As you may already know, the amount of data that we create, and store, as human beings has been growing immensely in the last few years. We start having more and more devices that can create, send, store and save data – we can just look at our mobile phones, and how powerful they have become in the last few years.
One study is showing that the amount of data, on a global level, will reach 175 zettabytes (ZB) by year 2025 (just as a fun-fact, and for comparison: 1000 Terabytes = 1 Petabyte; 1000 Petabytes = 1 Exabyte; 1000 Exabytes = 1 Zettabyte). Obviously, this is a lot of data.
Data can be considered everything that one business creates, or even an individual (from basic stuff like pictures, documents to a more complex calculation, and similar). First question here, obviously, is how to store data – in majority of the cases, big data centers have already covered that. Second, big question, where AI can be of great assistance is how to navigate that data, search it, and predict what’s needed. We’ve had a bit of this topic covered in the following article that you might find interesting, also – Data and SLA Forecasting.
AI can help out tremendously with any business that already has a large amount of data – via proper algorithms you can train AI so, in time, you get more accurate results, and predictions. For example, this can be applied to even simpler tasks that can be automated, and predicted, in time: installing large batches of applications, or gathering the system requirements, and similar.
Also – perhaps you want to expand your company department on a different continent: the data you have gathered, and created so far with the existing department, can be fed to the AI algorithms so it can spin it, and spit out the needed model and/or requirements that would be ideal, and optimized for the department that needs to be created on a different continent.
Even the word ‘data’ by one of its definition goes as follows: ‘data = facts and statistics collected together for reference or analysis’. This, by itself, already demonstrates that once analysis is done, certain result is being expected. This is where AI can be of great assistance, especially if you are dealing with large amounts of data – the more data you feed the AI with, the better are results, especially with time.
AI Strategy and Implementation
To battle with these amounts of data efficiently, and also follow the trends (let’s remember – ‘Artificial Intelligence’ and ‘Machine Learning’ are all the hype, and buzz words), companies do employ, more and more ‘Chief AI Officers’ or ‘CAIO’. This, indeed is a big leap forward since, up to very recently these positions weren’t existing at all. Usually you had ‘Chief Strategy Officers’, ‘Chief Business Officers’ and similar.
This meant that, on the executive positions at the companies across the globe were, primary, business-driven and oriented people with very little, or no knowledge of IT and the current trends – primary the business. However, this is changing rapidly and, with the introduction of these, and similar roles, now we have tighter integration of IT and business, meaning that companies are seeing the obvious benefits which these positions can bring to their businesses.
These positions can usually create an excellent bridge between the essential business needs and optimization of the data. For example: if fed, and trained, with the proper data, AI algorithms can decide/predict the best course of action if implementation of a newer version of software is needed. This optimizes a lot of processes and saves a lot of man-hours. Of course, AI is not the solution for every business, and every situation, nor it is creating certain positions necessarily redundant.
All of this does require a lot of planning, and analyzing of the business itself, alongside with the data that we have on hand, and the data that has been created thus far. In Rubik’s Code, as part of our services, we ensure that we evaluate the needs of our clients, and needs of business and based off of those, educated findings, we provide the best possible solution for business – may that be in a form of a software, or AI solution.
AI as an analytics tool
Overall, as presented so far – AI implementation is, primarily, another extremely powerful tool that can be defined as another class of analytics tools and can save up to a lot of man hours, while providing more precise results, and possible outcomes, eliminating the possibility of human error, which all of us are prone to. Depending of your business, AI department or rather – strategy – can be found and integrated within the different departments – most probably where you have the most needs. Training the AI algorithms, also, is not an easy job as the data that the algorithms are being fed needs to provide a certain value in return (may that be a monetary value, or something different, related to your business needs).
With that in mind, it is not a necessity, or a must, to have AI analytics implemented across the whole company, but rather there where it makes the most sense: where you have large amounts of data, or where it simply is going to have the best ROI (ROI = Return of Investment). After all, when looked through the eyes of the business – if it doesn’t add any value, or provides something in return – there is no need for certain implementation, most probably.
Spectacular growth of data isn’t showing any signs of slowing down. Number of people devoted to analyzing it can’t, simply, exponentially grow alongside with it. Therefore, certain intelligent automation, and analysis needs to take place here, and that seems to be an AI-based solution.
Thank you for reading!
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