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Data-Driven Decision Making in the Modern Business Environment

How does a data-driven mindset support faster and more accurate decisions? We’ll show you why it’s essential for staying competitive.

In today’s business world, data has become a key player in decision-making processes. Data-driven decision making is not just a trend—it’s a fundamental requirement for remaining competitive. Companies that effectively collect, analyze, and leverage data can respond faster to market changes and better serve their customers.

What is Data-Driven Decision Making?

Data-driven decision making is the process of making business decisions based on objective, measurable data—rather than relying solely on intuition or past experiences. This doesn’t mean human experience is unimportant, but rather that data provides a more solid foundation for informed decisions.

In the modern business landscape, data can come from CRM systems, ERP platforms, web analytics, social media, customer support tools, or direct customer feedback.

The Benefits of a Data-Driven Mindset

One of the biggest advantages is the speed and accuracy of decision making. With real-time data, companies can immediately react to market changes, optimize their campaigns, and manage their resources more effectively.

In addition, a data-driven approach enhances transparency, reduces the chance of errors, and helps build a performance-based culture that can be measured and improved over time.

Tools and Technologies

Many tools support data-driven decision making. The most popular Business Intelligence (BI) platforms include Power BI, Tableau, and Google Looker Studio. These tools help visualize data, reveal insights, and create decision-supporting reports.

For companies at a more advanced level, predictive analytics, machine learning models, and AI-powered solutions can also be introduced.

How Can a Company Become Data-Driven?

Establish automated data collection: Ensure all relevant processes collect data systematically.
Centralize the data: Store all data in a structured, queryable central repository.
Implement analytics tools: Use BI platforms, dashboards, and automated reporting.
Foster a data culture: Ensure decision-makers understand and value data-based thinking.

Invest in training: Develop your team’s data literacy through courses and mentoring.

Summary

Data-driven decision making helps organizations make more accurate, faster, and objective business decisions. Adopting this mindset early on can give your company a strong competitive edge in a digitized market.

Discover What Codecool Has to Offer!

Learn how to collaborate with one of Europe’s leading digital training centers and achieve your digital transformation goals faster and more effectively. Whether you need IT specialists or corporate training programs, we have the right solution for you. Click the link to explore our detailed offerings!

Frequently Asked Questions

Why is data-driven decision making important?

Because it enables objective, fact-based decisions, reduces errors, and boosts competitiveness.

Which tools are useful for data-driven decision making?

Power BI, Google Looker Studio, Tableau, CRM and ERP systems.

Who should embrace a data-driven mindset?

Companies of any size or industry can benefit from it—if they’re open to thinking in data.

You have 1 new team member! Generative AI at work

Today, artificial intelligence has become accessible to nearly everyone. Instead of relying on complex coding, AI can use simple, everyday language. But what exactly can we use it for, and how can we harness its potential effectively? Authors from the Harvard Business Review have explored this new era of human-machine collaboration, highlighting research that shows AI is poised to transform more than 40% of work activities soon.

Generative AI transforms jobs and the way we work

Generative artificial intelligence is set to revolutionize various jobs in the years ahead.

No longer exclusive to developers and IT professionals, AI tools empower anyone to provide instructions in plain, everyday language. Research from the Harvard Business Review authors reveals that generative AI has the potential to extend, automate, or reimagine most business functions and over 40% of all jobs in the United States. The most significant changes are expected in legal, banking, insurance, and capital markets, with additional impacts in retail, travel, healthcare, and energy.

In the future, many people will find that their success at work depends on their ability to get the best out of large language models like ChatGPT—and to learn and grow with them.

3 fusion skills to use AI effectively

HBR’s thought-provoking article highlights three fusion skills essential for anyone looking to use AI effectively and achieve meaningful results.

The ability to intelligently prompt involves instructing large language models in ways that produce better outcomes. This requires understanding AI’s language and leveraging its capabilities by breaking down processes into clear, step-by-step instructions or visualizing multiple possible solutions.

