It has often been said that the world of modern business sleeps for no one. In the same respect, it is not uncommon to experience bouts of insomnia when we consider just how complex the associated digital processes have become.
Are there any ways to navigate through this veritable conundrum of algorithms, AI-driven insights, and complicated batches of coding? How can we verify that the information itself is correct? We recently scratched the surface with the help of Marco Steiner.
Mr. Steiner is no stranger to the digital ecosystem, and he possesses several accolades which pay testament to his expertise. Not only does he boast more than seven years as a senior business analyst, but his knowledge of the latest online payment ecosystems provides him with a streamlined perspective on where things may be headed. When paired with a Bachelor of Arts in Economics from Vienna University, Marco Steiner is far from a novice. What insight can he provide?
The increasing prevalence of automation
There is little doubt that the most profound advancement in relation to digital data processing is the role that artificial intelligence (AI) now plays. Not only are these systems present on a macroeconomic level, but they have become pervasive when referring to everyday online interactions. Hyper-personalised search engine results, chatbots powered by generative artificial intelligence, and intelligent product recommendations are some examples.
Human-in-the-loop models
"However," Marco Steiner emphasises. "These systems are not without their own shortcomings. Back-end processes have become extremely complex. It's no longer a question of learning basic code. Programmers now have to develop an innate familiarity with the flexible nature of AI itself."
This is why Marco Steiner prefers the Human-in-the-Loop (HITL) approach. Such a model keeps humans directly involved; even if they may only monitor from the proverbial sidelines. The main benefit here is that small issues can be found before they devolve into time-consuming (and costly) concerns. For instance, data flagged by an automated software algorithm may be examined by a human counterpart to ensure a greater degree of accuracy.
The importance of contextual data
Marco Steiner goes on to highlight the issue of contextual data in relation to fact-checking processes. He rightfully chooses to use his own online research as an example.
"My studies have already been featured in a number of white papers related to the latest online e-commerce trends. This is important for two reasons. Firstly, it provides the research with a greater sense of transparency. Additionally, the information can be easily verified with the click of a button. This is the key when presenting a sound argument; especially if the data is not known to the general public."
Furthermore, checking information is no longer a matter of simply copying and pasting a string of data into a search engine. Transparency requires multiple verifiable sources. When discussing how complex the digital community has become, nothing less will suffice.
Client-side concerns
Another critical point that Marco Steiner rightfully highlights is how complex data will inevitably impact end-user behaviour. He cites the online banking community as a typical example.
"Customers aren't keen to become involved with long and complex registration processes. They don't want to be forced to adapt to an entirely new user interface simply when opening an account. The name of the game is streamlining, and this can be a challenging balance to obtain when dealing with advanced digital processes."
Marco Steiner instead feels that organic analyses need to be used in tandem with automated systems. He states that one is not necessarily better than the other. Each offers its own set of unique advantages, and a handful of possible drawbacks. In other words, why not embrace the best of both worlds?
Practical advice
Marco Steiner stressed that he did not want this informal interview to come across as a technically oriented white paper. He appreciates that average individuals instead want to get to the heart of the matter.
"I've recently spoken to several of my followers who expressed concerns with how complicated online data has become," he cites those who have subscribed to his professional networking profile as a prime example.
"These aren't necessarily tech-savvy users," he says in a slightly apologetic tone. "They are people who have become rightfully concerned about the ways in which complex data is being used. They are particularly worried about the collection of personal information, misleading marketing techniques, and identity theft. Unfortunately, all of these instances are on the rise."
So, are there any steps that can be taken to simplify things? Marco Steiner was able to provide us with a few surprisingly valuable suggestions.
Keep it simple
Brevity is the soul of wit. This is one of the mantras which has come to define how Marco Steiner approaches the cutting-edge world of data processing. He firmly believes that things should not be made any more complicated than they have to be.
"While coding and DevOps engineering are both complex industries, there's no reason why end-user interactions need to take on this level of confusion."
He moves on to list a handful of actionable recommendations:
- Look for friction-less end-user interfaces
- Only focus on the most pertinent information
- Businesses should place their data in discrete digital silos, as this simplifies management concerns
- Never be afraid to ask for assistance when required
However, Marco Steiner still understands that the sheer amount of information found online is overwhelming. The same holds true when discussing convoluted digital processes.
"Ultimately, I think that this represents a give-and-take scenario," he explains. "While businesses must strive to make their processes clear to the user, consumers also have a responsibility to remain ahead of the curve by keeping themselves regularly informed."
Experts agree that data is slated to become even more complex in the coming years; thanks in large part to how quickly computing power is continuing to progress. While challenges will inevitably come to light, there are also plenty of opportunities to explore.
