Learn the steps, challenges, and opportunities of transitioning from BI to data science or AI, and how to prepare your skills, portfolio, and network.

Another skill to assess is storytelling. BI and Data science/AI both need to storytell on data insights. Data scientists also need to be able to explain complex concepts, to data partners, like decision trees or neural networks in the simplest of ways.

To demonstrate your AI skills you need to gain practical experience in developing AI projects. I networked with leadership team and took up a data engineering and AI related delivery project. Build the team and worked with team members closely to enhance insights on AI as technology and usage.

How do you transition from BI to data science or AI?

Business intelligence (BI) is a field that involves collecting, analyzing, and presenting data to help organizations make better decisions. BI professionals use various tools and techniques to create reports, dashboards, and visualizations that provide insights and recommendations. But what if you want to take your BI skills to the next level and explore the domains of data science or artificial intelligence (AI)? How do you transition from BI to these more advanced and in-demand fields? In this article, we will discuss some of the steps, challenges, and opportunities that you can expect when you pursue this career path.

1 Assess your current skills

The first step to transition from BI to data science or AI is to assess your current skills and identify the gaps that you need to fill. BI and data science/AI share some common skills, such as data analysis, SQL, and communication, but they also differ in many aspects. For example, data science and AI require more programming, statistics, and machine learning knowledge than BI. You can use online resources, such as self-assessments, quizzes, or courses, to evaluate your strengths and weaknesses and plan your learning goals.

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Prachur Bhargava
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Another skill to assess is storytelling. BI and Data science/AI both need to storytell on data insights. Data scientists also need to be able to explain complex concepts, to data partners, like decision trees or neural networks in the simplest of ways.

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Rodolfo Augusto Vargas Hanke

Núcleo de Informações em Saúde | Planejamento Estratégico | Cultura Data Driven | Data Science | Lean SS | BI | SQL | DAX | Excel Power Query | Power BI | MV KPI/BSC |
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As três áreas estão altamente relacionadas e é possível transitar profissionalmente entre elas. Ter conhecimentos aprofundados em matemática e estatística, administração de empresas e ciências da computação, é fundamental para direcionar uma especialização que permeia a ciência de dados ou IA. Pode ser importante ponderar que o BI é um conceito mais abrangente e utiliza soluções de ciência de dados e IA. A própria ciência de dados pode abranger uma gama de especializações sendo algumas mais focadas em arquitetura de dados, engenharia de dados e modelagem estatística. A IA pode ser entendida como contida dentro da ciência de dados em meio às soluções de BI para empresas. Entender a diversidade de áreas e suas oportunidades é fundamental.

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S.Marzieh Hosseini
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Another important skill is to communicate efficiently. The more communicative you are, the easier the life would be specially with stakeholders and managers. So in my opinion, communication plays an important role in AI.

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2 Learn the fundamentals of data science and AI

The next step is to learn the fundamentals of data science and AI, such as the concepts, methods, and tools that are essential for these fields. You can start with the basics, such as Python, R, or other programming languages, and then move on to more advanced topics, such as data wrangling, exploratory data analysis, data visualization, hypothesis testing, regression, classification, clustering, natural language processing, computer vision, deep learning, and more. You can use various online platforms, such as Coursera, Udemy, edX, or Kaggle, to find courses, tutorials, or projects that suit your level and interest.

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Tapan Jain

Vice President | Data & Analytics | Program Management | Patent Holder
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To transition into AI, you need to understand the basic concepts of AI, such as machine learning, deep learning, natural language processing, computer vision, etc. You can start by reading introductory books on AI, taking online courses, or attending AI workshops, internal trainings, read internal white papers, read and refer papers and posts on LinkedIn, conferences.

