data strategy.
The Challange of Real-world Data
The task of collecting and mining real-world data can be expensive for most enterprises, limited in scope, often biased and fraught with data quality issues and privacy concerns. All of which can negatively impact the outcomes of your AI models. I remember one of my professors back in 1999 describing real-world data as the raw material that needs to be processed and primed before relying on it for real-world solutions.
It is critically important to appreciate that real-world data can often be messy and noisy, with missing values, outliers, and errors that can compromise the accuracy of models and insights. Even if your database is well structured, it does not mean data inputs are 100% compliant with this structure.
…Enter Synthetic Data
Synthetic data is a very powerful tool that refers to artificially generated data created using specialist algorithms, simulations, or other mathematical models for generating high-quality representations of the statistical properties of a given real-world scenario. In simple terms, this type of data is typically designed to mimic the behaviour and characteristics of real-world datasets while preserving the privacy and security of sensitive information of the users involved. Also, from the business budgeting point of view, synthetic data models train and enhance AI deployments in a highly efficient and cost-effective way, thus making it a great tool for smaller teams and startups.
One of the advantages of using synthetic data is that it can be generated quickly and efficiently, making it an attractive alternative to traditional data collection methods. Additionally, synthetic data can be customised to meet particular needs of any given sector, such as adjusting the level of noise or complexity or creating datasets that are representative of specific subgroups or scenarios.
More importantly, synthetic data accelerates AI enablement for organisations with very limited or unusable available datasets. For example, we worked with companies who’s databases were in the form of thousands of PDFs or spreadsheets. In their cases, our synthetic data models played a critical role in defining their overall data strategy, data modernisation, cloud migration and, ultimately, deployment of AI-ready data models.
Risks with Synthetic Data – It’s Not Plain Sailing!
While synthetic data offers many advantages, discussing the risks associated with its use is essential, starting with inaccurate modelling. Perhaps this is the most common issue associated with inadequate data modelling and user behaviour analysis that impacts the quality of the synthetic data generated.
Hence, we apply our BehaviorLink framework to scrutinise data models available to our team to fully understand cultural, societal, demographic, and psychographic representations of real-world user scenarios. Otherwise, the synthetic data may not be useful or may lead to inaccurate conclusions. This process takes time and requires a significant level of experience and patience!
In addition, to generate synthetic data that is representative of a specific domain, such as finance or healthcare, it is critically important to have a deep understanding of the domain and the characteristics of the data. This can include knowledge of relevant regulations, standards, and best practices. This is why our Innovation-as-a-Service solution involves bringing on board a panel of experts in a given project within a given domain. You must establish a similar panel within your organisation to ensure maximising accuracy of your synthetic data models.
Last, but not least, we always advise not to 100% rely on synthetic data from the start. While we put significant time, effort and expertise in generating highly accurate synthetic data, we still compare and contrast our generated models with live data to ensure further alignment and representation over time. We strongly advise you that you do the same, no matter how accurate your synthetic data sets might be. Real-world data is fluid and unpredictable and it is best to be safe and not rush into fully relying on your synthetic data.
Synthetic Datasets Help You Take it from Pilot to Production
According to a Summer 2022 Survey from Gartner, only 54% of AI projects make it from pilot to production. This figure accurately depicts our experience with customers who approached us to help them revive or enhance their AI projects and data models. Frances Karamouzis, VP analyst at Gartner stated in his report that organisations still “struggle to connect the algorithms they are building to a business value proposition.”
In other words, this issue makes it difficult for CIOs, IT departments and business leadership to justify the investment required to operationalise AI models. Again, this goes back to the quality of the available data rather than the quality of machine learning algorithms. Hence, it is unsurprising that the use of synthetic data has become increasingly popular in AI development cycles. Indeed, our research behind our AIQ cited large language models and ClientLab Innovation-as-a-Service offerings to customers are powered by numerous synthetic data models we built over the years. In another Garnter research published in 2021, analysts highlighted that over 60% of the data used for the development of AI and analytics projects will be synthetically generated by 2024.
Furthermore, the models we trained on synthetic data for customers and partners produced more accurate results than other models in most cases, with the advantage of eliminating privacy, copyright, and ethical concerns from using real-world, live data of active users. As a company that firmly and unapologetically prioritises digital ethics and responsible AI (i.e. we do not cut corners to be “market-first” or enhance our theoretical valuation), this is the key reason we apply synthetic data throughout our work.
So, to de-risk your investments in emerging technologies and maximise your ROI, you must focus first on your overall data strategy, the sources of your data and the quality of your data. Then, invest time and effort in developing synthetic datasets to get you started with your journey. If you wish to learn more about how we can help your team with your data strategy and maximise your ROI through synthetic data models, get in touch.
