During the past decade, the rapid increase of computing and processing power has elevated artificial intelligence (AI) and machine learning (ML) to an all-time high. The constant improvement of this technology has taken the digital world by storm, reshaping our understanding of digital tech.
Nowadays, the automation of most software processes, our ability to analyze and extract information from metadata banks, and the ability to predict or simulate scenarios are possible thanks to the implementation of AI and ML.
Additionally, these AI-related processes can be carried out faster and more efficiently than ever.
All of this may sound like the sort of very complex subject matter usually reserved for computer engineers and MIT students. However, AI has become a vital part of all our lives.
For example, machine learning plays a big role in health care and other essential services, as well as having more mundane uses like the Netflix algorithm that recommends what we should watch next.
Although AI and ML technology are now more relevant than ever, however, not many people realize how these seemingly impossible things came to be. The main player behind all these advancements is none other than synthetic data.
What is synthetic data?
It may sound like something Neo would say in the Matrix, but in reality, the nature of synthetic data is easy to explain. Put simply, synthetic data is fake data. For those looking for a more complex answer, we could also tell you that synthetic data is data generated by a computer or created by an AI. Actually, that might be the better answer.
Some of its more scientific applications can be found in fields like telecommunications or physics. Popular software programs like Matlab implement very complex algorithms that allow users to run simulations predicting the behavior of a system, allowing them to test various designs and models without leaving their desks.
Synthetic data can have many different applications. Outside of the scientific community, it’s used to train ML software and AI-powered processes. Most businesses are starting to implement these technologies to automate various aspects. These are some of the more common examples.
Synthetic data and its real-world uses
(Synthetic) data mining and data analytics
Synthetic data packs are used to train automated data mining and data analytics software systems. Before delving into the importance of data mining and data analytics in today’s digital ecosystem, it’s important to recall the main differences between the two.
Data mining experts use mathematical algorithms to find patterns and structures within pre-existing data. Data analytics specialists, however, do not require such techniques, since the data can be structured, semi-structured, or unstructured.
Although these two data-centric fields are not the same, both use synthetic data to develop their respective systems. In the case of data mining, software developers use realistic-looking synthetic data to recreate patterns that resemble those found in real-life data banks.
How is that possible? Well, synthetic data packs are generated according to the distributions of real data, respecting the already existing correlations between variables. Thanks to these data-generation methods, the synthetic data is virtually identical to the real one.
The result is AI-generated data that can be used to train data mining automated software.
On the other hand, data analytics uses synthetic data as complementary data that allows developers to create more robust datasets, filling in the gaps left by missing information.
Cybersecurity
AI-generated data is also commonly used to test and train fraud detection systems and cybersecurity programs. Nowadays, data has become a form of currency for most companies. As a direct result of this data-centric shift in e-commerce, investing in strong data security measures is a priority for digital businesses like telebanking companies.
These systems are usually built using synthetic data in order to teach a system how to react to certain situations, like a security breach. If you’re a cloud storage company, you can safeguard yourself against a data breach by implementing automated cybersecurity software.
However, in order to prepare these programs accordingly, security companies are not allowed to test their AI using real data. To safely follow the official protocols included in the GDPR (General Data Protection Regulation), cybersecurity software developers use synthetic data that resembles the type of intel hackers may look for i.e. banking details, personal info, and so on.
Due to its purely artificial nature, synthetic data holds no real information and cannot be traced back to any clients or companies. This way, synthetic data helps developers to reduce risks during the early stages of development, since this is when the software is most vulnerable to a potential cyberattack.
Automated marketing
Although the correlation between synthetic data and marketing may come as a surprise to some, the truth is that artificial data is not new to marketing. Sellers have been using synthetic data since the 80s and 90s – they simply gave it a different name before i.e. buyer personas.
The term ‘buyer persona’, or ‘user persona’ as it was originally called, was coined by American software designer and user experience expert Alan Cooper. In essence, a buyer persona is a fictional or imaginary representation of a potential client. These profiles are designed using real-life data but do not necessarily represent real people.
Cooper came up with this concept in the early 90s to help fellow programmers develop more easy-to-use software. Computing was in its infancy, and most programs lacked intuitive designs. To do so, Cooper created a fictional client named Kathy – an average user without computing skills.
Nowadays, due to the implementation of AI-powered software, marketing and synthetic data have a very different relationship. Advertising has been one of the many areas of business to shift towards automated processes. In the digital era, automation has become one of the key ways in which developers and marketers can collaborate.
Many AI-powered marketing automation software programs retrieve customer data in order to develop successful marketing strategies across various channels. However, to automate such processes, this AI-powered software needs to be trained using synthetic data.
