Congratulations, you've landed a new job as a data analyst! Data analytics is a fast-growing and exciting field that requires a combination of technical, analytical, and communication skills. But what do you need to know to succeed in your new role? Here are some tips to help you prepare for your first day and beyond.
Data analysts use a variety of tools and software to collect, process, analyze, and visualize data. Depending on your specific job, you might need to be familiar with different programming languages, databases, frameworks, and platforms. For example, you might use Python, R, SQL, Excel, Tableau, Power BI, or Google Analytics. Make sure you know the basics of the tools you'll be using, and brush up on any skills you're not confident about. You can also ask your manager or colleagues for recommendations on what to learn or practice.
- The organization's structure, goals, and culture. - The team's processes, tools, and systems. - The data sources, types, and formats. - The team's data analytics methodology and standards. - The stakeholders and their needs. By understanding these aspects of the job, you'll be able to hit the ground running and start contributing to the team's success.
Starting as a data analyst, mastering the tools of the trade is paramount. Acquaint yourself with programming languages like Python, R, and SQL, along with tools such as Excel, Tableau, Power BI, or Google Analytics. Seek guidance from colleagues or managers to prioritize your learning, ensuring proficiency in the tools relevant to your role.
As a new data analyst, prioritize these three areas for a successful start: 1. Understand the Business Context: Grasp the industry and company goals to align your data analysis with organizational objectives. 2. Data Infrastructure: Familiarize yourself with the databases, data warehouses, and tools like SQL, Python, or R used in your company for data analysis. 3. Communication Skills: Develop the ability to communicate insights effectively to both technical and non-technical stakeholders, using clear visualizations and presentations. Focusing on these areas will help you make a significant impact in your role as a data analyst.
I agree that familiarity in software such as Excel for spreadsheet analysis, SQL for database management, Python or R for programming, and Tableau or Power BI for data visualization is often essential. But most importantly, an analyst must not only be competent in using these tools but also remain adaptable to learning new technologies as the landscape evolves.
Understanding the essential tools in the data analyst's arsenal, including SQL and ETL tools like Azure Synapse, ADF, or Talend, alongside proficiency in Business Intelligence platforms such as Power BI or Tableau, is crucial for success in the role. Mastering these tools empowers analysts to efficiently analyze data and craft insightful presentations, ensuring valuable insights are effectively communicated.
Data is the raw material of data analytics, and you need to know where it comes from, how it's stored, how it's accessed, and how it's cleaned. Data can come from various sources, such as web analytics, surveys, social media, sensors, or transactions. Data can be stored in different formats, such as CSV, JSON, XML, or databases. Data can be accessed through APIs, queries, or scripts. Data can be cleaned by removing duplicates, outliers, missing values, or errors. You should be able to perform data exploration, data manipulation, and data quality checks using your tools.
The most important part is… “it’s past midnight, do you know WHERE your data is?” New analysts are well skilled but cannot be effective if they cannot locate or understand the data. New questions often turn into “here copy this SQL code which has the table names and change a few things”. Investing in a proper onboarding process that walks thru the business meaning of the data, data dictionaries, etc. will pay big dividends. We started a “data tours” series that has proved super valuable to both new and seasoned analysts.
Know how to collect data and standardize it in a usable easy to understand format! Data is useless unless people know what it's telling them, and all data has meaning????
In your role as a data analyst, understanding your data is crucial. Know its origins, storage, access methods, and cleaning procedures. Data may derive from diverse sources like web analytics or transactions, stored in formats like CSV or databases, and accessed through APIs or queries. Proficiency in data exploration, manipulation, and quality checks is essential using tools like Python, SQL, or Excel. This ensures accurate and meaningful analysis.
Data Analyst primarily needs to have strong analytical skills. No matter which data he/she is looking in to; he/she must know and expertise in what are the different steps, concepts, processes, tools , technologies needs to be used to determine/pull out the required insights is the real skill of a Data Analyst.
In my experience knowing your data is the most crucial point, understanding what the data means, how different data stores connect to each other, what are the right filters, where are the outliers and how to validate it will set you up for success as a trusted data analyst on your team. Leveraging your understanding of the data you can start generating insights about what are the important metrics, what is trending, and what is interesting. Anyone can put a report or chart together, however to establish your career you need to thrive for accuracy. The most important thing to know is “Generating the wrong insights is worse than not generating any insights at all!”
