Gone are the days when you needed a six-month course or a degree in IT to use a piece of software. Today’s business analytics apps are designed for the average Joe, and there’s nothing derogatory in the term.
Of course, low or no-code apps aren’t inferior to those with cumbersome UI, quite the contrary. The more time it takes to program the software, the more time you actually waste. However, this isn’t the only benefit and that’s why it pays to take a closer look at low or no code self-service intelligence.
Self-Service Intelligence Disambiguated
This kind of business software solution is designed to be used by those with low or no technical knowledge. The lack of knowledge here refers to the specific skill-set to properly gather, classify, and analyze the data.
Up until recently, large enterprises and some budding start-ups needed to hire an entire team of skilled professionals to provide data that makes sense. Hence, data mining and analysis specialists have become a hot commodity due to their unique ability to gather and interpret information.
Nevertheless, the demand for actionable information applies to the companies that can’t afford a million-dollar team of tech buffs. And this is where low or no code self-service intelligence comes into to its own. Once this kind of software is implemented, it pretty much runs on autopilot.
That being said, these apps require monitoring, services, and some human input. But that’s nothing compared to staring at endless lines of code or numbers and trying to figure them out.
What Are the Desirable Features?
Due to the increased demand, there’s a lot of self-service intelligence software. For that reason, it might be tricky to determine which one fits your business the best. The suite of features may vary based on the industry, but all great self-service intelligence shares certain characteristics.
First of all, it should provide a comprehensive Cloud-based UI. To make things clear, all the main features should be accessible from a single dashboard and via different devices that pair with the software. Plus, it’s best if a single platform is designed to handle different kinds of workflows.
Without going into technicalities, this means that software architecture is scalable and customizable as per enterprise needs. But above all, this kind of software offers the so-called snap-and-assemble pipeline.
This indicates that there’s no manual integration of auxiliary features and functionality. Meaning, the users don’t need to code or can copy and paste lines of code with minimal knowledge of the software architecture.
Most people associate ML (machine learning) and AI (artificial intelligence) with advanced robotics. However, these tools are invaluable for predictive analytics because they have the ability to spot trends, automate, and adapt on their own.
To hint at the power of ML and AI, you should take a look at Wikipedia editor bots. These pieces of AI software have gone to war with each other trying to make the open-source encyclopedia better. And no, there wasn’t any human input that sparked the fight.
The bottom line is, with continuous input and some competition AI evolves and becomes more efficient. This takes time and monitoring but the bottom line is the same – companies will be able to be more profitable, resource-efficient, and deliver better products.
Are There Any Downsides?
With low or no code business intelligence, the main challenge is to educate the users to request the data. In other words, the output will be as accurate as the initial query. However, the time it takes for a user to get proper training has been cut to a minimum and it might not be dependent on specific professional competencies.
In Data We Believe
With the rapidly expanding application of AI and visualization tools, low and no code intelligence is here to stay. Some even go so far to predict that it’s going to replace the majority of current manual tasks. But the truth is digital business warriors still need some human input to work as they’re supposed to.