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If you’re eyeing the digital marketing scene, know that every click tells a story, and every strategy can turn the tables for companies fighting for the online spotlight. Creativity meets metrics, and social media is a place to make connections that matter and spread the word about your brand or make it so that the word spreads on its own. Here is how to become a digital marketing specialist.
Understanding the Role of a Digital Marketing Specialist
Imagine being the person for everything online in a business. That’s more or less what a digital marketing specialist does.
They handle everything from making a website appear at the top of search results (thanks to SEO) to crafting the kind of content that makes people click, share, and buy. Specialists know what makes audiences tick on social media and keep up with the latest hashtag trends to figure out the best time to post. They don’t throw content out into the void and hope for the best—they analyze everything.
Which tweet has the most engagement? Did changing the call-to-action on that email campaign increase conversions? It’s all in a day’s work.
But what’s the big deal about all this, you might ask.
Being visible online is pretty much everything for a business these days. It matters little if it’s a local bakery or a multinational tech company. They all need to be online so potential customers can find them. Their digital marketing specialist uses a mix of creativity, analytics, and tech-savvy to boost brand awareness, rake in leads, and ultimately, drive sales. These specialists are the ones making sure a business is actively connecting with people and turning them into customers.
Essential Skills and Qualifications
To become a digital marketing specialist, you must have strong technical skills, creativity, and a fair bit of analytical prowess. Here’s a rundown:
- SEO/SEM reveals the alchemy of search engines that can make or break a brand’s online visibility. This skill helps you know the right keywords, the magic of backlinks, and the techniques of ranking higher.
- Content creation (blogs that tell a story, videos that captivate, or tweets that go viral) must resonate with your audience.
- Data analysis is great if you love numbers because dissecting website traffic, engagement rates, and conversion stats to understand what’s working (and what’s not) is a big part of the game.
- Social media expertise means knowing your TikToks from your Tweets. Each platform operates on a distinct system, and mastering these can help you connect with audiences effectively.
- PPC advertising is just as significant. Crafting the ads at the top of the search results requires a keen understanding of PPC strategies, especially if you’re aiming for a monetary balance.
As for the educational backdrop, while you don’t necessarily need a degree in digital marketing, having one in related fields like marketing, communications, or business can give you a solid foundation. That said, digital marketing values skills and real-world experience just as much, if not more.
Path to Becoming a Digital Marketing Specialist
Here’s a guide on how to be a digital marketing specialist:
1. Obtain a Relevant Degree
While you don’t need a degree in marketing to break into digital marketing, it undoubtedly helps. A bachelor’s in marketing, communications, or a related field lays a solid foundation. It introduces you to marketing principles, consumer behavior, and, increasingly, digital marketing basics. If college isn’t your path, there are still many success stories from self-taught specialists.
2. Get Hands-on Experience
There’s no substitute for getting directly involved in digital marketing. Internships are a great step, as they give you a peek into the industry’s workings and let you apply what you’ve learned. Or you can start your own project. Create a blog, manage social media for a family business, or run your own PPC campaigns to build a portfolio that showcases your skills.
3. Prioritize Continuous Learning
Digital marketing evolves fast, so you must keep up with the trends. Online courses and certifications can keep you up to date while also boosting your resume. Certifications like Google Ads, Facebook Blueprint, or SEMrush certifications also prove your skills and dedication to the craft. Each one is a step toward establishing your expertise.
4. Start Networking
Join digital marketing forums, attend webinars, and don’t be shy about reaching out to professionals you admire. The digital marketing community is surprisingly welcoming and a treasure trove of insights and opportunities.
5. Apply for Entry-Level Positions
With some education, a portfolio, and a few certifications under your belt, start applying for entry-level positions. Adjust your resume to highlight your digital marketing skills and projects, and don’t get discouraged by setbacks. Every interview, whether successful or not, is a point of learning.
OPIT’s Digital Marketing Education Programs
OPIT has a couple of solid programs up its sleeve for digital marketing—the BSc and MSc in Digital Business. They aren’t typical, dry lecture-based courses. These programs give you both the theory and practice necessary in digital marketing.
OPIT’s approach is unique in the way it blends theory with practice. You learn the latest trends, tools, and strategies currently being used in the industry. Plus, you’re not learning in a vacuum. OPIT connects you with industry experts, giving you a chance to pick the brains of people who have invaluable experience and skills. The programs offer first-hand insights, resources, and a network of professionals you’d be hard-pressed to find anywhere else.
Why Choose a Career as a Digital Marketing Specialist
For starters, every business out there, from the hole-in-the-wall coffee shop to giant corporations, is trying to make its mark online. That means there’s an enormous demand for people who can handle the digital world creatively and effectively.
