Technological decisions are no longer made on intuition alone. Today, the data-driven decision making allows them to be backed up by real metrics. Data for decision-making is a strategic asset, so managers must know what data exists, how it is collected, how it is stored, how it is protected, and how it is used.
You don't need to be an expert, but you do need to understand the basic concepts and Identify what problems need resolving before opting for any solution. To do this, train yourself with programmes such as Software Development Engineering, Bachelor of Computer Entrepreneurship.
The importance of managers understanding technology
Technology, beyond being a support for any business, is key in business development. Nowadays, decisions are made with the help of artificial intelligence, cloud systems are used, or tasks are automated in all sorts of departments, not just IT. On the other hand, it also influences how customers perceive the company, increases profitability, redefines internal competencies, and demands new professional profiles.
A manager must be capable of Identify real risks and opportunities, to clearly communicate needs to technical experts and ensure that the technology used is well aligned with the goals of the company in question. For example, if RPA is chosen for implementation, the work does not end with simply installing software, role changes are also needed, knowing what the return on investment will be, etc.
Understanding technological language without being technical
Many managers feel intimidated by the technical terms And you don't need to be an expert, just understand enough to make strategic decisions. Cloud computing, Artificial intelligence, machine learning, cybersecurity, RPA and blockchain These are some of the concepts that every manager should know.
1. Cloud Computing
In this scalable service delivery model, computing resources are rented rather than purchased. For example, a Startup uses cloud can reduce the initial investment and increase their infrastructure on demand rather than buying servers.
2. AI and Machine Learning
AI and machine learning-based systems learn from data in order to to make decisions or predictions. An insurer could calculate policy risks faster and improve its pricing, making it personalised.
3. Cybersecurity
Cybersecurity is practices intended to protect systems and data from attacks such as phishing. To achieve this, a company can invest in running security drills that help reduce the gaps by 70%.
4. Automation and RPA
These software they perform repetitive tasks automatically. For example, a bank can automate the management of routine claims so that staff have spare time for other tasks most important for the business.
5. Blockchain
This distributed ledger technology cannot be easily modified, which allows secure transactions that all types of businesses can take advantage of. Logistics companies use blockchain to improve traceability and, therefore, customer confidence.
Understand the business before using technology
A fairly common mistake is adopting technologies simply because they are “trendy.” However, a good manager, whether male or female, should first ask themselves What problem are you trying to solve? And what data to use for decision-making. Opting for highly advanced technologies or ones not suitable for the company's needs can end up being an unnecessary expense or making things more complicated.
This framework helps improve data-driven decision-making, allowing technological opportunities to be identified and impulsive decisions to be avoided, discarding impulsive or poorly aligned decisions:
- What is the real business problem or need?
- How does this problem affect the results or customers?
- Is technology X the best way to solve it?
- Who will be involved in this decision?
- What resources will we need (time, money, staff)?
- What will the expected return be and in what timeframe?
- What are the technical and human risks involved?
- How will we know if the decision was a success or a failure?
From here, it's worth remembering that one decision or another also implies responsibility: which processes should be automated and which should not, how client data and privacy are protected, what consequences their use may have, etc. We're talking about Technology governance when we refer to the parameters under which the organisation's technology is used: standards, responsible parties and processes.
The role of people in technology
It is important to note that technology does not work on its own, but rather it depends on the human team that uses it. For a manager, this entails paying attention to talent management, fostering a culture of innovation, and managing change.
1. Talent management
It is necessary to hire people with technical skills and combine them with strategic roles, preventing IT and other areas from working in isolation, while also Continuous learning is encouraged. A fairly common mistake is to underestimate the skills gap: if technology is brought in but people are not prepared, the implementation is likely to fail.
2. Culture of innovation
Corporate culture determines whether it accepts change or rejects it, so a leadership figure can promote the experimentation of new ideas, celebrate learning opportunities after failures, and reinforce a shared vision among all the teams. To understand this better, a company that organises internal hackathons generates ideas and encourages collaboration.
3. Managing change
When a new tool or system is implemented, changes must be communicated to all departments of the organisation, especially those who will benefit from the technology in question, as well as provide the necessary training and ongoing post-implementation support. If this doesn't happen, even the best technology on the market can fail because employees don't adopt it.
Ultimately, the data-driven decision making it has become an essential competency for any manager who wants to align technology, business, and results.