For instance, a customer service representative in financial services might use AI to resolve complex customer queries. A pharmaceutical researcher could rely on it to analyze drug compounds and molecular interactions. Meanwhile, a marketer might utilize AI to examine data sets and determine optimal retail pricing.

The integration of discernment involves incorporating human judgment—both professional and ethical—to ensure that the answers and solutions generated by AI are reliable and accurate.

Large language models often lack the technical or business background knowledge necessary to solve specific problems, and this gap is frequently filled with AI hallucinations. Identifying where the model requires further learning and determining credible sources to support this process is crucial, all while maintaining a strong commitment to data security.

Mutual learning involves supplementing artificial intelligence with authoritative knowledge bases when necessary.

This process is reciprocal: as generative AI learns from the organization’s data and expertise, the human training it gains the ability to leverage AI for increasingly complex challenges. This dynamic turns the machine into a valuable team member—a co-creator.

Let me know if you'd like further refinements!

Empirical research consistently shows that ad hoc instructions—commonly used by most AI model users today—often result in unreliable or poor outcomes, particularly for complex reasoning tasks. This applies across all functions, from customer service and marketing to logistics and R&D. Bringing greater rigor to using generative AI in the workplace is essential for achieving dependable results.

How to ask AI smart?

Always think step-by-step: when prompting AI, break down the process into its individual components and focus on optimizing each step. Visualize a chain of thought that leads to your desired outcome. Empirical studies show that their performance improves significantly when generative AI tools are guided to decompose reasoning tasks in this manner.

Even for the simplest tasks, including a “think step-by-step” prompt is beneficial. This encourages the AI to explain how it arrived at the result rather than merely providing an answer from a black box.

Mutual learning should also take place in stages

For complex tasks requiring expertise or business context in human-machine collaboration—such as law, medicine, R&D, or inventory management—you can achieve better results by integrating AI into the workflow gradually and in stages.

MIT researchers, for example, explored the feasibility of developing an “AI scientist” capable of integrating diverse experimental data and generating testable hypotheses. They discovered that ChatGPT 3.5-Turbo could be fine-tuned to learn the structural biophysics of DNA when the researchers divided this complex task into a series of manageable subtasks for the model to master.

This approach is equally practical in non-scientific areas, such as inventory management. Subtask stages can include forecasting demand, collecting inventory data, predicting reorders, evaluating order volumes, and assessing performance. We can leverage our expertise and information for each additional subtask to teach, test, and validate the model.

Make room for creativity

Many workflows, from strategy design to product development, are open-ended and iterative. To maximize human-AI interaction in these activities, it is beneficial to guide machines to explore multiple solution paths and respond less linearly and binaryally.

This approach to intelligent querying can also enhance AI’s ability to make accurate predictions about complex financial or political events. Research by Philipp Schoenegger and Philip Tetlock demonstrated this in an experiment where human analysts and forecasters were paired with GPT-4 assistants to create “super-forecasters.”

This collaboration aimed to assign probabilities and uncertainty intervals to possible outcomes while listing arguments for and against each scenario. The researchers found that predictions generated by the trained assistants—on topics ranging from the Dow Jones closing figures on a specific day to the number of migrants arriving in Europe via the Mediterranean in December 2023—were 43% more accurate than those produced by commonly used, untrained GPTs.

Critical thinking and judgement

Integrate the RAG

Not only can large language models be prone to hallucinations, but the information and data sets used to train them are often outdated. As a result, working with these models usually requires human judgment to determine how critical it is to ensure the outputs are reliable, relevant, and up-to-date. In domains where fresh and accurate information is essential—such as healthcare or finance—retrieval-augmented generation (RAG) can be a valuable tool. This method enables large language models to access and utilize external data sources not part of their original training.

Pay attention to data security

If your work with AI involves confidential data or proprietary information, avoid using open-source or public language models. Always rely on corporate-approved LLMs that operate securely behind corporate firewalls.

Watch out for distortions

Pay close attention to distortions that may arise in your prompts. For example, suppose a financial analyst asks an LLM to explain how yesterday’s quarterly report indicates that the company is ready for a five-year growth cycle. In that case, the response may exhibit recency bias—the tendency to overestimate recent information when predicting future events.