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3 Build a portfolio of projects

The third step is to build a portfolio of projects that demonstrate your data science and AI skills and showcase your value to potential employers. You can use real-world data sets, such as those from Kaggle, UCI, or other sources, or create your own data sets based on your domain knowledge or personal interests. You can also participate in competitions, hackathons, or challenges that allow you to apply your skills to solve real problems and learn from others. You should document your projects, such as by using GitHub, Jupyter notebooks, or blogs, and highlight your process, results, and insights.

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Tapan Jain

Vice President | Data & Analytics | Program Management | Patent Holder
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To demonstrate your AI skills you need to gain practical experience in developing AI projects. I networked with leadership team and took up a data engineering and AI related delivery project. Build the team and worked with team members closely to enhance insights on AI as technology and usage.

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Paulo Bega

Ciência de dados | Business Intelligence (BI) | Gestão de Projetos | Gestão Estratégica | Administrador
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Não se esqueça de algo tão importante quanto o estudo, as técnicas, portfólio...etc. O chamado networking, um bom networking pode abrir portas muito mais rápido, ou ainda, abrir portas que poucos terão acesso.

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Emanuel Cirano Vazquez Rojas

Consultor Analista de información.
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Y lo consideras necesario? Considera lo complicado que es generar la cantidad de parámetros que requiere la IA para tomar una decisión, la ciencia de datos es muy parecido al BI, pero alguna vez te has preguntado dónde queda la intuición? La IA no la tendrá.

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4 Network and connect with the community

The fourth step is to network and connect with the data science and AI community, such as by attending events, joining groups, or following influencers. This can help you learn from the experts, discover new trends and opportunities, and expand your professional network. You can also use platforms, such as LinkedIn, Twitter, or Medium, to share your projects, insights, or opinions, and engage with other data enthusiasts. You can also seek feedback, advice, or mentorship from more experienced data scientists or AI practitioners who can guide you in your career journey.

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Tapan Jain

Vice President | Data & Analytics | Program Management | Patent Holder
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Networking with AI professionals can help you learn more about the industry, and get mentorship. I learned a lot while talking to AI team members, understand the AI use cases and outcome business is trying to achieve, problems faced and potential solution, reading client success stories. You can as well attend internal AI trainings.

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5 Apply for jobs or roles that match your skills and goals

The final step is to apply for jobs or roles that match your skills and goals. You can use various online platforms, such as Indeed, Glassdoor, or LinkedIn, to find data science or AI jobs that suit your qualifications, experience, and preferences. You can also use your network, such as your contacts, referrals, or recommendations, to find opportunities that are not advertised online. You should prepare your resume, cover letter, and portfolio, and highlight your BI background, data science/AI skills, and achievements. You should also practice your interview skills, such as your technical, behavioral, and case study questions, and showcase your enthusiasm and passion for data.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Amit Shivpuja

Director of Data Product, Governance, & Strategy - Walmart | Strategy, Data, Business Intelligence, Analytics
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To me AI & ML are just different analytics TOOLS so I mean that Data Scientists, Machine Learning folks are still Analysts like Business Intelligence folks using a different toolkit. They are all trying to get to identify or predict patterns that would help make insights i.e. an informed decision and an action. So for someone to get from BI to AI I feel it is just learning a new toolkit and its nuances. The fundamental overall concepts, flow, and process of getting from Data to Insights is the same (Data -> Extract,Transform, Load -> Clean -> Visualize -> Analyze -> Enable Action -> Data)

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How do you transition from BI to data science or AI?

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1 Assess your current skills

2 Learn the fundamentals of data science and AI

3 Build a portfolio of projects

4 Network and connect with the community

5 Apply for jobs or roles that match your skills and goals

6 Here’s what else to consider

Business Intelligence

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How do you transition from BI to data science or AI?

1

2

3

4

5

6

1 Assess your current skills

2 Learn the fundamentals of data science and AI

3 Build a portfolio of projects

4 Network and connect with the community

5 Apply for jobs or roles that match your skills and goals

6 Here’s what else to consider

Business Intelligence

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