Data analysis is the process of applying statistical and logical techniques to extract insights and patterns from data. Data analysts use different methods and models depending on the type and purpose of the data. For example, you might use descriptive analytics to summarize the data, inferential analytics to test hypotheses, predictive analytics to forecast outcomes, or prescriptive analytics to recommend actions. You should be able to choose the appropriate analysis method for your data, and apply it using your tools.
Understanding data analysis is pivotal for a data analyst. Employ statistical and logical techniques to derive insights and patterns. Methods like descriptive analytics summarize data, inferential analytics tests hypotheses, predictive analytics forecasts outcomes, and prescriptive analytics recommends actions. Proficiency in selecting and applying the suitable analysis method using tools like Python, R, or statistical software enhances the efficacy of your data-driven decisions.
Siendo analista de datos no dudo de tus habilidades matemáticas, en la que podrás hacer diferentes tipos de técnicas estadísticas para llegar a una solución, sin embargo, es fundamental que tengas sensibilidad del negocio, que entiendas la industria, la operación de la empresa y su dia a dia, de manera que tus análisis puedan ser implementados y generen un impacto real en la operación. Evita ser el analista que solo propone soluciones de papel y que no entiende la operación del negocio.
Once again, understanding the business goal(s) is an important aspect for your analyses. There are four type of analysis; - Descriptive: This is a summarisation of the data. It is the first step in any analysis, it tells you the answer to the "question of what". - Diagnostic: The seeks to answer the "why did this happen?". For example, there's a dip in sales for a product in June, as against other months. The analytical team would be tasked with knowing why this happened. - Prescriptive: This follows findings of Diagnostic analysis and essentially, recommends solutions towards the case. - Predictive: This uses the given data and machine learning approaches for the forecasting and prediction of future outcomes.
Know your analysis.! Knowing your analysis helps you know what tools you need to analyze the data, the insights you can draw, and what needs to be visualized. Then you can communicate this effectively to the stakeholders for strategic decision making.
Meet the Data Makers: Data doesn't magically appear (although sometimes it feels that way!). Get to know the folks who generate and manage the data you'll be analyzing. This could be the IT team, sales department, or marketing folks. Build relationships with them early on. Understanding their data collection methods and any challenges they face will save you time and frustration in the long run. During my first week at a retail company, I met with the sales team to understand their point-of-sale system and how they categorized customer purchases. This knowledge came in super handy when I needed to analyze buying trends by product category.
Data visualization is the process of presenting data in a graphical or interactive way to communicate findings and stories. Data analysts use different types of charts, graphs, maps, dashboards, and reports depending on the audience and message. For example, you might use a bar chart to compare categories, a line chart to show trends, a pie chart to show proportions, or a scatter plot to show relationships. You should be able to design and create effective and appealing visualizations using your tools.
Data literacy and technical maturity of the greater audience plays a vital role on the visualisation of data. Also depending on the use case the visuals can be operational, C-suite or managerial. Knowing your audience after knowing your data should be the main task before visualising data
In my opinion, visualization step should start from knowing the audience the visualization is being made for. Reason that the whole purpose of visualization is to communicate all the intricate details in a way that your audience can make sense of all that information. Keeping it simple is the key.
Data visualization is an important skill to be successful as a data analyst. After the analysis is done, knowing key indicators that are impactful and related to why the analysis is conducted would help keep track of the message to share with the stakeholders. Data visualization is all about presenting findings and creating an effective story that the stakeholders can use for making a decision.
Visualization serves as a powerful tool in the data analysis process, transforming complex datasets into digestible insights and narratives. It involves presenting data in graphical or interactive formats to effectively communicate findings and stories. Proficiency in designing and creating compelling visualizations is essential for data analysts. This entails not only selecting the appropriate visualization type but also utilizing tools adeptly to ensure clarity and appeal. By mastering visualization techniques, analysts can unlock the full potential of data, empowering stakeholders to make informed decisions and derive actionable insights.
Simplicity is the key when it comes to data visualization. The primary goal is to make complex data understandable at a glance. The best visualizations are those that convey the message with minimal explanation needed. It is a crucial skill to master the art of simplifying visualizations without oversimplifying the data - this is about finding the balance between completeness and clarity, ensuring that the visuals are both informative and intuitive.
