In our adolescence we think that a perfume or a new shirt will be the key to our success. When we grow up we realize that this is not the case. Or maybe not.

It is not unusual to see companies and organisations invoke AGILE methodologies, the use of AI or being Data Driven as a guarantee of success. I wish it were so easy.

Let’s look at some characteristics of good use of Big Data with AI, but don´t forget  that nothing guarantees success, but everything helps to achieve it.

The term AI was first used in 1955 and was a constant obsession during the Cold War. The first time the term Big Data was used in its current sense was in 1989. We are not talking about two new trends, but about lines of work that are more than thirty years old.

The increase in hardware power together with the explosive growth of data thanks to social networks and the internet of things has meant that both disciplines have become more important and have come together to provide better results.

As is often the case when the same topic is discussed everywhere, we are quick to jump on the bandwagon so as not to be left behind. Afraid to be the first, but eager to be the second to try “the new thing”.

Melanie Mitchell explains in her book “Artificial Intelligence. A Guide for Thinking Humans” that AI is a field of study that periodically gains notoriety only to be relegated and covered in accusations of unfulfilled promises. It is what he calls the AI Springs. In each of these springs, great advances are made, but then it seems that nobody wants to know anything about AI. Are we currently living through one of these springs? Some say yes. Are there breakthroughs being made? Huge.

Let’s mix

What AI can do with data is easy to imagine: it will process it much faster, it can find patterns more easily, it has the capacity to propose new solutions, products and services, and properly trained it can help us improve databases and make predictions with greater accuracy.

The flaw is that, to date, AI is still suffering from hallucinations and does not distinguish the quality of the information it handles. In fact, this leads some experts in the field to claim that we cannot speak of intelligence, but rather of advanced processing. It is a fascinating discussion, but today we are here to discuss other issues.

The data culture has been trying to enter organisations for years, and some people keep the door closed to it. The reasons often refer to the inaccuracy of the data, the lack of understanding of the reality of the organisation, or the need to be intuitive when deciding where to go from here. As a plot for the old Wall Street executive movies of the 1980s it may be appealing, in 2024 it is untenable.

We have never had so much data at our disposal. The problem is that we cannot use it as if it were a quarterly report. We need to embed a data culture throughout the organisation and have specialised staff to help make the most of and return on that material.

Let’s look at an example.

With the emergence of social media came analytics reports. Those reports indicated what had the most impact on the audience and many thought that was the way forward. When the social media analytics met the analytics of an online shop, it was not uncommon to find that the “applause” did not always translate into sales and that the best sellers were often received by the public in a discreet way. 

The difference? We finally knew for sure. An online shop knew that it positioned itself and also that it generated profits. Moreover, we were seeing both changing at a considerable speed. This led to the adoption of methodologies that allowed us to “navigate” this speed without the organisation becoming consumed by stress. 

Agile

Agile methodology leaves the world of software and reaches companies and all types of organisations. It is necessary to know how to move fast and be operational and efficient without losing sight of the objectives. A company should not change to improve statistics, but to increase profits. 

With these premises, improvement and excellence cease to be the glorious end of a period of silence and concentrated work and become a constant path and a way of being day by day, contacting the target public, adapting to the demands and needs and testing the limits.

But with data. 

With processed data.

Data processed quickly thanks to AI. 

Benefits of Working with AI and Big Data for Enterprises

Let’s list some of the most obvious benefits of this combination of tools:

Improved decision making.

The combination of AI and Big Data allows companies to analyse huge volumes of data in real time. This provides more accurate and relevant insights, making it easier to make informed and strategic decisions. By relying on hard data, companies can reduce uncertainty and increase the likelihood of success in their business strategies.

Optimising processes and resources

AI tools have the ability to automate repetitive tasks and analyse workflows to identify areas for improvement. This includes processing data in real time, allowing Big Data to be brought into the enterprise with less resource consumption. This frees employees from routine tasks, allowing them to concentrate on higher value-added activities.

Detailed information coupled with rapid analysis will give us a highly improved and analysed version of the resources consumed by our organisation, resulting in a greater possibility of improvement.

Personalisation of customer service and marketing

AI and Big Data allow companies to analyse customer behaviour and predict their needs. With this information, companies can create highly personalised marketing campaigns and significantly improve the customer experience. This not only increases customer satisfaction and loyalty, but also improves conversion rates and sales.

Product and service innovation

By analysing market trends and customer feedback, we can identify new opportunities to develop innovative products and services aligned with market demands. AI and Big Data foster continuous innovation and ensure that the company remains competitive, always staying ahead of consumer needs and wants. At no point can it become a frantic race. Remember, it’s not about increasing statistics, it’s about increasing profits and business goals. The tools can be powerful, but they must not divert us from what we want to achieve.

Predictive capability.

Identifying potential risks, detecting fraud patterns or security threats in real time allows companies to take preventive measures in a timely manner. This not only reduces the risk of financial loss, but also protects the integrity and reputation of the company.

This capability also anticipates future trends and market behaviours. It will be easier to plan strategically and effectively, adapting and responding proactively to market changes. 

In his book “Leading Change”, John P. Kotter wrote: “…. more and more organisations will be forced to reduce costs, improve the quality of their products and services, identify new opportunities for growth and increase their productivity”. He wrote it in 1996, but he could have written it this morning.