ATCON GLOBAL’s Post

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗱𝗼 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 𝗮𝘀 𝗮 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁! When you are interacting with stakeholders, you should follow these things: • Always stay solution-focused. This removes overhead from stakeholders. • Don't explain technical stuff to them. • Try to understand problems from their angle. • Ask them what they are looking for from this analysis/dashboard or more. • Keep things as simple as possible. • Always keep references when explaining insights. For ex: sales comparison of 2024 and 2023 for Texas. This ensures effective stakeholder communication. Also, this will build trust over time. Enjoy. Like, Comment, and Follow for more! #DataAnalyst #BusinessProblems #DataAnalytics #BusinessAnalyst #BusinessAnalytics #DataAnalysis #BusinessAnalysis #ProductAnalyst #DataAnalysisTips #AnalyticsCareer #BusinessProblems #CommunicationSkills #EffectiveCommunication #DataScienceCommunity #AnalyticsTips #DataWorkflow #AnalyticsBestPractices #DataProcesses #DataTypes #AnalyticsDocumentation #US #UK #Europe #Singapore #Germany #SQL #Python #PowerBI #Excel #AmazonRedshift #ABTesting #Remotework #GlobalRemoteWork #SalesAnalyst #HealthcareAnalyst #MarketingAnalyst #FinancialAnalyst #SalesAnalysis #FinancialAnalysis #HealthcareAnalysis #MarketingAnalysis #RemoteDataAnalyst #RemoteWork #USRemote #UKRemote

I recently had a conversation with a seasoned Data Architect who shared his frustration about calls he’s getting from agency and internal recruitment teams.                        This particular individual has an extensive background in data modelling and architecture, but he is consistently approached for technical Data Engineering roles, requiring skills such as SQL and Python scripting —skills he doesn’t have. Simply seeing "Data Architect" on a CV does not mean the candidate is a fit for all technical roles. Data Architects might have grown from a Business Analyst background, who honed their craft in areas like data modelling, data strategy, and architecture, rather than hands-on technical scripting. This sounds like pretty basic advice, and tbh it kinda goes without saying but … ·      Understand the role ·      Ask clarifying questions ·      Read CVs in detail And once you’ve grasped these 3 very simple steps, you might even be able to educate/advise/consult/influence the hiring manager. Imagine that? #Credibility #Recruitment #DataArchitecture #DataRecruitment #DataBI #RecruitmentFrustrations 

I have always wondered if a Data Analyst can work as a virtual assistant and I found out it's extremely possible. I would list a few skills where a Data Analyst can render his/her services as a virtual assistant. 1. Data Entry and Management. 2. Research and Data Visualization. 3. Scheduling and Calendar Management. 4. Email and Communication Management. 5. Bookkeeping and Invoicing. 6. Automation and Workflow Optimization. and so much more #DataAnalyst #PowerBI #MicrosoftOfficeSpecialist #TechMum #Chef

Why hire a fractional data engineer/data analyst? - Flexibility - Reduced risk - Access to expertise and a broader network Flexibility: You can scale the engagement depending on your business needs. You can hire fractional data people both for short-term and long-term projects. Great for businesses with fluctuating workloads. Reduced risk: With a fractional data engineer, you won't have to deal with financial overhead like benefits and other expenses. You won't need a rigid hiring process or longer-term commitments. Skill mismatch may be avoided as well. Access to expertise and a broader network: You don't have to train a full-time employee for months before they start providing you value. A fractional data engineer will be ready to dive into helping your business from day 1. Also, they most likely have their own network of other consultants. You may be able to expand your network through theirs and get appropriate resources and expertise for your projects. Have you hired a fractional data person? If so, what was your experience like? #independentconsulting #dataanalytics #dataengineering

🌟 **A Day in the Life of a Business Systems Analyst / Data Analyst** 🌟 Every day in this role is a blend of strategic thinking, data deep-dives, and collaborative problem-solving. Here's a snapshot of my typical day: 🔍 **Morning Analysis & Reporting:** I start my day by analyzing key metrics and preparing reports. This involves extracting data from various sources, cleaning it, and generating insights that drive decision-making. Today’s focus: identifying trends in customer behavior to enhance user experience. 🤝 **Stakeholder Meetings:** Next, I meet with business stakeholders to discuss current projects and gather requirements for new initiatives. Communication is key, ensuring I understand their needs and translate them into technical specifications that our development team can implement. 🛠️ **System Optimization:** Midday is often dedicated to system analysis and optimization. I review our existing systems, identify areas for improvement, and collaborate with IT teams to streamline processes. This could involve troubleshooting issues or implementing new features to enhance system performance. 📊 **Data Modeling & Visualization:** In the afternoon, I dive into data modeling and create visualizations using tools like Tableau or Power BI. These visuals help translate complex data into actionable insights, making it easier for teams to understand trends and make informed decisions. 📈 **Project Management & Documentation:** I wrap up the day by updating project documentation and managing timelines. Ensuring all activities align with business goals and regulatory requirements is crucial, especially in the banking and insurance sectors where precision and compliance are paramount. 💡 **Continuous Learning:** The field is ever-evolving, so I dedicate time to learn new tools, techniques, and industry trends. This helps me stay ahead and bring innovative solutions to the table. Being a Business Systems Analyst/Data Analyst is all about bridging the gap between business and technology, ensuring systems are optimized, and decisions are data-driven. It's challenging but incredibly rewarding! #DataAnalysis #BusinessSystemsAnalyst #DataDriven #Banking #Insurance #SystemsOptimization #DailyRoutine