Data communication is the process of sharing and explaining data analysis and visualization to stakeholders and decision-makers. Data analysts need to have strong communication skills to translate complex and technical data into simple and actionable insights. You should be able to write clear and concise reports, give engaging and informative presentations, and answer questions and feedback. You should also be able to tailor your communication style and language to suit different audiences and contexts.
Communication is the key. If you miss any vital assumption from stakeholders or you do not validate your assumption with the right stakeholders then at the end you will have " Garbage in and Garbage out"
Communication is vital in data analytics for several reasons. Firstly, clear communication ensures that stakeholders understand the insights derived from data analysis, enabling informed decision-making. Secondly, effective communication facilitates collaboration among team members, enhancing the efficiency and accuracy of analysis processes. Lastly, feedback loops enabled by communication allow for iterative improvements in analytical approaches, leading to more robust and valuable outcomes. In essence, communication bridges the gap between data analysis and its practical implementation, driving the success of data-driven initiatives.
After all is said and done , This is where the essence lies. It’s the Data story that matters most, when it come to implementing your analysis. Can you place all of the analysis into a data story that indicates what action should be taken to mitigate/expand? Can you make people convince of the result of the analysis? ?? Is the consumer of the insights able to connect the dots? Your job as a Data Analyst is understanding a consumers pain points and delivering the exact solution, that he doesn’t know exists.
In data analytics, the second important thing after data understanding is the communication of analysis results through clean and clear visualisation, in short “Data to meaning”. Some of the common wrong practices are: 1. Displaying every thing performed on the data, 2. Crowded dashboards, 3. Complex story flow resulting in unclear action items. Being confident by knowing the meaning of analysis results helps in communicating critical action items. There are numerous story telling platforms like Tableau, Bower BI and PowerPoint etc available for analytical communication.
Data analysis is all about making sense of information and sharing those insights. Here's how to be a communication champion: Clear and Concise Reports: Write reports that are easy to follow, with well-explained findings and visuals. Speaking the Audience's Language: When presenting, tailor your message to your audience's technical background. Avoid jargon and use clear, impactful language.
Data analytics is a dynamic and evolving field that requires continuous learning and improvement. Data analysts need to keep up with the latest trends, technologies, and best practices in the industry. You should be proactive in seeking feedback, learning new skills, taking on new challenges, and expanding your network. You should also be open to collaboration, experimentation, and innovation. You should have a growth mindset and a passion for data.
Embarking on a career in data analytics requires a perpetual commitment to growth and adaptability. In this dynamic field, staying relevant means actively pursuing continuous learning, seeking constructive feedback to refine analytical skills, embracing versatility and adaptability to navigate diverse challenges, cultivating a network within the analytics community, and fostering a passion for innovation. A growth mindset is not merely a recommendation; it is the compass that guides data analysts through the ever-evolving landscape, ensuring their journey is marked by resilience, curiosity, and a relentless pursuit of excellence.
The world of data analysis is constantly evolving. Stay ahead of the curve by: Conferences and Courses: Attend industry events and online courses to learn new skills and stay updated. Industry Publications: Subscribe to publications like "Harvard Business Review" or "Towards Data Science" to stay informed about the latest trends.
Data analysts should also use an experienced person's knowledge to give meaningful insights. Then only insights are meaningful and relatable to different conditions.
Researching and working with a domain expert goes a long way in analytics. You most likely would be working with several people from different domains and they would want to better understand their data. Your interpersonal skills/ soft skills would play a big role as you communicate and understand the requirements needed for a given business goal. Never neglect your soft skills.
You should also gain an understanding of the business context how the data is being used. Speak to the operational end users if possible. Ask how the information is being used, what is good and about the data products being provided, does the way the data products are being served to them work well?
Lay the right foundation for any data project by investigating and understanding the quality of the data you’re working with. Garbage in, garbage out. Take the time to check assumptions about trends, outliers, consistency, timeliness of datasets.
Know your business, customer and consumer. Having the understanding of environment from where your data originated would enable you to have the true understanding of what it means. As they say, data analysis is story telling through data. To tell a good story you need to first understand the context, characters, and all such variables. This understanding will make the difference from very first step of understanding the Business problem and will continue till visualisation/storyboarding.
Only focusing on tools and techniques would not help. Focus should be to understand business and visualize business problems (like Working capital issues etc.) using appropriate measures/KPIs. This will help the decision maker to find the root cause.