Large operators are actively developing solutions to help users address these challenges. Microsoft and Google are introducing features in their services to give users better control over harmful prompts and responses. Meanwhile, Salesforce is building an AI architecture that safeguards confidential customer data by capturing it during prompt creation, preventing its sharing with third-party LLMs. This system also evaluates outputs for risks such as toxicity, bias, or privacy violations and gathers feedback to refine prompt templates.

Always investigate suspicious answers

Hallucinations and errors are inevitable, even with careful planning, as current research shows. Therefore, always approach AI responses with caution and scrutinize them for errors or suspicious signs.

When users encounter an incorrect output, they often reflexively prompt the model to try again repeatedly, which can gradually degrade the quality of the response, as UC Berkeley researchers Jinwoo Ahn and Kyuseung Shin have shown. Instead, the researchers recommend identifying the specific step where the AI made a mistake and using another large language model to address that step.

This involves breaking the problem into smaller, individual components and using the new output to fine-tune the original model’s response. For example, imagine a scientist using OpenAI’s ChatGPT to develop a new polymer through stepwise computations. If an error occurs in the chain, they could turn to Anthropic Claude to break down the problematic step into smaller subproblems and provide an explanation.

This refined information can then be fed into ChatGPT to improve the answer. By applying chain-of-thought principles, this technique enhances the quality of outputs that initially appear incorrect.

You and AI: Master and Disciple

As LLMs grow in size and complexity, they may develop advanced new skills that were not explicitly part of their training but emerge after being customized with contextual data or knowledge. To foster this development, consider the following steps:

Provide AI with patterns of thinking

Before assigning a problem to a large language model, you can train it to adopt a specific way of thinking. For example, you can teach it the “least to most” approach, which involves breaking down a complex challenge into smaller, more straightforward tasks. Solve the least difficult task first, then use that solution as a foundation for tackling the next challenge, and so on.

Denny Zhou and his team at Google DeepMind demonstrated that this “least to most” method improved AI output accuracy from 16% to 99%.

Take, for example, a marketing manager at a sportswear brand seeking AI assistance to plan a new collection. Here’s how the problem can be broken down for AI:

Target Audience: Identify fitness enthusiasts who could be potential customers—a relatively straightforward task, especially for a model trained on the company’s customer data.
Messages: Develop messages that emphasize performance, comfort, and style—this is a more complex and creative task that builds on the identified target audience.
Channels: Select social media platforms, fitness-specific blogs, and influencer collaborations to effectively deliver these messages to the target audience.
Resources: Allocate the budget—often the most debated aspect in any organization—according to the chosen channels.

Teach AI new processes

You can delegate task execution to AI by guiding it with examples within the context of your prompts.

For instance, researchers reported in Nature that they trained large language models to summarize medical information by providing examples such as radiology reports, patient queries, status updates, and doctor-patient dialogues. Their findings revealed that 81% of the summaries generated by the models were equivalent to or better than those produced by humans.

You can also train AI by providing contextual information and guiding it through questions until it solves the problem.

Take two software companies, both aiming to increase their sales. At the first company, the sales team struggles to forecast software license demand effectively. Their manager starts by supplying historical sales data to the AI and asking about the expected demand for the next quarter. Next, they provide details on customers’ software upgrades and annual budgets, inquiring about the effects of seasonality. Finally, they add detailed statistics from CRM systems and marketing reports, asking how marketing campaigns impact sales.

At the second company, the sales team focuses on improving customer selection. Their manager provides specific financial data and asks the AI to rank customers by spending. They then proceed with follow-up questions about geography, technical expertise, and other relevant criteria.

At every step, both managers teach the AI and fine-tune its ability to perform tasks within their company’s sales strategy. Incorporating organizational and industry expertise into their interactions enables the AI to adapt. The AI gains more experience with each company’s unique sales processes, producing increasingly accurate and valuable responses.

