Here is the new article written by our Director and Co-Founder Javier Orús Lacort for the Economy and Business section of the Heraldo de Aragón, published on February 6th, 2022.
We hope you like it!
We reproduce here the published text:
“We have to do something about the vast amount of data we have in-house that we’re not taking advantage of – this can’t be!”.
Surely these words resonate again and again in many companies, usually to try to motivate the use of the data they have, to be able to make better decisions and thus manage and develop the business in a more accurate way.
Analyzing those initial phrases themselves, they are more “reactive” than “proactive”, that is: instead of having a clear strategy for leveraging data aligned with the company’s strategy and objectives, it is more a completely natural reaction to perhaps having the feeling that the company is “missing something”. It is clearly identified that data should be leveraged much more in the era of Artificial Intelligence and Big Data, where precisely everyone is talking about the importance of data and the value it brings. It is a very normal reaction, very human and fundamental to be aware of the need to do certain things differently, but it can lead to decisions that are not the best way to really take advantage of the full potential of data.
Another phrase that comes next is often the following, “We have a lot of data: what can we do with it?”. Again, not the best possible approach to get the most benefit out of it. Let’s put an analogy: if we go fishing with a trout rod in an area where there are tuna, well, we will catch neither one thing nor the other. If we try to “fish” for information in a sea of data without being clear about “what” we are looking for, and above all “what” we are looking for, we will be rather lost, as if we were rowing aimlessly. We can spend a lot of effort completely in vain, without reaching a good port. Analyzing data for the sake of analyzing it does not usually give good results. It is like looking for a needle in a haystack.
First: What business objective are we pursuing?
To apply Artificial Intelligence and Big Data to the business with the greatest possible common sense, the first thing to do is to determine which business objectives of our strategic plan we want to achieve. These business objectives can be, for example: optimizing the supply chain, optimizing the operation of production lines or maximizing sales.
Second: What data analytics help us achieve the stated business objective?
Once the business objective has been set, we can then consider what data analytics can be carried out to help us achieve these business objectives. For example, for the objectives discussed above, these could be the following analytics, respectively: demand forecasting to optimize the supply chain, predictive maintenance to anticipate machine failures and prevent them before they happen, or an analysis of
360-degree customer service to meet their needs in a much more personalized and effective way.
Third: What data can we use for the proposed analytics?
After having set the objective and the analytics to be performed to achieve it, then it is time to pull the thread and determine what data we are going to analyze and how. Nowadays, this data can be both internal and external to the company, providing value to both, depending on the case to be made and the business area in question. This is very different from the initial approach, where we could have a large amount and variety of data, but where many of them could contribute nothing to the strategic objectives we have.
Fourth: Which technology is the most convenient to carry it out?
The next step is to determine which technology is best suited to address the resulting data analytics. This avoids “killing flies with cannons” by committing to the right technology that truly adds value to the analytical and business process, fully adjusting to the needs of each case.
So, to truly capture the value of data in our companies, we have to follow a certain methodology to ensure success. We will not be inventing anything new, but performing the analytical process with as much common sense as possible.
Artificial Intelligence has a lot of “intelligent” in it, hence its name, but issues such as “common sense” cannot yet be fully entrusted to it, and that is what we are here to provide.
