What does a Data Analyst do? A Day in the life of Mukul Kumar

Morning: Data Collection and Cleaning

Mukul takes time and invests a lot of effort in the time-consuming but very cardinal aspect of data collection and data cleaning. He extrudes information from diverse sources, such as customer relationship management platforms such as Power Bi, which contain broad customer information, sales history, and feedback received. Hootsuite and Sprout Social provide valuable statistics on the users’ engagement and their attitude towards the brand. Google Analytics provide essential performance indicators that measure the number of hits and user characteristics on the website.

But this is where the collection of this data also begins. Data cleaning is a process that occupies roughly 80% of a data professional’s time, as IBM has reported, and this is the area where Mukul shines. This process is time-consuming since several problems, including contradiction, redundancy, or the lack of values, must be solved. For instance, data from various sources arrives in different formats; hence, there is a need for Microsoft Excel and Talend to format the data sets. Records that refer to the same entity are found and merged using techniques like exact matching and fusiform matching, where rules like the Levenshtein is used to identify somewhat similar entries. In cases where a specific data field is missing in a record, different imputation methods are used for a record with missing values, such as the Mean Imputation method or KNN imputation method, and record interpolation techniques that come up with missing data from other documents.

Data cleaning is one of the most crucial steps in data preparation. The clean data type is credible, hence enabling the drawing of valuable conclusions that, in turn, inform decisions. For instance, clean data improves the efficiency of marketing communications as they reach the right audience with the best message, and therefore, marketing outcomes are optimized. Software tools like Alteryx and Trifacta are used for data blending and preparation, while error correction routines include regular expressions and data validation rules. By applying these tools and techniques, Mukul enhances the quality of data and the efficiency of the entire analysis. Therefore, the time spent cleaning data is worth it since it forms the basis of data analysis and strategic decision-making.

Midday: Data Analysis

From mid-morning onwards, Mukul gets rightly engrossed in the core of the data analysis efforts. Sustained exertion from the morning is replaced by more meticulous work, which is very intense and touches on more profound data analysis, devoid of frivolity that might accompany a morning rush, with the aid of powerful statistical tools. Python, his weapon of preference, and its sturdy frameworks, such as pandas and NumPy, are the ground he pans for gold regarding patterns and trends concealed in the big data.

While moving through the data, Mukul employs several tools and advanced data analysis methods to provide practical insights. For instance, he applied logistic regression to the customer churn issue, giving him spectacular results. This is not simply a piece of data; this is a clear and powerful predictive evaluation that can successfully estimate which customer can potentially churn and why, providing optimum prospects for businesses to keep those customers.

The upset that such advanced analytics have is significant. The strategies provided by McKinsey also show how advanced analytics can increase efficiency by 20% in various industries. This statistic does not represent just a figure, but rather, it is a story of how insights can help revolutionize a company’s activities. Indeed, every model Mukul unfolds, and each algorithm he implements processes data and heralds improvements and better planning.

His analysis might show that some modes of marketing have boosted the customers’ engagement more or that other changes in operations could enhance efficiency. This work is not limited to influencing the decisions; it forms the future path of the company’s evolution, which reveals that in data science, each piece of insight can be the key to making a monumental change.

Afternoon: Reporting and Visualization

As the afternoon unfolds, Mukul shifts gear to one of the most impactful aspects of his role while creating visualizations. He leverages one of the most powerful tools in data analysis, Tableau, to develop interactive dashboards, making large volumes of data understandable and easy to use. It is critical to note that these, by no means, are mere COOs; instead, they are the dynamic narratives that shed light on the data into easily understandable visuals. I also incorporated the idea of utilizing key dashboards where stakeholders can follow the information in real time, which is essential whenever decisions must be made promptly, as seen in Mukul’s designs.

The potential inherent in effective data visualization is best illustrated by the fact that it can help minimize the time used in arriving at decisions. Standards imply that decision-making time can be reduced by up to 30% due to sharp visualizations. This efficiency is obtained through data communications in such a format that allows decision-makers to understand trends and other relevant facts for action.

Apart from visualizations, the detailed structure of Mukul’s reports is crucial as they mainly help to turn data into action. His reports are well-prepared, down to the last detail, where he makes recommendations for the future agenda. Forrester notes that when such accurate information is reported, it can result in a potential increase in revenue of up to 8%. This impact is necessarily entailed by Mukul’s orientation and strengths of seeing patterns and trends as well as converting these into actionable business initiatives. Every report he compiles assists in a decision that is being made or planning that is being done, thus affirming the importance of his position in the firm.

Late Afternoon: Collaboration

As the day progresses, Mukul transitions to one of the most collaborative aspects of his role: practicality and interdepartmental cooperation. On a daily basis, he collaborates with the marketing and product departments and engages in detailed conversations to determine their needs and issues. Such an association is critical, enabling Mukul to translate his technical outputs into business solutions.

By helping to consolidate and then present large amounts of data analyses in a digestible manner, such as in the above example recommendation concerning repeat purchases, Mukul ensures that the marketing department can review their recently implemented campaigns and adjust their approaches and that the product department can make accurate conclusions regarding product development and enhancement. The integration of analyzing data and departmental objectives is beneficial when matching strategies and performance enhancement.