Learning new fusion skills

The widespread adoption of generative AI skills will require significant organizational investment as well as individual initiative, learning, and hard work. While some companies already offer relevant training, most have yet to develop widely accessible programs.

HBR’s research underscores this point: in their 2024 survey of 7,000 professionals, 94% of respondents expressed willingness to learn new skills to work with generative AI, yet only 5% reported that their employers were actively providing training in this area.
As a result, many individuals will need to take the initiative and stay ahead of the rapid advancements in AI.

The AI revolution is no longer on the horizon—it’s here. Leading companies leverage this transformative technology to rethink how industries, functions, and jobs are performed.
Generative AI has significantly raised the stakes, demanding that humans collaborate, act consciously to ensure trust, and continuously refine the technology and themselves to achieve better outcomes. No other major innovation in history has spread at such a rapid pace. Work will evolve faster and more profoundly than many of us can imagine. Get ready: the future of business won’t be shaped by AI alone but by the people who can harness its potential most effectively.

The Future of Work: Highlights from the WEF 2025 Report

ChatGPT was released to the public in November 2022. Two years later, it boasts a staggering 200 million weekly users on the OpenAI platform. This rapid AI-adoption foreshadowed a wave of sweeping changes in the labor market—including layoffs, skills shifts, and entirely new job categories. In January 2025, we can see what it has led to.

A new era unfolds

As of January 2025, we now have a clear perspective on how AI and technology reshape the workforce. The World Economic Forum (WEF) has published an in-depth report detailing what has already changed and what lies ahead for the labor market. One key finding has captured global attention:

“Regarding adjusting the composition of their workforce, 70% of organizations surveyed plan to hire new staff with emerging in-demand skills, 51% intend to transition staff from declining to growing roles internally, while 41% foresee staff reductions due to skills obsolescence.”

The 41% reduction mentioned in the report can already be felt—companies have started initiating layoffs across multiple industries. Unsurprisingly, AI stands out in the report, with half of employers planning to adapt their business strategies to leverage its potential and two-thirds intending to hire talent skilled in AI.

Speed up your digitalization!

Find out how to achieve your digitalization goals faster and more efficiently with Codecool’s specialized corporate training in AI, Cybersecurity, and more. Get a tailored training plan, or get in touch to hire AI-ready IT professionals to fill your vacant positions!

👉 See you options here!

The future of jobs: 2025 and beyond

The WEF’s Future of Jobs Report 2025 highlights a dramatic transformation in the global workforce over the next five years. Technology, climate change, and economic shifts are reshaping employment landscapes, projecting a net growth of 78 million jobs by 2030. This growth stems from a delicate balance: while 170 million new jobs will be created, 92 million existing roles will be displaced.

Technology’s role in job creation and displacement

Technological advancements are set to be the most significant drivers of labor market shifts, with digital access, AI, and robotics leading the way.

Broadening digital access alone is expected to create 19 million jobs while displacing 9 million, making it a key force in reshaping the workforce. Similarly, AI and information processing technologies are projected to create 11 million jobs but displace 9 million, emphasizing their transformative power.

Robotics and autonomous systems, however, are anticipated to result in a net loss of 5 million jobs, marking them as the most prominent job displacers.

Top 10 jobs by largest decline

Cashiers and Ticket Clerks
Administrative Assistants and Executive Secretaries
Building Caretakers, Cleaners and Housekeepers
Material-Recording and Stock-Keeping Clerks
Printing and Related Trades Workers
Accounting, Bookkeeping and Payroll Clerks
Accountants and Auditors
Transportation Attendants and Conductors
Security Guards
Bank Tellers and Related Clerks

Top jobs by net growth

On the sunny side of the sweeping changes, emerging technological roles are experiencing fast-paced growth; however, the most significant absolute job gains happen in frontline positions, such as farm workers, delivery drivers, and construction workers. Developers rank third on the list, reflecting the increasing demand for tech expertise alongside traditional labor roles.

Farmworkers, Laborers, and Other Agricultural Workers
Light Truck or Delivery Services Drivers
Software and Applications Developers

The care economy is also booming, with professions like nursing and social work significantly expanding, driven by demographic trends, especially aging populations.

