What will you learn as a data analyst in the next decade?

Cloud computing plays a crucial role by allowing analysts to concentrate on data exploration & pattern recognition without being burdened by infrastructure concerns. This is because cloud services provide a plethora of managed services for nearly every analytical task. Cloud computing offers optimal system configurations for seamless analysis of extensive datasets, ensuring speedy data exploration. Cloud vendors offer a range of managed services at different levels, spanning data warehousing, DB, orchestration, ETL processes, and even BI. This facilitates businesses in swiftly deploying production-ready solutions within weeks, as opposed to the traditional time frame of months/years, all without substantial upfront investments.

Cloud computing is a lot more common in data analysis than you think. Azure, AWS, and GCP are the top 3. Professionally I have been apart of project where databases are running off of AWS servers. Know the fundamentals, how data is stored and extracted from the cloud can definitely make you stand out as an analyst.

While cloud computing will be an essential part of the data analytics in the near future, a data analyst should first learn about the fundamentals before jumping to cloud computing. For example, a data analyst should learn about data lifecycle, data cleansing, data mining, exploratory data analysis and some programming experience like SQL, Python or/and R. Then he or she could start learning cloud computing.

Cloud computing plays a core role in data engineering, delivering reliable, maintainable, and scalable benefits. It removes the need for local hardware, assures flexibility, and assists in security and compliance. Cloud services streamline processes and integrate smoothly with DevOps methods, while their worldwide presence increases performance. Yet, one of the extremely crucial factors that is mostly unseen is the lowering of administrative hassles. Cloud providers perform maintenance duties, upgrades, and patches, enabling data engineers to focus on innovation and analytics rather than normal upkeep, thereby boosting data driven insights and decision making processes which sometimes may require more maintenance than initially planned.

In data analytics, cloud computing enables scalable storage, processing, and analysis of large datasets. It provides on-demand resources, accelerates computations, and facilitates collaboration. Cloud platforms like AWS, Azure, or Google Cloud offer data services, allowing analysts to focus on insights rather than infrastructure management. Embracing cloud solutions enhances flexibility and efficiency in data analytics workflows.

1. As more and more tasks become automated, skills such as story-telling and strategic thinking will become more important for data analysts. 2. For a long time, tools such as SQL and Excel have been sufficient for data analysts. However, in the modern world, learning Python is becoming a requirement for most data analysis roles. This is because data analysts are more than ever required to use machine learning and deep learning in their work. At the moment, Python is the number one language in the artificial intelligence space, and it is projected to continue to dominate in the foreseeable future.

AI’s potential in reshaping data analytics is vast and profound. One application that stands out is propensity modeling. As organizations become more sophisticated in their operations, they are turning to statistical techniques like propensity modeling to predict the likelihood of certain actions by visitors, leads, and customers. With the integration of machine learning, we can now predict these event likelihoods with greater accuracy, considering consumer attributes and past behaviors. Given the trajectory of AI’s influence on data analysis, mastering propensity modeling and similar applications will be essential for data analysts looking to stay ahead of their peers.

Learning Python will be essential to fully understand and leverage AI at a technical level. Having learned R many years ago, I now recommend to everyone they learn Python in today's market given the AI wave that has made it the indispensable language of the future.

In increasingly data-driven world, the ability to extract insights and make informed decisions from vast amounts of information is paramount. Python, with its user-friendly syntax and powerful data analysis libraries, has emerged as the go-to language for data professionals. As we move into the future, where data is the lifeblood of innovation and progress, mastering Python for data analysis becomes not just a skill but a necessity. It empowers individuals to navigate the data-rich landscape, uncover hidden patterns, and contribute meaningfully to fields ranging from business to healthcare, ensuring their relevance and impact in the data-driven era ahead. Python is a language which speaks to anything and everything so we must hurry!!

Data ethics is necessary to protect privacy, build trust, avoid discrimination, comply with laws, maintain reputation, foster innovation, prevent harm, respect autonomy, facilitate global data sharing, and ensure the long-term sustainability of data-driven technologies and industries. We should have more clear and comprehensive guidelines from government so no one can mishandle the data as these are our most precious assets!!

In my own view, big data ethics is important for anyone to become a data analyst in the next decade. Unconventional methods, for example, web scraping are normally applied for big data collection, however, ethics guidelines of these unconventional methods require urgent research.