This is especially true with operational communication, as it is defined next. Deloitte has provided statistics regarding the extent to which clarity of communication can affect stakeholder engagement and, thus, company success — up to 25%. Mukul’s detail-oriented approach to communicating technical findings for non-technical persons drives improved cooperation. His practical interpersonal skills make it easier to ensure that everyone is well-informed of the results and is goal-tuned to achieving established goals and objectives.

In this process, Mukul’s statistics transform beyond mere figures and serve as the impetus for organizational strategies and plans for specific marketing activities or productions, product launches, or the overall advancement of business objectives.

"Working closely with different departments helps me understand their challenges and tailor my analysis to provide the most value," says Mukul, reflecting on the core of his Data Analyst role. His approach emphasizes the significance of collaboration and strategic insight in the data industry. For freshers and those considering a career switch to the data industry, Mukul offers the following valuable advice supported by industry facts:

• Understand Business Operations:One should dedicate time to learning how other departments operate and their issues. As Deloitte reported, successful organizations rely on data and seek to understand departmental necessities to make appropriate decisions.
• Master Technical Skills:While technical know-how is essential, ensure you are up to par with data analysis tools and methods. Data skills are in demand more than ever; statistics from the US Bureau of Labor reveal that jobs for data analysts will grow by 36% between 2021 and 2031. Therefore, it is advised that one must be very technical.
• Develop Strong Communication Skills:Communication brings out the best in stakeholder relationships. McKinsey notes that good communication can increase stakeholder engagement by as much as 25%. Start explaining technical matters to other stakeholders to link data to business.
• Seek Feedback and Collaborate:One should go out of his way to get feedback. A Harvard Business Review study indicates a 20% likelihood of superior financial performance if cross-departmental communication is vital.
• Stay Curious and Adaptable:There is often a fit in data projects and ideas, explains the Data Science Society; 70% of the staff in the data professions agree that constant education is necessary to stay up to date with the latest technologies and methods.
• Focus on Problem-Solving:Problem-solving skills should always be applied to data, according to the Data Visualization Society. This is mainly because employers prefer problem-solving skills in data professionals when tasked with crucial problems.
• Leverage Data Visualization:It helps turn your work into reachable insights that can influence decisions. According to a recent Gartner report, data visualization tools like Tableau and Power BI have increased organizations' decentralization by 30 percent because of improved interactive reports.
• Build a Network:One can learn much from other people in the data field about where to search for jobs. LinkedIn data suggests that 85% of jobs are obtained through networking.
• Understand Data EthicsBe aware of data privacy and ethical considerations. The General Data Protection Regulation (GDPR) has set a high standard for data protection, and non-compliance can result in fines up to €20 million or 4% of global annual turnover, emphasizing the importance of responsible data handling.

Mukul observes that the demand for data analysts is booming as companies increasingly value data-driven decision-making. This trend is backed by several key industry statistics specific to the UK:

• Job Growth Projections:According to the UK government's Labor government's view, the demand for data analysts and data scientists is projected to grow significantly. The Office for National Statistics (ONS) reports that data science and analytics roles are among the fastest-growing jobs in the UK, with a projected increase in demand of up to 40% by 2025.
• Industry Demand:The UK's Data Skills Task Force highlights that the data and analytics sector is critical to the UK economy, contributing over £200 billion annually. This contribution reflects data analysis's integral role in driving business decisions across various sectors.
• Investment in Data Analytics:A report by the UK's Centre for Data Ethics and Innovation (CDEI) reveals that businesses in the UK are increasingly investing in data and AI technologies to enhance their operations. Over 60% of UK businesses surveyed plan to increase their investment in data analytics in the coming years.
• High Return on Investment:A study by the McKinsey Global Institute indicates that data-driven businesses in the UK experience up to a 10% increase in productivity and a 5% boost in profitability compared to their peers. This underscores the substantial impact of practical data analysis on business performance.
• Growing Demand for Skills:According to LinkedIn's 2023 UK LinkedIn's Rise report, data analysis is among the top emerging skills in the UK job market. The report highlights a significant increase in job postings for data analyst roles, reflecting the growing demand for data expertise.
• Salary Trends: The UK's National Career service reports that the median annual salary for data analysts in the UK is approximately £45,000, with the potential for higher earnings depending on experience and location. This competitive salary reflects the high value of data analysis skills in the UK job market.

The data analyst field in the UK is experiencing robust growth, driven by the increasing importance of data in shaping strategic business decisions. This expansion offers numerous opportunities for professionals in the field, underscoring the data analysts role.

Mukul highlights an appealing aspect of a career in data analysis: they are likely to make a large amount of money. The starting salary of Data Analysts in the UK ranges from £23000 to £35000, depending on their place of work, their educational level, portfolio and the industry in which they work. However, one can expect to earn towards the higher end of this range if, for instance, one starts as a junior or senior associate in London or with one of the largest firms.