This job also keeps you on your toes. One day, you might be cracking the code on a Google Ads campaign, and the next, you’re storytelling on Instagram or analyzing website traffic. It’s this mix of creativity, strategy, and analytics that makes the work diverse and, believe it or not, pretty exciting.
Become the Social Media Strategist You Were Meant to Be
The path to becoming a specialist is fairly varied, but two factors hold true: you need to keep on top of current trends, and you need hands-on experience. Fortunately, OPIT positions you on the right career path by providing just that. Check out OPIT’s bachelor’s and master’s programs in digital business and learn how to be a good digital marketing specialist first-hand.
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From the same author
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- Agenda Digitale, published on May 16th, 2025
By Riccardo Ocleppo, Founder and Director of OPIT – Open Institute of Technology
AI ethics requires ongoing commitment. Organizations must integrate guidelines and a corporate culture geared towards responsibility and inclusiveness, preventing negative consequences for individuals and society.
In the world of artificial intelligence, concerns about algorithmic bias are coming to the forefront, calling for a collective effort to promote ethical practices in the development and use of AI.
This implies the need to understand the multiple causes and potential consequences of the biases themselves, identify concrete solutions and recognize the key role of academic institutions in this process.
Bias in AI is a form of injustice, often systemic, that can be embedded in algorithms. Its origins are many, but the main culprit is almost always the data set used to train the models. If this data reflects inequalities or prejudices present in society, the risk is that AI will absorb and reproduce them, consolidating these distortions.
But bias can also manifest itself in the opposite direction. This is what happened some time ago with Google Gemini. The generative AI system developed by Google, in an attempt to ensure greater inclusivity, ended up generating content and images completely disconnected from the reality it was supposed to represent.
Further complicating the picture is the very nature of AI models, which are often characterized by complex algorithms and opaque decision-making processes. This complexity makes it difficult to identify, and therefore correct, biases inherent in the systems.
Ethical Data Management to Reduce Bias in AI
Adopting good data management practices is essential to address these issues. The first step is to ensure that the datasets used for training are diverse and representative. This means actively seeking data that includes a wide variety of demographic, cultural, and social contexts, so as to avoid AI exclusively reproducing existing and potentially biased models.
Alongside data diversification, it is equally important to test models on different demographic groups. Only in this way can latent biases that would otherwise remain invisible be highlighted. Furthermore, promoting transparency in algorithms and decision-making processes is crucial. Transparency allows for critical control and makes all actors involved in the design and use of AI accountable.
Strategies for ethical and responsible artificial intelligence
Building ethical AI is not an isolated action, but an ongoing journey that requires constant attention and updating. This commitment is divided into several fundamental steps. First, ethical guidelines must be defined. Organizations must clearly establish the ethical standards to follow in the development and use of AI, inspired by fundamental values such as fairness, responsibility and transparency. These principles serve as a compass to guide all projects.
It is also essential to include a plurality of perspectives in the development of AI. Multidisciplinary teams, composed of technologists, ethicists, sociologists and representatives of the potentially involved communities, can help prevent and correct biases thanks to the variety of approaches. Last but not least, promote an ethical culture : in addition to establishing rules and composing diverse teams, it is essential to cultivate a corporate culture that places ethics at the center of every project. Only by integrating these values in the DNA of the organization can we ensure that ethics is a founding element of the development of AI.
The consequences of biased artificial intelligence
Ignoring the problem of bias can have serious and unpredictable consequences, with profound impacts on different areas of our lives. From the reinforcement of social inequalities to the loss of trust in AI-based systems, the risk is to fuel skepticism and resistance towards technological innovation. AI, if distorted, can negatively influence crucial decisions in sectors such as healthcare, employment and justice. Think, for example, of loan selection algorithms that unfairly penalize certain categories, or facial recognition software that incorrectly identifies people, with possible legal consequences. These are just some of the situations in which an unethical use of AI can worsen existing inequalities.
University training and research to counter bias in AI
Universities and higher education institutions have a crucial responsibility to address bias and promote ethical practices in AI development. Ethics must certainly be integrated into educational curricula. By including ethics modules in AI and computer science courses, universities can provide new generations of developers with the tools to recognize and address bias, contributing to more equitable and inclusive design. Universities can also be protagonists through research.
Academic institutions, with their autonomy and expertise, can explore the complexities of bias in depth, developing innovative solutions for detecting and mitigating bias. Since the topic of bias is multidimensional in nature, a collaborative approach is needed, thus fostering interdisciplinary collaboration. Universities can create spaces where computer scientists, ethicists, lawyers, and social scientists work together, offering more comprehensive and innovative solutions.
