The top data management skills needed for jobs in AI
As a chief data or chief analytics officer, data scientist, data analyst or IT manager, you know information technology, data management and data analytics by heart. But what you may not have experience with is some of the rising technologies, like AI or artificial intelligence, for instance.
AI is definitely seeing a huge boost in popularity these days. During the tail end of 2014, a gain of nearly half a billion dollars in the AI industry occurred. That warranted calling the movement a “resurgence,” which is still happening. AI and machine learning is cropping up seemingly everywhere, across so many different industries like manufacturing, construction, retail and even finance.
But an AI system doesn't just turn on and instantly become helpful. It takes time for it to learn enough to be effective. That's why Alexa, Siri and Google Assistant have gotten better and more reliable over time. Through a combination of big data, cloud computing and machine learning technologies, modern AI systems gradually become more useful and more commonplace.
You already have the necessary skills, experience and knowledge to begin developing and working with this relatively young technology. Although AI has been around for quite some time, it’s just now really coming into its own.
With the right skills, particularly in data management, you should be able to find a solid job or career related to the AI industry.
What Careers Work With Artificial Intelligence?
It stands to reason, before you can define what skills are needed you must first understand what career opportunities are available.
Keep in mind, this is not a comprehensive list, and the market is always evolving. Opportunities will surely continue to grow. Example of careers that work with AI include:
- Software analysts and software developers
- Computer and data scientists
- Computer engineer consultants
- Mechanical engineers and technicians
- Manufacturing and electrical engineers
- Surgical technicians and physicians
- Medical health professionals and researchers
- Military and aviation developers
- Graphic and creative art designers
- Digital musicians and DJs
- Film producers
- Textile manufacturers
- Professors and educators
- Financial trading professionals
- Customer service professionals
Notice how most of these have overlapping skills and requirements, such as working knowledge of software development? Here are the top data management skills you’ll need.
1. Information Technology and Software Development Experience
The software serves as the brain of automation or robotics system, telling the machinery and platforms what to do and how to react.
Software developers, IT professionals, security experts and analysts work together to produce the software that will control these technologies. Before there was a Siri, there was a software developer's code, raw and uncompiled. If you want to work with these systems — even if you're not working with the software and developing it — you need an understanding of how it all works. You can't be asking developers and programmers for things that aren't possible. Again, that requires a working knowledge of the technology and what developers are capable of.
As a data professional with direct hands-on experience — remember, you’ve been working directly with these systems and platforms for years — you understand exactly what they are capable of. You know how to leverage the data that’s being collected, and you know how to work with a team to deploy future strategies.
In the end, that’s what machine learning, automation and AI are all about. They are systems and tools designed to reference existing data and take action, devoid of human interaction. Your direct experience working with these platforms, along with your software development and IT knowledge means you are better equipped to work on and build out these applications.
2. Operational Procedure Experience
Major retailer Amazon has installed a huge selection of automated robots in their warehouses, but they complement the human workers that already exist by making their jobs easier. Amazon's workers must know not only how to do their jobs, but how to work alongside those automated robots. Amazon also employs maintenance and repair crews to keep the systems operational.
But again, these are automated robotics we're talking about. In a factory that uses heavy-duty machinery or automated equipment systems, you better believe that there are people who keep these platforms operational. That includes interfacing with the technologies and interacting with controls or maintenance systems.
This skill description is quite broad. Obviously, there are specializations within each industry directly related to various equipment. What equipment you need to learn to work at a job or location, will depend on the systems employed.
Soon, it won’t just be big companies like Amazon that use automated robots. Eighty percent of smart systems can be recycled and an even larger percentage can be repurposed or resold. This means that when companies like Amazon want to upgrade their robotics, they’ll likely sell their older systems to other companies to repurpose. Automated robotics can then trickle down from the largest corporations to the rest of the industry.
These systems are going to be controlled by modern computing solutions which are usually powered by software and digital tools. Without working knowledge of these systems and keeping them operational, there's no way to know when a software is doing something right or wrong. All developers, especially when it comes to automated systems, need operational procedure experience in the industry they are designing for.
Data professionals can cash in on this by applying everything they know about the industry to further improve natural language processing and AI system or platform reactions. Artificial intelligence is nothing more than a data system, analyzing a treasure trove of information almost instantly to find a solution. But the trick is teaching these platforms to identify and discern the right data, and how to put it into use.
You already know how to find, leverage, and deploy this information. More specifically, you know what’s required to extract useful data, and that can significantly contribute when teaching modern AI and automation systems the way.
3. Probability, Applied Math, Algorithms and Statistics
Math is obviously a necessary component of software development and data analytics. You know that better than anyone. Working with statistics and probabilities, and knowing how to work with and manipulate algorithms, as well as applied mathematics, is crucial.
Expanding even more, other concepts like convex optimization, Lagrange, quadratic programming, equations and fractions and even summations are all necessary.
You need to be able to predict and calculate what actions an AI, machine learning, or automation platform should be employing. You have tons of experience with these math elements in standard data analytics, and data processing, so it should be a cinch to rely on them even more.
4. Unix and Related Toolsets
Unix and Linux are ideal for the AI industry because they were practically designed from the ground up for machine learning and automation. Even if you don't use the platform itself for AI, you'll likely use various Unix based tools like grep, find, cat, awk, sort, sed, cut and tr.
Automation professionals do most of their development, processing and personalization from a Linux-based machine or platform. You need to know these tools, what they are used for, and how to take advantage of them. If you’re not familiar with these tools or the Linux/Unix platform, you’re going to have a long, difficult road ahead.
5. Systems Analysis and Data Gathering or Manipulation
To do their jobs and complete the work required, AI systems, robotics, and automation platforms need to be fed information continuously. This requires not just working knowledge of data analytics and gathering but also manipulation.
It may come in a raw form, unusable and unorganized, and require a professional to extract usable information from that data. Then it must be fed to the systems and software in a usable form. Whether that is through algorithms, existing platforms or adapting the data and converting it depends entirely on the task.
You will need experience and knowledge for all these things. But as AI and data management become an increasingly important part of more and more industries, these skills will be useful well into the future.
These systems will serve as the brains for robotic process automation and AI platforms. Without them, the machines and systems would have absolutely no idea how to find, utilize, or organize various data points and sets.
Since you already have plenty of experience with systems analysis and data gathering — or even organization — you can help manage the systems that will facilitate and handle data. Frankly, without professionals like you, modern AI and robotic automation tools will never get anywhere practical.