[My BI Path | Part-1] Unlocking the World of Business Intelligence: A Guide to a Rewarding Career
I am a Business Intelligence specialist with 8 years of experience (as of the time of writing) in analytics, data governance, operations, and strategy consulting. Through this article series, I aim to share my firsthand experience and insights on how to kickstart a successful career in business intelligence, even with limited resources available. By sharing my journey, I hope to inspire and guide others who are eager to enter this exciting field and make an impact using data and technology. Whether you’re a recent graduate, a mid-career professional or simply looking to pivot into a new field, this article will provide you with the information, resources, and inspiration you need to take your first steps on the path to a successful and rewarding career in business intelligence.
Demand for Data Science Professionals in the Fourth Industrial Revolution
The landscape of data science and business intelligence is rapidly changing, with organizations across industries recognizing the power of data-driven decision-making. With the growing demand for data-savvy professionals, there has never been a better time to pursue a career in business intelligence. The purpose of this article is to explore and demystify the exciting and fast-growing world of data science. With the digital age well and truly upon us, data has never been more important in shaping business and decision-making.
In a world where data drives business decisions and fuels innovation, it’s no surprise that data science has become one of the most in-demand careers of the 21st century. The demand for data-savvy professionals has skyrocketed, making a career in data science an attractive proposition. However, many aspiring data scientists may feel daunted by the perceived barriers to entry, such as a lack of resources or technical know-how. When you do not have ample resources at hand, becoming a data scientist can be a daunting task.
As the Fourth Industrial Revolution takes hold, driven by the incredible advancements in data science and artificial intelligence, the job market is undergoing a dramatic shift. Gone are the days when data was simply a by-product of digital applications, operating systems, and platforms; today, data is recognized as a valuable asset that drives innovation forward. With data being the fuel of this Fourth Industrial Revolution, it’s no surprise that organizations are clamoring for individuals who possess the knowledge and skills to turn data into actionable insights.
As the world transforms into a digital-first landscape, the demand for skilled professionals in the field of data science has skyrocketed. The World Economic Forum (WEF) & PwC conducted studies that revealed a stark disconnect between the demand for data science skills and the supply of qualified professionals. In the study conducted by PwC, an overwhelming 69% of employers expect candidates with data science abilities to be given priority for job opportunities within their organizations, yet a mere 23% of college and university leaders believe their graduates are equipped with these critical skills. This has created a new talent race in the Fourth Industrial Revolution, as companies scramble to find individuals with the right mix of technical and business acumen to leverage data to their advantage.
The World Economic Forum’s report (WEF) on data science talent provides key insights into the current and future state of the data science labor market. By 2022, it is expected that human workers and automated processes will share the workload of current tasks equally, while a range of new roles will emerge. According to the report, data science roles and skills form a relatively small part of the workforce, but they are currently among the highest in-demand roles in the labor market.
But why is demand for data science skills skyrocketing? The answer is simple: data is the backbone of modern business. Companies in nearly every industry are now collecting vast amounts of data and looking for ways to turn it into actionable insights that can drive growth and innovation. This requires a deep understanding of advanced data analytics, machine learning, and other cutting-edge technologies, which makes data scientists one of the most sought-after professionals in the job market.
Benefits of choosing a career in data science
Why a Career in Data Science is a Smart Choice. A career in data science is not only in high demand and well-compensated, but it’s also a prestigious and versatile field with endless opportunities for growth and development. If you have a passion for data and enjoy solving complex problems, then a career in data science may be the perfect choice for you. If you’re considering a career in this field, here are some compelling reasons why it’s a smart choice:
It’s in high demand: Companies are eager to stay ahead of the curve and make informed decisions based on data. Data scientists are essential in collecting, interpreting, and analyzing data to help companies optimize their operations and stay ahead of the competition.
It pays well: Data science professionals typically earn high salaries, with an average salary of $125,724 per year. The amount you can earn varies depending on the industry and your level of experience, but your earning potential will increase as you develop your skills and gain experience.
It’s a prestigious career: Companies rely heavily on data scientists to make informed business decisions. By pursuing a career in data science, you’ll play an essential and respected role within your organization and be a key part of the decision-making process.
