By Jon Reilly, co-founder and COO at Akkio, working to level the playing field for AI in business.
Data engineers have an important job of transforming data into valuable insights for businesses.
Given the exponential growth of big data, and the ability of data engineers to manage and manipulate this data, data engineers are essential to a company’s success.
Challenges Of Data Engineering
That said, data engineering is far from easy.
The more data you have, the harder it is to make sense of it. After all, the average person can only focus on four pieces of information at a time. Even if we’re told that “data is the new oil,” more data also means more complexity.
One major obstacle is that the infrastructure needed to handle the data is costly and not available in most organizations.
This is particularly the case when building artificial intelligence (AI) models, which require a ton of computational power and specialized infrastructure. For instance, when building models for complex tasks like fraud detection, the size of the data required is huge. When you factor in the cost of renting hardware from cloud providers, model training and retraining and deployment, it can get very expensive.
Not only is it costly, but it’s incredibly time-consuming, with 40% of companies taking longer than a month to deploy a single model into production. Engineers need to spend a lot of time just sifting through the data, building pipelines and doing other tedious tasks.
To make matters worse, data is often unorganized and siloed, which means that it’s hard for teams to collaborate on analyzing the data.
Further, this level of complexity means that many less-technical people are at a loss when analyzing the data, and they can’t help but be overwhelmed by the sheer volume of information. As a result, AI remains out of the reach of many small businesses.
Indeed, while over 70% of medium-sized businesses use AI and machine learning, only 13% of very small businesses do. This gap indicates that traditional AI systems are still too complex for many businesses to use, which leaves them at a competitive disadvantage.
No-code AI is one tool data engineers can use to make their work easier and enable businesses to achieve a competitive advantage.
These tools are easy to use and require little training. They also work on any data and can be deployed anywhere, which means data engineers can use them across industries and company sizes.
By automating the tedious aspects of data engineering, no-code AI lets teams quickly and easily build and deploy AI models, which in turn allows for greater agility and more time spent on innovation.
After all, AI is just a tool, not the end goal. With better tools, you can focus on what matters most: the insights and value you’re creating for your business and customers.
No-code AI is being used to achieve competitive advantages in many areas, from sales and marketing to finance and cybersecurity.
For instance, in the financial sector, no-code AI is being used to automatically scan transactions and detect signs of fraud. This, in turn, helps employees by freeing up their time to do other tasks — and it also helps these companies manage financial risks more efficiently.
Moreover, marketers are using no-code AI to better understand when customers are about to leave and turn to competitors. That way, these businesses can proactively engage with their customers before they walk out the door.
In the sales industry, no-code AI is used to find high-conversion customer leads, or even find customers who are disappointed in the solutions offered by your competitors, so you can then reach out to them with your offer.
Data engineers can help organize and source the data that fuels these models, helping businesses achieve competitive advantages.
The benefits of AI include increased productivity, decreased human error and lower costs. By automating repetitive tasks, data engineers can help businesses focus on actions that truly move the needle.
The world of AI is constantly evolving and companies are always looking for new ways to use it to benefit their business. Data engineers can use no-code AI to make businesses more competitive and achieve success.