But that’s not all. As places of critical thinking and debate, universities can foster dialogue between developers, policy makers, and citizens through events, workshops, and conferences. This engagement is essential to raise awareness and promote responsible use of AI.
In this direction, several universities have already activated degree courses in artificial intelligence that combine advanced technical skills (in areas such as machine learning, computer vision and natural language processing) with training that is attentive to ethical and human implications.
Academic Opportunities for an Equitable AI Future
More and more universities around the world – including Yale and Oxford – are also creating research departments dedicated to AI and ethics.
The path to ethical AI is complex, but it also represents an opportunity to build a future where technology truly serves the common good.
By recognizing the root causes of bias , adopting responsible data practices, and engaging in ongoing and vigilant development, we can reduce the unintended effects of biased algorithms. In this process, academic institutions – thanks to their expertise and authority – are at the forefront, helping to shape a more equitable and inclusive digital age.
Read the articleSource:
- TechFinancials, published on May 16th, 2025
By Zorina Alliata
Artificial intelligence (AI) used to be the stuff of science fiction. Stories about rogue machines and robot uprisings were once a source of amusement, not anxiety. But over recent years, AI has quietly embedded itself in our daily lives.
From the algorithms behind social media feeds, to the voice assistants managing our calendars. This quiet takeover has become something far louder: fear.
Headlines around AI are often alarmist. Statements such as “AI will take your job”, “AI will end education”, or “AI is dangerous and unregulated” are thrown around regularly. These narratives feed on uncertainty and fuel distrust.
But it doesn’t have to be this way. The hyper-fixation on the never-ending negative aspects of AI is the wrong approach to take. What if AI isn’t the villain? What if, at this stage, it’s simply a child?
AI, in many ways, is still learning. It mimics human behaviour, absorbs language, and forms patterns based on what it sees. Its current capabilities, however powerful they may seem, are not equivalent to human intelligence. It has limitations. It makes mistakes. It can even be manipulated and misled. It reflects our world; flaws and all. In that sense, AI is less an omnipotent force and more in line with a toddler trying to find its way.
And, like any child, it needs guidance.
This is especially evident in education. The emergence of AI tools such as ChatGPT has caused a stir in higher education institutions and universities, sparking fears about plagiarism and the erosion of critical thinking. Some institutions have responded with strict bans, while others have embraced cautious integration. The panic is understandable, but is it misplaced?
Rather than jumping to conclusions, educators should consider shifting the conversation. AI can, in fact, become an ally in learning. Instead of assuming students will cheat, we can teach them to use AI responsibly. Most of us educators can already recognise the signs of AI-generated work: excessive use of numbered lists, repetitive language and poor comparison skills. So why not use this as a teaching opportunity?
Encouraging students to engage with AI critically, understanding what it’s good at, where it falls short, and how to improve its output, can strengthen their own analytical skills. It invites them to become more active participants in their learning, not passive consumers of machine generated answers. Teaching young people how to work with AI is arguably more important than shielding them from it.
Outside the classroom, AI’s impact on the workforce is another growing concern. Stories about AI replacing jobs often dominate the news cycle. But these conversations often ignore a key point: AI is not autonomous. AI needs human designers, engineers, analysts, and ethicists to guide it. For every job that AI may eliminate, others will emerge to support and direct it.
More importantly, there are many things AI simply cannot do. It doesn’t understand nuance, morality or emotion. It can’t make ethical decisions without human input. These aren’t minor gaps, they’re fundamental. That’s why we must stop imagining AI as an unstoppable force and start thinking about how to raise it responsibly.
When considering how to raise our AI child responsibly, we need to acknowledge the issue of the algorithm being biased. Critics often point out that AI reproduces prejudices and errors, and whilst this is true, the source of that bias is us. It is important to remember that AI learns from historical data created by us, much of which reflects deeply ingrained societal inequalities.
Take, for example, mortgage lending in the US, where decades of discriminatory practices have skewed the data. Unless we intervene, AI trained on this information will inevitably reflect those same biases.
That’s not a reason to reject AI. It’s a reason to be more involved in its development, like any good parent. The responsibility lies with us.
Parenting is not about control for control’s sake; it’s about nurturing growth while setting boundaries. AI, like a child, needs feedback, accountability, and care. It will grow, but how it grows is up to us.