The analysis observed that as professionals advance in their chosen careers, their earnings prospects rise sharply. It is not hard to earn six figures for highly experienced data analysts, especially if they are in the fields of advanced analytics or are involved in industries that highly demand data analysts. Exploring the career options from the United Kingdom's Careers Service, the professionals who work as senior data analysts or managers get paid £70,000 or even more; sometimes, the yearly salary can go up to £100,000.

The target income, which can be rather impressive, is considered one of the most persuasive reasons for people considering this kind of job. The relatively high starting pay, together with the high possibilities for a pay raise after gaining experience and skills, also speaks for data analysis as a profession.

Although Mukul finds his job very fulfilling, he has to face some issues in performing the duties of a Data Analyst. These hurdles underscore the complexities of the profession and the skills required to navigate them effectively:

• Keeping Up with Evolving Tools and Technologies:Data analysis as a field is evolving, and there is a tendency for new tools and technologies to appear rather frequently. Mukul also has the ethos of self-improvement and practices on the current methods in the market together with the existing software. In a report by the Data Science Society, the professionals reported that they need to learn sequentially to cope with new technological developments, which makes up 74% of responses.
• Ensuring Data Privacy and Security:Security and protection of personal data are priorities today based on data usage. Mukul should handle personal information well, including specific rules like the GDPR.
• Managing Large Datasets: •Managing and analyzing large datasets can be challenging as organizations generate and collect vast amounts of data. Mukul uses advanced tools and techniques to handle big data effectively, but the sheer volume can be overwhelming.

However, these are the challenges that Mukul has identified in his work as a data analyst, although he enjoys problem-solving in this line of duty. This is particularly the case when the data are complex, and he is inspired by the chance to turn the latter into solutions for vital business issues. The fact that I have achieved all these challenges and made a real contribution to business decisions makes the position of a data analyst complex and rewarding.

To Mukul, the role of Data Analyst is very fulfilling, especially from the perspective of translating a collection of facts into wisdom for decision-making. However, what he enjoys most in his work is identifying how his analysis and work influence the business's strategic directions and performance.

• Turning Raw Data into Insights:Mukul's most enjoyable task was data processing, which entails transforming raw data into useful information. Using the data and the recognizable trends, he assists corporate organizations in decision-making, which can significantly help in performance and strategy alteration. I wouldn't want to state that this shift from data to usable intelligence is a work of art that Mukul genuinely enjoys.
• Impact on Business Strategy:Mukul mentioned that he enjoys seeing his duties impact the business's performance. The opportunity to offer information that may influence changes in the marketing approach, improve the company's competitiveness, or improve the product is very fulfilling. The report by Forrester further shows that business organizations that harness data outcompete those that do not by a factor of 5 regarding decision-making speed and results.
• Motivation Through Results:The material fruits of his work are the incentives that continue to inspire Mukul. Knowing that the analysis can lead to more revenues, satisfied customers, or effective operation is a strong incentive. Such a positive correlation between the company's efforts makes him enthusiastic about the field and challenges him to develop further and be creative.

The Data Analyst's responsibilities are diverse and show how data analysis can be helpful in many ways. Here is a look at some of the key projects he works on:

• App Rating Recommendations:Mukul also improves usability by evaluating app ratings and making suggestions about the best applications based on user feedback. This project is focused on compiling and analyzing user reviews and ratings to discover relevant and appreciated apps.
• Comcast Telecom Customer Complaints:In this project, Mukul forecasted customer complaints regarding Comcast Telecom's issued services to enhance its service delivery system. Using complaint data, he analyzes frequent problems and complaint patterns and provides the company with the solutions.
• E-commerce Sales Dashboard:Mukul designs performance reports for e-commerce as dashboards. These dashboards offer current information on the company's sales patterns, including other vital sales indicators, to help the business make the right decisions and evaluate its performance.
• Comparative Study of Countries:Through the numbers, comparing the different socio-economic variables, and considering other countries, Mukul offers a palpable perception of global differences. In terms of use, this comparative analysis assists in evaluating the relative performance of different nations regarding attributes like gross domestic product, education, and health.
• Sales Performance Analysis: In this project, Mukul analyzes sales records to make conclusions about sales tactics and potential developments. He is a wizard at Mapping Revenue Sales, identifying key areas of improvement, and increasing sales productivity for the business.

These projects demonstrate the scope of the domain across business areas and for leveraging data analysis to enhance products or services, and effectiveness of financial investment. Every project illustrates his abilities to analyze data and make changes that produce tangible results.

This day depicts the importance of data analysts in today's world, where data is critical. From collecting and cleaning the relevant data to the stages of analysis, reporting, and cooperation with other departments, their roles are the underlying platforms of decision-making and planning today.

Thus, using their skill set, Mukul and other data analysts are crucial data professionals whose primary focus is transforming raw data into meaningful information that supports business success. Through patterns, advice, and problem-solving strategies, they can ensure that organizational decisions are informed and innovation in competitive environments is achievable. With the advancement in data volumes and importance, the position of Data Analysts will be highly valued more in the future.

Explore more about starting your career as a Data Analyst by visiting the Fortray Data Analyst Program.