The technological context has found great strength in the business world. To think of resources without the leading role of innovation is, to say the least, incoherent. Therefore, a combination is essential to make this happen: data science and artificial intelligence. It is a challenge for managers to apply this strategy only with a human eye and having technology behind it is essential to guarantee management success, bringing competitiveness and performance.
That's why we've prepared a focused and objective article with the main points involving the connection between these two intelligences to boost your business. Read on!
What is data science and artificial intelligence?
Data science is a field that applies techniques to the study of collecting, storing and processing information. Nowadays, businesses require the analysis of indexes, metrics and numbers that reflect the health and development of a project. Another particularly interesting point is the boom in the mass use of smartphones and access to websites, social networks and applications. All this generates a considerable number of transactions and interactions both online and offline.
Data science aims precisely to study these interactions. A clear example is the transformation of the customer journey, which includes counting accesses, site behavior and the path to the purchase decision. Key characteristics such as:
- age;
- gender;
- schooling;
- social class; among others.
If data science is fundamental, artificial intelligence is not far behind. In this technological scenario with advanced information analysis, having equipment that can respond to stimuli with pre-programmed actions is an important asset for generating value for a business.
Based on algorithms, it is possible to identify variables, as well as interpret and solve problems. Thus, a machine is able to simulate human actions, mainly mechanical and operational. Logically, actions that involve analytical reasoning are not supported, but repetitive processes that are relatively easy to structure are perfectly programmable, allowing machines to speed up routines and eliminate errors.
How are data science and artificial intelligence related?
Let's think about two key questions to understand the relationship. Firstly, how do we collect, process, store and analyze data? The technique for this is to apply data science. Now, how do we use this data in a way that is relevant to a business? The answer is through artificial intelligence. In other words, while data science is more theoretical, AI is more practical for the business.
What's more, while data science will be useful for strategic planning, artificial intelligence will be important for automating processes. The latter will be more effective when the former is well applied, you see. Now, let's look at the practical benefits of this combination.
The decision-making process evolves
Data science works specifically to improve the knowledge base of the business. At this point, the manager gains more scope to assess and identify risks in order to make the best decisions.
Artificial intelligence, on the other hand, will speed up the flow of processes, giving more freedom to think about issues of complexity, creativity and innovation. In this way, managers can integrate operational issues more easily, without worrying about the mechanical side of a business.
Evolves from empirical to analytical method
Combining the two concepts also tends to improve the analysis method. Not that the empirical method has no value, but with a large amount of data to analyze, considering experience and observation alone can limit decision-making power. Technological data science and artificial intelligence tools, on the other hand, add exceptional value to this decision-making potential. They also enable managers to combine the two techniques to make the best possible decision.
How can these concepts be applied to a business?
Applying these concepts to a business essentially involves adopting professional technological solutions. Again, combining knowledge with techniques and innovations is what will make the business more competitive and more developed.
It's also worth adding a focus on innovation to the business policy and getting the team to prioritize technological methods for management. At this point, tools become crucial for adding value and raising the level of management excellence.
Practical applications of data science involve analyzing markets such as finance, industry and web traffic. As well as statistics in sectors such as retail, education or economics, understanding numbers and turning data into valuable information.
Practical applications of artificial intelligence involve robotics, autonomous cars and process automation technologies. The aim is to make devices work in a self-programmed way and return routines more quickly, qualitatively and without errors.
What can we expect from this combination in the long term?
The use of data science and artificial intelligence in companies is expected to grow over the next few years. This is because they are cutting-edge technologies that tend to underpin any business over time.
All you have to do is look at consumer behavior and you'll see how much progress has been made in recent years. Smartphones and the web dominate actions, and this technological combination generates an absurd amount of data and information.
Covid-19 itself has shown a great advance in online technologies and has pushed companies to adopt more and more digital solutions, both for survival and for business evolution.
In this scenario, business solutions are focused on adding value in data management and automation. Studies are progressing all the time and various solutions are emerging exclusively in the digital environment, such as:
- e-commerce;
- digital marketing;
- business intelligence;
- advanced data analysis;
- big data; among others.
All these technologies only attest to the validity of solutions such as those applicable to data science and artificial intelligence. It is important for managers to adopt and maintain continuous study so that their businesses know how to implement these technologies.
We have reached the end of our article. It is worth reflecting on the relevance of data management in the context of technology, so this connection between data science and artificial intelligence is crucial to achieving excellence.
Shall we take it to the next level? How about sharing the impacts of the combination of data science and artificial intelligence on your social networks?
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