It’s tempting to view technology as something that happens to us, rather than something we can shape. But AI doesn’t exist outside of society, it’s a product of our values, decisions, and input. If we treat it as a monster, it may become one. If we treat it as a mirror, it will reflect what we show it. And if we treat it as a child, we may be able to raise it into something better.
So instead of fearmongering, let’s ask ourselves a better question: Are we raising AI correctly?
- Zorina Alliata, Professor of Responsible AI at OPIT– Open Institute of Technology
Read the full article below:
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Source:
- Agenda Digitale, published on May 16th, 2025
By Riccardo Ocleppo, Founder and Director of OPIT – Open Institute of Technology
AI ethics requires ongoing commitment. Organizations must integrate guidelines and a corporate culture geared towards responsibility and inclusiveness, preventing negative consequences for individuals and society.
In the world of artificial intelligence, concerns about algorithmic bias are coming to the forefront, calling for a collective effort to promote ethical practices in the development and use of AI.
This implies the need to understand the multiple causes and potential consequences of the biases themselves, identify concrete solutions and recognize the key role of academic institutions in this process.
Bias in AI is a form of injustice, often systemic, that can be embedded in algorithms. Its origins are many, but the main culprit is almost always the data set used to train the models. If this data reflects inequalities or prejudices present in society, the risk is that AI will absorb and reproduce them, consolidating these distortions.
But bias can also manifest itself in the opposite direction. This is what happened some time ago with Google Gemini. The generative AI system developed by Google, in an attempt to ensure greater inclusivity, ended up generating content and images completely disconnected from the reality it was supposed to represent.
Further complicating the picture is the very nature of AI models, which are often characterized by complex algorithms and opaque decision-making processes. This complexity makes it difficult to identify, and therefore correct, biases inherent in the systems.
Ethical Data Management to Reduce Bias in AI
Adopting good data management practices is essential to address these issues. The first step is to ensure that the datasets used for training are diverse and representative. This means actively seeking data that includes a wide variety of demographic, cultural, and social contexts, so as to avoid AI exclusively reproducing existing and potentially biased models.
Alongside data diversification, it is equally important to test models on different demographic groups. Only in this way can latent biases that would otherwise remain invisible be highlighted. Furthermore, promoting transparency in algorithms and decision-making processes is crucial. Transparency allows for critical control and makes all actors involved in the design and use of AI accountable.
Strategies for ethical and responsible artificial intelligence
Building ethical AI is not an isolated action, but an ongoing journey that requires constant attention and updating. This commitment is divided into several fundamental steps. First, ethical guidelines must be defined. Organizations must clearly establish the ethical standards to follow in the development and use of AI, inspired by fundamental values such as fairness, responsibility and transparency. These principles serve as a compass to guide all projects.
It is also essential to include a plurality of perspectives in the development of AI. Multidisciplinary teams, composed of technologists, ethicists, sociologists and representatives of the potentially involved communities, can help prevent and correct biases thanks to the variety of approaches. Last but not least, promote an ethical culture : in addition to establishing rules and composing diverse teams, it is essential to cultivate a corporate culture that places ethics at the center of every project. Only by integrating these values in the DNA of the organization can we ensure that ethics is a founding element of the development of AI.
The consequences of biased artificial intelligence
Ignoring the problem of bias can have serious and unpredictable consequences, with profound impacts on different areas of our lives. From the reinforcement of social inequalities to the loss of trust in AI-based systems, the risk is to fuel skepticism and resistance towards technological innovation. AI, if distorted, can negatively influence crucial decisions in sectors such as healthcare, employment and justice. Think, for example, of loan selection algorithms that unfairly penalize certain categories, or facial recognition software that incorrectly identifies people, with possible legal consequences. These are just some of the situations in which an unethical use of AI can worsen existing inequalities.
University training and research to counter bias in AI
Universities and higher education institutions have a crucial responsibility to address bias and promote ethical practices in AI development. Ethics must certainly be integrated into educational curricula. By including ethics modules in AI and computer science courses, universities can provide new generations of developers with the tools to recognize and address bias, contributing to more equitable and inclusive design. Universities can also be protagonists through research.
Academic institutions, with their autonomy and expertise, can explore the complexities of bias in depth, developing innovative solutions for detecting and mitigating bias. Since the topic of bias is multidimensional in nature, a collaborative approach is needed, thus fostering interdisciplinary collaboration. Universities can create spaces where computer scientists, ethicists, lawyers, and social scientists work together, offering more comprehensive and innovative solutions.
But that’s not all. As places of critical thinking and debate, universities can foster dialogue between developers, policy makers, and citizens through events, workshops, and conferences. This engagement is essential to raise awareness and promote responsible use of AI.