It involves automation: Data science often involves using programming and machine learning to automate processes and make them more efficient. If you enjoy finding ways to optimize processes and make them more productive, then a career in data science may be perfect for you.
It’s versatile: Data science is a versatile field that is relevant in almost every industry. This means you’ll have a wide variety of opportunities to work in different sectors, such as healthcare, finance, and insurance. With a career in data science, you’ll never be limited in your opportunities for growth and advancement.
Exploring the Roles in a Data Ecosystem: Understanding the Vital Players in delivering Data-led Business Opportunities
Data Science is a field that involves gaining insights and knowledge from structured and unstructured data. It encompasses different roles, each having their own set of skills, tools, and responsibilities. These roles play a crucial role in helping businesses make informed decisions, optimize operations and grow. Data science is a team sport. Successful organizations build data science teams that incorporate different skill sets and responsibilities, instead of relying on a few elite individuals. And I group them into 3 major archetypes based on their role, responsibilities, and deliveries. This article highlights some of the most common roles in data science, including:
- Data Analyst: responsible for collecting, processing and analyzing large datasets to identify patterns and trends. They communicate the insights to stakeholders through reports and presentations.
- Data Scientist: performs advanced statistical and machine learning techniques to make predictions and uncover insights that drive business decisions. They work closely with data analysts to prepare data and develop models and algorithms.
- Business Intelligence (BI) Specialist: designs, implements, and maintains the organization’s BI infrastructure. They develop dashboards, reports, and other visualizations to track key metrics and KPIs.
- Data Engineer: builds and maintains the infrastructure for storing, processing, and analyzing large datasets. They design data pipelines and platforms to collect, store, and process data efficiently.
- Data Architect: designs the overall data architecture, ensuring it is scalable, secure, and efficient. They work closely with data engineers and BI specialists to design data models, data warehouses, and data lakes.
- Business Analyst: bridges the gap between data science and the business by gathering data and presenting insights and recommendations to stakeholders. They specialize in specific areas like marketing, finance, or human resources.
- Statistician: applies statistical methods to data to help organizations make informed decisions. They work closely with data scientists and data analysts to ensure the data is appropriate for statistical methods.
- Machine Learning Engineer: develops and deploys machine learning models that enable organizations to automate decision-making and make predictions. They work closely with data scientists and data engineers to ensure scalability, efficiency, and accuracy.
Roles in the data ecosystem are divided between technical and business focus. Technical roles, such as Data Engineer and Data Architect, mainly design, develop, and maintain the infrastructure for storing, processing, and analyzing data, ensuring its quality and scalability. Business roles, such as Business Analyst, BI Specialist, and Statistician, focus on providing insights to support decision-making. Both Data Scientists and ML Engineers work on building the technical infrastructure for data analysis and model development, but also need to collaborate with business stakeholders to understand their needs and communicate insights and predictions. These roles often have a dual technical and business focus.
30-Second Takeaway
- The demand for data science professionals is rapidly increasing due to the recognition of data-driven decision-making in various industries.
- A career in data science is in high demand, well-compensated, prestigious, involves automation, and versatile.
- According to a study by PwC, 69% of employers expect candidates with data science abilities to be given priority for job opportunities.
- The World Economic Forum (WEF) predicts that by 2022, human workers and automated processes will share the workload equally and new roles in data science will emerge.
- Data science skills are currently among the highest in-demand roles in the labor market.
- Data is the backbone of modern business, and companies are collecting vast amounts of data to turn it into actionable insights.
- A career in data science requires a deep understanding of advanced data analytics, machine learning, and other cutting-edge technologies.
I am writing this article to empower and inspire those who have limited resources available to them, by sharing my journey as a Business Intelligence (BI) specialist and offering valuable insights and advice on how to kickstart a career in this field. Stay tuned for more of my personal thoughts, insights, perception, and tips on how to navigate the exciting world of data science and business intelligence.
[My BI Path | Part-2] Unlocking the World of Business Intelligence: A Guide to a Rewarding Career
[My BI Path | Part-3] Unlocking the World of Business Intelligence: A Guide to a Rewarding Career
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Reading: Achieving business impact with data
Reading: An experience of a “Data Ecosystem”
