If we were to say that the following years are the years Bangladesh Cricket Team have won the ICC ODI Cricket World Cup: 1987, 1999, 2003, 2007, 2015, 2023. Would you believe us? No, right?

Because we all know, Bangladesh Cricket Team have never won any of the ODI Cricket World Cups as of this date. So, what we have showcased to you is a list of the years Australian Cricket Team won the ODI Cricket World Cup. We would consider that to be an authentic dataset representing the triumphs of Australian Cricket Team in ICC ODI Cricket World Cup but when it comes to Bangladesh Cricket Team, those data would be considered synthetic data, which can be called a more sophisticated way of saying fake data. We can get a gist of what synthetic data is from the above example, it’s basically information that is artificially generated in contrast to information that we can gather from the incidents of the real-world. At first glance, these data might feel like useless information but they serve to be more purposeful than they appear to the naked eye as we dive deep into the world of synthetic data.

What is Synthetic Data?

Synthetic data basically means data that is artificially generated and replicates the characteristics of the real-world data without collecting actual information from real individuals or real-life events. These datasets are created using various algorithms or models that are somewhat similar to real-world datasets.

What are the Advantages of Synthetic Data?

Real-world data, in terms of statistics, is hard to come by and is often confidential. Another big reason real data is not a viable option because it is very expensive and time consuming to collect real-life data.

This is where synthetic data comes in as the savior for industries that are highly dependent on data and requires a large number of datasets on a regular basis. Producing synthetic data is cheap and easier compared to collecting real data.

But that’s not all it has to offer; it can be perfectly labelled as per our need. Surprisingly, neither of these qualities are carried by real-world data.

What are its Uses?

Now, question may arise, why do we need all these fake datasets when we can use the existing real-life data?

To answer that, there lies a huge number of beneficiaries in the data hungry world of AI (Artificial Intelligence) and machine learning. It is truly fascinating how synthetic data is being used to train machine learning models and is enabling data scientists to fine-tune their models in an effective manner. There is another catch, they can transfer the resulting machine learning algorithms from synthetic data to real-world data. Gartner, an American tech-research firm says, by 2025, we will be needing 70% less real data to feed the data hungry AI industry. Moreover, a lot of other industries such as finance, healthcare, marketing can also highly benefit from using synthetic data.

What are the Challenges Faced by Synthetic Data?

Although synthetic data is deemed to be advantageous to many of its users, it does have a few limitations in its use.

For example, it is challenging to produce synthetic data is representative and perfect in every way because they can’t always accurately account the various real-world factors or variables that may affect the performance of the model being trained. The one key distinction that makes it challenging for synthetic data to replicate real-world data is the unpredictability of the events that occur in real-life. This leaves synthetic data in the urge of predicting the unpredictable circumstances.

Moreover, the tools and techniques used for generating synthetic data are still in the evolution cycle and has a wide range of possibilities with room for improvement of the technology.

How is Synthetic Data Revolutionizing the World of Advertising?

When we think of advertising or marketing in general, we know that data is the literal lifeline of these streams. As our world of digital media is changing rapidly, it is becoming harder to collect data that is accurate, well-structured and bias-free. People of the advertising and digital marketing field require a viable solution to attain these datasets. That is where synthetic data comes in. It can fix the privacy challenges of sharing data with third parties, helping the purpose of targeted digital marketing campaigns.

In the digital marketing sphere, targeted advertising is known to be the holy grail for businesses that want to optimize their reach to potential customers. But the problem is that the effectiveness of targeted marketing is highly relied on accurate and quality data. Nowadays, businesses are prone to using synthetic data generation tools and techniques to collect these data and effectuate their advertising strategies.

What are the Benefits of Using Synthetic Data in Targeted Advertising?

• Prevention of Privacy Breach: Synthetic data can facilitate the advertisers to ensure the privacy of the users while enhancing the targeting strategy.
• Conceptual Product Testing: When it comes to testing new product prototypes, synthetic data can be used to stimulate market response and help strategizing plans according to the response.
• Augmentation of Datasets: By using synthetic datasets if we can augment real datasets, it can increase the chances of reading user behavior patterns for effective targeting.

In conclusion, synthetic data is already a talk of the town in the world of AI and Digital Marketing. In theory and practice, it is not a farfetched idea or a concept as it is already being used by many individuals and businesses in the world of marketing. Furthermore, it is not too far in the future that this technology will be used or applied in the marketing strategies broadly by implementation in the marketing mix. As businesses are growing, they will require the help of these artificially generated datasets and synthetic data is proving to be the future of these businesses in need of quality datasets.

Author: Irtiza Zaman