In this direction, several universities have already activated degree courses in artificial intelligence that combine advanced technical skills (in areas such as machine learning, computer vision and natural language processing) with training that is attentive to ethical and human implications.
Academic Opportunities for an Equitable AI Future
More and more universities around the world – including Yale and Oxford – are also creating research departments dedicated to AI and ethics.
The path to ethical AI is complex, but it also represents an opportunity to build a future where technology truly serves the common good.
By recognizing the root causes of bias , adopting responsible data practices, and engaging in ongoing and vigilant development, we can reduce the unintended effects of biased algorithms. In this process, academic institutions – thanks to their expertise and authority – are at the forefront, helping to shape a more equitable and inclusive digital age.
Read the full article below:
Source:
- TechFinancials, published on May 16th, 2025
By Zorina Alliata
Artificial intelligence (AI) used to be the stuff of science fiction. Stories about rogue machines and robot uprisings were once a source of amusement, not anxiety. But over recent years, AI has quietly embedded itself in our daily lives.
From the algorithms behind social media feeds, to the voice assistants managing our calendars. This quiet takeover has become something far louder: fear.
Headlines around AI are often alarmist. Statements such as “AI will take your job”, “AI will end education”, or “AI is dangerous and unregulated” are thrown around regularly. These narratives feed on uncertainty and fuel distrust.
But it doesn’t have to be this way. The hyper-fixation on the never-ending negative aspects of AI is the wrong approach to take. What if AI isn’t the villain? What if, at this stage, it’s simply a child?
AI, in many ways, is still learning. It mimics human behaviour, absorbs language, and forms patterns based on what it sees. Its current capabilities, however powerful they may seem, are not equivalent to human intelligence. It has limitations. It makes mistakes. It can even be manipulated and misled. It reflects our world; flaws and all. In that sense, AI is less an omnipotent force and more in line with a toddler trying to find its way.
And, like any child, it needs guidance.
This is especially evident in education. The emergence of AI tools such as ChatGPT has caused a stir in higher education institutions and universities, sparking fears about plagiarism and the erosion of critical thinking. Some institutions have responded with strict bans, while others have embraced cautious integration. The panic is understandable, but is it misplaced?
Rather than jumping to conclusions, educators should consider shifting the conversation. AI can, in fact, become an ally in learning. Instead of assuming students will cheat, we can teach them to use AI responsibly. Most of us educators can already recognise the signs of AI-generated work: excessive use of numbered lists, repetitive language and poor comparison skills. So why not use this as a teaching opportunity?
Encouraging students to engage with AI critically, understanding what it’s good at, where it falls short, and how to improve its output, can strengthen their own analytical skills. It invites them to become more active participants in their learning, not passive consumers of machine generated answers. Teaching young people how to work with AI is arguably more important than shielding them from it.
Outside the classroom, AI’s impact on the workforce is another growing concern. Stories about AI replacing jobs often dominate the news cycle. But these conversations often ignore a key point: AI is not autonomous. AI needs human designers, engineers, analysts, and ethicists to guide it. For every job that AI may eliminate, others will emerge to support and direct it.
More importantly, there are many things AI simply cannot do. It doesn’t understand nuance, morality or emotion. It can’t make ethical decisions without human input. These aren’t minor gaps, they’re fundamental. That’s why we must stop imagining AI as an unstoppable force and start thinking about how to raise it responsibly.
When considering how to raise our AI child responsibly, we need to acknowledge the issue of the algorithm being biased. Critics often point out that AI reproduces prejudices and errors, and whilst this is true, the source of that bias is us. It is important to remember that AI learns from historical data created by us, much of which reflects deeply ingrained societal inequalities.
Take, for example, mortgage lending in the US, where decades of discriminatory practices have skewed the data. Unless we intervene, AI trained on this information will inevitably reflect those same biases.
That’s not a reason to reject AI. It’s a reason to be more involved in its development, like any good parent. The responsibility lies with us.
Parenting is not about control for control’s sake; it’s about nurturing growth while setting boundaries. AI, like a child, needs feedback, accountability, and care. It will grow, but how it grows is up to us.
It’s tempting to view technology as something that happens to us, rather than something we can shape. But AI doesn’t exist outside of society, it’s a product of our values, decisions, and input. If we treat it as a monster, it may become one. If we treat it as a mirror, it will reflect what we show it. And if we treat it as a child, we may be able to raise it into something better.
So instead of fearmongering, let’s ask ourselves a better question: Are we raising AI correctly?
- Zorina Alliata, Professor of Responsible AI at OPIT– Open Institute of Technology
Read the full article below:
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