Data ethics is the storyteller in our digital age, weaving empathy into algorithms. It transforms data into narratives, making privacy a sanctuary and transparency a radical honesty. In this tale, diversity is celebrated, biases are challenged, and the data scientist becomes a moral custodian. It's not a constraint but an innovation catalyst, urging a global commitment to responsible practices. Data ethics is the golden thread in our technological tapestry, illuminating a path where progress and human values coexist harmoniously, shaping a future where data powers societal betterment.

In today's data-driven landscape, managing the vast amounts of daily retrieved and manipulated data demands a strong focus on data governance, masking, and ethical guidelines to safeguard sensitive information. The increasing influence of AI heightens the imperative for data analysts to grasp cybersecurity intricacies, cloud computing, and privacy ethics, considering the risks involved. Moreover, it's essential for nations, particularly developing ones, to prioritize the establishment of actionable data protection and privacy laws. These laws play a critical role in governing data usage responsibly.

Data ethics in data analytics involves responsible and transparent handling of data. It includes ensuring privacy, obtaining consent, and preventing bias in data analysis. Practicing data ethics requires compliance with regulations, honest communication about data usage, and a commitment to fair and unbiased decision-making throughout the analytics process.

Data storytelling is essential for achieving results. Data analysts need to understand what their audience needs to know, do, and feel in order to achieve their objective. The storyline needs to make sense horizontally and vertically. Storytelling bridges the gap between the technical analysis and the business decision.

Data storytelling can be either an individual or a team sport. In the former, an analyst must have enough technical and business knowledge to acquire, scrub, analyze, and visualize relevant data to make a succinct argument leaders will understand. In the latter, SMEs from each respective area must collaborate to achieve a similar (or better) result. Imagine a quadrant with speed and quality on the axes. Different resourcing approaches will fall into different spaces in the 4x, but the large organization’s ultimate goal is for cross-functional teams to repetitively land data storytelling in the high-and-right space at scale.

In storytelling, the incorporation of domain knowledge is akin to adding color to a canvas, bringing depth and nuance to the data-driven narrative. This collaborative approach not only ensures a more comprehensive understanding of the data but also fosters effective communication with stakeholders who may lack a deep technical background. Artificial Intelligence (AI) plays a pivotal role in acquiring and leveraging domain knowledge. Through natural language processing and machine learning, AI tools can sift through vast amounts of domain-specific information, extracting key insights and trends.

Data is at its best when we use for storytelling Some key points might be considered: 1. Learn from resources such as books and online courses. 2. Practice with real data to apply your knowledge. 3. Utilize data visualization tools and libraries. 4. Attend workshops and webinars for hands-on experience. 5. Analyze examples of effective data storytelling. 6. Engage in online data visualization communities. 7. Seek feedback to refine your skills. 8. Practice regularly to improve. 9. Stay updated with the latest trends. 10. Emphasize context and impact in your data narratives.

Practice your storytelling. I was never very good at it and still am not great. But I have practiced and honed areas that have led to some great successes because of my ability to communicate the story the data is telling. There are many courses and books out there that are immensely helpful in improving your storytelling, so go out, find them, and get to work. Don't forget to practice with family and friends (this can be easily done at holidays) and take note of what does and does not work with that group. Then use the concepts that work with them at work.

On the journey to becoming a competent and reliable data professional, this is so important. Being able to understand and execute on tasks requires a thorough understanding of the industry as well as continuous upskilling in this ever-evolving field

Organizations should invest in a Data literacy program for all employees. With the rise of big data and AI, organisations that does not use (their) data, will be left behind by competitors. When you have a Data literate workforce, they will have the insights to pick up any anomolies and outliers as well. This training must be implemented in the whole organisation with "buy in" from the respective departments and senior leadership. When dealing with clients, it would be useful to cover some key data terms as well.

The cost of technical knowledge in the next 10 years will fall off a cliff. It used to be that we would learn concepts bottom-up, especially in school. We learn about the technical stuff, the 'what', and at one point it culminates in the 'how' and the 'why', which is referred to as experience. AI will replace data analysts for the 'what', but not for the 'how' and 'why', at least not yet. In my opinion, the role of a data analyst will increasingly resemble that of a product manager as it is today. Data literacy will therefore be crucial for any data analyst, and it will be one of the main differentiator in achieving success.