𝗧𝗵𝗲 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝘂𝗽𝗼𝗻 𝗶𝘁! So, when you work on a business problem as a data analyst, you analyze the data to generate actionable insights, right? So, what do you consider as criteria for successful project delivery? Is it the tools you used, the methods you used, or the solution you created? Actually, it's none of these. The actual criteria for a successful data project: 𝗜𝘁 𝗵𝗮𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝘄𝗲𝗿𝗲 𝘀𝗶𝗺𝗽𝗹𝗲 𝗲𝗻𝗼𝘂𝗴𝗵 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 𝗮𝗻𝗱 𝗻𝗼𝗻-𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗲𝗻𝘁𝗶𝘁𝗶𝗲𝘀 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱. So, focus on developing solutions that are simple and easy for stakeholders and non-technical entities to understand as a data analyst. Enjoy. Follow for more! #DataAnalyst #DataCleaning #DataTransformation #DataAnalytics #BusinessAnalyst #BusinessAnalytics #DataAnalysis #BusinessAnalysis #ProductAnalyst #DataAnalysisTips #AnalyticsCareer #BusinessProblems #CommunicationSkills #EffectiveCommunication #DataScienceCommunity #AnalyticsTips #DataWorkflow #AnalyticsBestPractices #DataProcesses #DataTypes #AnalyticsDocumentation #US #UK #Europe #Singapore #Germany #SQL #Python #PowerBI #Excel #AmazonRedshift #ABTesting #Remotework #GlobalRemoteWork #SalesAnalyst #HealthcareAnalyst #MarketingAnalyst #FinancialAnalyst #SalesAnalysis #FinancialAnalysis #HealthcareAnalysis #MarketingAnalysis #RemoteDataAnalyst #RemoteWork #USRemote #UKRemote #EuropeRemote #SingaporeRemote #HiringRemote

𝗗𝗼𝗻'𝘁 𝗱𝗼 𝘁𝗵𝗶𝘀 𝗷𝘂𝗻𝗶𝗼𝗿 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝗺𝗶𝘀𝘁𝗮𝗸𝗲! Rushing every step of data analysis is not a great thing to do. Data Analysts generally do this during the early stage of careers. What rushing means here is that you are not spending enough time at every step of data analysis. These steps are data cleaning, transformation, validation, EDA, and more. 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗱𝗿𝗮𝘄𝗯𝗮𝗰𝗸 𝗼𝗳 𝗿𝘂𝘀𝗵𝗶𝗻𝗴 𝗶𝘀: You will have to go back to the previous steps multiple times which is not optimal at all. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You went on to perform EDA and realized data quality has some issues and it also needs further data cleaning. Tell me how much time do you guys spend on each step of data analysis? Enjoy. Follow for more! #DataAnalyst #DataAnalytics #BusinessAnalyst #BusinessAnalytics #DataAnalysis #BusinessAnalysis #DataAnalysisTips #AnalyticsCareer #DataScienceCommunity #AnalyticsTips #DataWorkflow #AnalyticsBestPractices #DataProcesses #AnalyticsDocumentation #US #UK #Europe #Singapore #Germany #SQL #Python #PowerBI #Excel #AmazonRedshift #ABTesting #Remotework #GlobalRemoteWork #SalesAnalyst #HealthcareAnalyst #MarketingAnalyst #FinancialAnalyst #SalesAnalysis #FinancialAnalysis #HealthcareAnalysis #MarketingAnalysis #RemoteDataAnalyst #RemoteWork #USRemote #UKRemote #EuropeRemote #SingaporeRemote