Despite these gains, about 39% of the workforce must update or completely transform their skills by 2030.

Employers are responding, with 85% prioritizing workforce upskilling and over half planning to increase wage allocations. Future of Jobs Survey respondents consider skill gaps the most significant barrier to business transformation, with 63% of employers identifying them as a substantial barrier over the 2025-2030 period. Accordingly, 85% of employers surveyed plan to prioritize upskilling their workforce, with 70% expecting to hire staff with new skills, 40% planning to reduce staff as their skills become less relevant, and 50% planning to transition staff from declining to growing roles.”

Fastest-growing roles

Roles in emerging fields like AI, big data, networks, and cybersecurity are experiencing the fastest growth among all job categories, driven by the ongoing digital transformation trend. Unlike frontline roles, which show the most significant absolute growth, these roles are fueled by 60% of employers anticipating digital access to reshape their business by 2030.

Top 10 occupations with the most significant expected job growth:

Big Data Specialists
FinTech Engineers
AI and Machine Learning Specialists
Software and Applications Developers
Security Management Specialists
Data Warehousing Specialists
Autonomous and Electric Vehicle Specialists
UI and UX Designers
Light Truck or Delivery Services
Drivers

These projected trends underscore a growing need for organizations and individuals to adapt rapidly to the evolving demands of the workforce. As technology reshapes industries and creates new opportunities, addressing the skills gap has become critical.

Bridge your skill gap with Codecool

Meanwhile, at Codecool, we are keeping pace with these technological advances.

Our full-stack course is continually updated to focus on the rise of AI technologies. Our curricula and methods constantly evolve and include AI-focused workshops and AI projects to help our IT professionals master AI tools for the programming lifecycle.

We also work with companies to help speed up their digitalization with specialized corporate IT training. These training solutions provide an all-in-one answer to digitalization: upskill your team efficiently to avoid layoffs, boost employee satisfaction, and improve retention.

Discover what Codecool has to offer!

Work with one of Europe’s leading digital training centers and simplify digitalization for your people.

We can help you achieve your goals with tailored corporate training or by connecting you with the perfect IT professional adept in the most in-demand skills, technologies, and AI.

OpenAI has created the latest ChatGPT extension for developers.

Canvas is the name of an extension that OpenAI has created specifically for developers.

Chat GPT was released to the public in November 2022. Two years later, two hundred million users use the OpenAI platform weekly. It was already possible to write code using ChatGPT, but Canvas opened new doors for programmers. The innovation is that developers and artificial intelligence work together to write code.

The program can be freely edited, reworked, or pasted from existing code.

This was not possible before. By December 10, 2024, OpenAI announced that Canvas had moved out of beta and was made available to all ChatGPT users, including those on the free tier.

Although the new extension is compatible with most programming languages, it is most effective for writing code in JavaScript, Java, PHP, C++, Python, and TypeScript.

On the closure of Codecool Austria

We would like to inform you, our dear partners, about the closure of Codecool Austria.

The economic difficulties we are experiencing in our country are also present beyond our borders, and Austrian companies have significantly reduced their demand for junior IT developers. As a result, finding suitable jobs for our graduates and implementing Codecool’s unique business model has become increasingly challenging in recent quarters. Therefore, the presence of our Austrian campus was no longer justified.

We are proud, however, that as the leading programming school in the CEE region, we have enabled dozens of people to change careers in Austria. Many have successfully started a new IT career with Codecool. Our existing Austrian customers will be served from our Budapest campus from now on.

We are confident that the Austrian IT market will soon recover, and we would like to thank all students, collaborators, and corporate partners for their trust in us and all our employees who have been the company’s pillars.

Accedo: Scandinavian vibe and cutting-edge video technology

In our Inspiring Digital Employers series, we’re bringing you some of our 300+ business clients from 4 countries. Meet Accedo from Hungary.