Data literacy in data analytics refers to the ability to understand, interpret, and communicate insights from data. It involves the skills to analyze data, comprehend visualizations, and make informed decisions based on data-driven information. Developing data literacy is essential for effective collaboration and decision-making within an organization's analytics processes.

Data literacy is crucial for interpreting and using data effectively, not just for analysts but for anyone dealing with data. It's key for informed decision-making and innovation. As a data analyst, enhance your own literacy and help others develop theirs. Learn to teach data concepts, facilitate discussions, provide feedback, and nurture a data-centric culture in your organization.

Being a more "full-stack" Data professional adds a lot of flexibility to your resume and allows you to fit into multiple roles in the industry. Along with the traditional Data Science and Analyst skill sets, it's really valuable to get some practice on more Data Engineering and Analytics Engineering tools: - Data Ingestion: Kafka is the most popular one for streaming data - Data Storage: Get some hands-on experience with Amazon S3 and GCS - Data Warehousing: Amazon Redshift and Google Bigquery - Data Orchestration: Traditionally Airflow or Mage if you want to try a newer tool - Data Transformation: DBT is the gold standard here - Version Control: Essential to learn the basics of Git

I see data analysts leveling up their skills in the future and being more involved in the designing, storing, organizing, and cleaning of data in databases. If data lakes become the norm, we can expect to see data analysts required to retrieve, prepare, and then analyze data in a quick and powerful process.

I’m currently in a position as a “Full Stack Data Scientist” and knowing the fundamentals of Data Engineering and automating pipelines are crucial in my position. I’m not sure how this works in Fortune 500 companies, but if you’re in a startup you will definitely wear multiple hats beyond just your typical analyst role.

As we design data pipelines, it's pivotal to think about data's end-to-end journey. This means considering real-time error handling, ensuring smooth ETL processes, and even thinking about how easily the final data can be accessed by non-tech teams.

Data engineering is one field i have fallen in love with and the trajectory of it in the future would be crucial. Data engineering is the process of deploying, building and maintaining infrastructure of data analysis. With the growth of AI and modern technologies more data sources are created and more curated data would be needed for analysis, Machine model training and much more. The future of Data Engineering is critical for the future.

In the next decade, data analysts will need more than just technical skills. They should also possess a deep understanding of business concepts. They must genuinely comprehend how data management can impact a business, collaborate closely with business units, and contribute to driving the company forward.

Here are what you need to continue to practice: Data Analytics Problem Solving Skill, Analytical Thinking Frameworks, Critical Thinking, Communication and Teamworking. Although there are AI helpers, such as AI Copilot, ChatGPT, and Google Bard that can give you some Data Insights from your data, you still want to know Why your data are so, What made it or What are Influencers on your data. And then you need Data Problem Solving Skill and Analytical Frameworks to answer those questions, for you to understand your data better. These Data Skills are helpful for you, despite of time pass by or tools. You also need to improve Critical Thinking, better Communication and Teamworking. These skills are the fundamental skills for Data Analysts.

I think that data governance is vitally important for data analysts, as it forms the foundation for ensuring data integrity and trustworthiness. It's more than just organizing data; it's about guaranteeing its accuracy, availability, and security. In an era where data privacy and ethical handling are of utmost importance, the human skill in implementing governance policies and understanding compliance is indispensable. This human-centric approach in data governance is a key element, ensuring the reliability and effectiveness of data in informing decisions.

In the exciting world of data analysis over the next decade, get ready for some game-changing skills! Cloud computing is a must, allowing you to analyze vast datasets without the hassle of hefty hardware. Dive into AI, particularly ML and Python. Craft compelling data stories that go beyond numbers, bringing your analyses to life with narratives and visualizations. But that's not all! Data literacy isn't just for analysts; it's a valuable skill for everyone involved with data. Embrace data engineering, a crucial foundation for designing and maintaining the infrastructure that makes data analysis possible. It's like becoming a "full-stack" data professional, adding a whole new dimension to your skill set.

With the advancements in AI, writing code and even doing run-of-the-mill data analysis is going to take minutes instead of hours or days. Data Analysts who simply pull data, create dashboards, and dump numbers onto stakeholders are going to find it difficult to stay afloat. Domain knowledge and business acumen are going to be increasingly critical in the next decade. The role of data professionals will evolve towards providing deeper insights and strategic guidance as the technical aspects become incredibly efficient.