Despite the technological advancements that separate both techniques, this approach is little different from what Alan Cooper did: using fake customer profiles to create different demographic targets.
These marketing automation tools can help companies with a social media presence boost productivity and reach more leads by creating social media posts aimed at different targets.
However, in order to do so, developers first give AI an input of synthetic data. Through ML processes, “these robotic advertisers” decide when, where, and how to create social media posts.
And that’s not all. Synthetic data has many other applications in the field of automated marketing. Personalized data-driven email marketing, for example, uses CRM (customer relationship management) and customer journey data in order to develop emails aimed at specific clients.
However, due to GDPR, synthetic data is needed in order to run detailed simulations that improve automated marketing tools. This way, both developers and marketers can protect the customers’ privacy and avoid tampering with real-life customer data.
From marketing to sales
Much like its brother department, the sales department implements various forms of synthetic data to boost productivity. Sales teams can maximize this by using predictive dialer technology. The best VoIP service providers will always include free predictive dialer features within their packages.
These predictive dialers are based on complex mathematical algorithms that allow agents to place phone calls faster and more efficiently. This pacing algorithm lets the dialer predict how many calls should be placed according to when the next agent will be available.
Like with any other predictive algorithm, synthetic data is used to train and develop the system’s predictive capabilities.
Customer service
Voice-over internet protocol phones are not only used by sales teams. Customer service departments also utilize the services offered by business VoIP providers. These phones are very useful for attending to the needs of customers in the B2B industry (since VoIP phones can be used from anywhere and at any moment).
Most modern call centers use features like IVR (interactive voice response applications). These tools are especially useful for visually impaired clients because they’re voice-activated. These are built using complex databases and are handled by cloud providers.
An interactive voice response automated phone system allows callers to access information by interacting with pre-recorded voice messages. This process is completely automated and does not require an agent.
As with any other AI-based service, these automated processes are built by implementing synthetic data that helps to develop and train systems.
Additionally, digital businesses that operate via app-based services can offer excellent IT support to clients by adding mobile application management (MAM) to their services. This is especially useful for companies looking to offer 24/7 customer support because MAM facilities give IT staff access to apps remotely and instantly.
Unlike other remote-control technologies, such as mobile device management, MAM only focuses on managing specific apps. This means admins can work within applications without needing access to the entire device, ensuring the IT process runs as smoothly as possible.
Another popular use of synthetic data in the customer service field is developing strong AI-CRM tools. CRM stands for ‘customer relationship management. This automated process tracks and manages how a company interacts with leads and pre-existing customers down to the point of sale system. The data is used to analyze strengths and weaknesses and boost customer retention and sales rates.
However, AI-powered CRM software needs to be trained and developed using pre-existing data. But how can a CRM software developer sharpen its system without such data?
Here’s where synthetic data comes into play. In order to train this artificial intelligence system safely, synthetic data is used as a mock-up or stand-in for real-life data. This makes the machine learning process faster and safer.
Popular open standard software-based PBX platforms like 3CX implement CRM tools to manage how companies interact with customers. A PBX (or private branch exchange) system is a communication network via telephone that operates within a company and establishes a direct line of communication with clients.
Such tools are ideal for digital businesses looking to provide excellent customer support.
Product management
As we previously mentioned, synthetic data is especially useful when dealing with tedious and complex matters like data analytics. Although most people have come to think of data analytics as a tool to observe consumer trends, click-through-rate statistics, and conversion rates, the reality is that data analysis is a versatile tool for businesses.
Inventory tracking is one of the biggest pitfalls for most retail companies and e-commerce businesses looking to sell products online. With the rapid rise of internet shopping, both huge corporations like Amazon and smaller businesses have been forced to keep a close eye on their inventory.
Excess stock and insufficient merchandise are equally problematic. Companies want to find a nice balance between overselling and running out of high-demand products and investing too much cash into slow-moving items that don’t sell.
To do so, shops must implement inventory management software. These tools can help businesses plan ahead, using vast databases that help retailers understand the fluctuations in customer buying patterns.
As with any other data analyzing tool, inventory tracking apps are fed with synthetic data in order to sharpen their efficiency.
Synthetic data means money
As you’ve seen, AI-powered automation and machine learning software have taken over most areas of business. However, it’s important to acknowledge that none of this would have been possible without the vital assistance of synthetic data development.
Synthetic data has helped many digital businesses to make money, but the reality is that synthetic data itself can represent a prime asset. Investing in research regarding this could open the door for many software developers looking to branch out and learn new skills.
If you’re wondering which developer career path you should choose next, consider adding synthetic data to your list of know-hows.