Our partners inspire us not only to become better employers ourselves, but also to contribute to their success with the next generation of skilled tech colleagues. It’s challenging to find the right talent in today’s labour market. We’re proud to be the tech training and hiring partner that can connect businesses with the right talent – our amazing Codecoolers.

Meet Accedo, a tech company delivering ground-breaking video services to the world’s leading broadcasters, content owners and TV operators. We’ve sat down with Head of Software Development, Istvan Hilgert.

Istvan, please introduce yourself and your company to us.

My name is István Hilgert, I’m Head of Software Development at Accedo Broadband HU Kft.

Accedo is a global company with 16 offices across North America, South America, Europe, and the Asia Pacific region. Our headquarters are in Stockholm, Sweden, so we have Swedish roots. Our Budapest office was opened back in 2014, in the heart of Budapest. Currently we have 60 employees.

We create a next-level video experience for content owners, broadcasters, TV and media providers.

Our 400+ customers worldwide need innovative video streaming solutions with amazing quality. We make this happen on almost any platform, screen, or device.

Why is Accedo a great place to work at?

The first thing that comes to mind is our Swedish roots and our Scandinavian-like company culture.

We can be laser-focused on driving results, but we are always positive and altruistic in everything we do.

This culture and attitude ensure transparent operations and strict compliance with rules and laws, including the salaries and the labour law. All our colleagues are entitled to a multi-layered benefits package with well-being elements, including optional consultations with a psychologist, too.

We’re keen on creating a healthy work-life balance for everyone, so we also give a lot of room for home office and hybrid work. We value everyone’s opinion and aim for building a culture based on feedback. We try to involve everybody in discussions about important company matters.

You can hear about the growing digital talent gap everywhere. How does it affect you?

Well, in the past 6 months, I’d say the situation has turned quite dramatic. I might even say, tragic. It’s getting harder and harder to find highly qualified developers with experience.

It’s almost like there’s a war fought for tech talent and employers are competing in giving out the highest wages.

It’s difficult to keep up with the competition.

A lot of companies actively go for others’ developers. It’s common for an experienced developer to get multiple job offers in just one week. So, the bottom line is that we’re not just fighting for finding great talent, but also for keeping our colleagues at the same time.

Hiring or training? How do you grow the digital skillset of your organisation?

Recruitment alone is just not enough anymore.

We have an immersive onboarding training, and all our colleagues get the chance to take part in further professional training and take courses later, too. It’s in everyone’s best interest to use these opportunities, so that they can keep their skills relevant and have a long, successful career.

Why did you choose to partner up with Codecool?

We assumed that people who complete Codecool’s year-long course do not only get a wide spectrum of knowledge, but they must be all-in and super motivated, too. Plus, we knew about the pre-selection process they go through in the beginning.

We also like that Codecoolers learn soft skills, too, so they work well in teams. And they use English during the course, which is especially important for us, since all our partners are located abroad and we’re a completely international company.

Codecoolers can choose a specialisation at the end of their Full-Stack Development course, so they each have a deeper knowledge of some special field, which is often very valuable.

How do you see Codecoolers after working with them for some time now?

Well, they surpassed all our expectations by handling initial challenges very well. We gave all of them an on-site onboarding training in our specific technologies for a start, and by now they all are working reliably and independently.

I have to say that every single Codecooler at Accedo was a great pick.

This approach proved to be super successful in our case, and we’re just about to kick off another training for our third round of new Codecoolers.

Can you share some of your future plans?

Our aim at Accedo has always revolved around transforming the video experience and with that, drive the industry developments further. Our focus is to turn TV viewers into video lovers, globally.

The ever expanding portfolio of products and customers tied together with our new partnerships steer us towards a very ambitious roadmap in terms of growth. Thus we have a quite aggressive approach for future expansion. In Hungary, our focus is on the local talent, but we are open to onboard people from the entire region, the aim being to bring them onboard as soon as possible. And I could also mention our other offices in Stockholm, Madrid and London, where we’re also hiring.

Inspired by Accedo’s example?

Reach out to us if you need great junior tech professionals or best-in-class training for your organisation.

Hope to talk to you soon!