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗷𝗼𝗶𝗻 𝘁𝘄𝗼 𝘁𝗮𝗯𝗹𝗲𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗺𝗺𝗼𝗻 𝗰𝗼𝗹𝘂𝗺𝗻 𝗮𝘀 𝗮 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁! A lot of times, as a data analyst, you will have tables that don't have common columns between them due to strict normalization. Also, you will need to perform a join between them as a data analyst. 𝗦𝗼, 𝗹𝗲𝘁 𝗺𝗲 𝘁𝗲𝗹𝗹 𝘆𝗼𝘂 𝗵𝗼𝘄 𝘁𝗼 𝗽𝗲𝗿𝗳𝗼𝗿𝗺 𝗷𝗼𝗶𝗻 𝗶𝗻 𝘀𝘂𝗰𝗵 𝘀𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻: • You may have a branching table that contains the primary key of both these tables as the primary key or foreign key. • Connect both of these tables with the branching table. • This creates an indirect link between those two tables and you can extract relevant data from them. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: 𝗦𝗘𝗟𝗘𝗖𝗧 𝗲.𝗲𝗺𝗽_𝗻𝗼, 𝗲.𝗳𝗶𝗿𝘀𝘁_𝗻𝗮𝗺𝗲, 𝗱.𝗱𝗲𝗽𝘁_𝗻𝗮𝗺𝗲 𝗙𝗥𝗢𝗠 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗔𝗦 𝗲 𝗜𝗡𝗡𝗘𝗥 𝗝𝗢𝗜𝗡 𝗱𝗲𝗽𝘁_𝗲𝗺𝗽 𝗔𝗦 𝗱𝗲 𝗨𝗦𝗜𝗡𝗚 (𝗲𝗺𝗽_𝗻𝗼) 𝗜𝗡𝗡𝗘𝗥 𝗝𝗢𝗜𝗡 𝗱𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁𝘀 𝗔𝗦 𝗱 𝗨𝗦𝗜𝗡𝗚 (𝗱𝗲𝗽𝘁_𝗻𝗼) Here, emp_no is the primary key of employees table dept_no is the primary key of dept_emp and departments table. Enjoy. Like, Comment, and Follow for more! #DataAnalyst #BusinessProblems #DataAnalytics #BusinessAnalyst #BusinessAnalytics #DataAnalysis #BusinessAnalysis #ProductAnalyst #DataAnalysisTips #AnalyticsCareer #BusinessProblems #CommunicationSkills #EffectiveCommunication #DataScienceCommunity #AnalyticsTips #DataWorkflow #AnalyticsBestPractices #DataProcesses #DataTypes #AnalyticsDocumentation #US #UK #Europe #Singapore #Germany #SQL #Python #PowerBI #Excel #AmazonRedshift #ABTesting #Remotework #GlobalRemoteWork #SalesAnalyst #HealthcareAnalyst #MarketingAnalyst #FinancialAnalyst #SalesAnalysis #FinancialAnalysis #HealthcareAnalysis #MarketingAnalysis #RemoteDataAnalyst #RemoteWork #USRemote #UKRemote #EuropeRemote #SingaporeRemote #HiringRemote #UjjwalJha

𝗗𝗼 𝘁𝗵𝗶𝘀 𝗮𝘀 𝗮 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁! I recently created a post where I discussed why you should check and correct data types of variables! I got a few messages saying - "𝗪𝗵𝗮𝘁 𝗶𝗳 𝘄𝗲 𝗱𝗼𝗻'𝘁 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗽𝗲𝗿𝗳𝗼𝗿𝗺 𝗮𝗻𝘆 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝘁𝗵𝗼𝘀𝗲 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀, 𝗰𝗮𝗻 𝘄𝗲 𝘁𝗵𝗲𝗻 𝗸𝗲𝗲𝗽 𝘁𝗵𝗲𝗺 𝗮𝘀 𝗶𝘁 𝗶𝘀". I thought I should create a post to answer that. In my earlier post, I mentioned that if your datatypes are incorrect, you will not be able to perform operations such as arithmetic operations and more. 𝗡𝗼𝘄, 𝘁𝗼 𝗮𝗻𝘀𝘄𝗲𝗿 𝘁𝗵𝗮𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗬𝗲𝘀, 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗰𝗼𝗿𝗿𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 𝘁𝘆𝗽𝗲𝘀 𝗼𝗳 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 𝗲𝘃𝗲𝗻 𝗶𝗳 𝘆𝗼𝘂 𝗱𝗼𝗻'𝘁 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗽𝗲𝗿𝗳𝗼𝗿𝗺 𝗮𝗻𝘆 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝗺. The reason for that is: • Your RDBMS optimizes your variables according to their data types. • So, if you have incorrect data types for certain variables, processing time can increase as RDBMS cannot optimize it efficiently. • This will lead to slow performance of the query. • Also, when you have a large amount of data, this will decrease performance even more. • You might not find the issue as the query execution plan may or may not show this. I hope I was able to clear this up for you guys. Also, tell me, do you spend time correcting data types or not? Enjoy. Follow for more! #DataAnalyst #DataAnalytics #BusinessAnalyst #BusinessAnalytics #DataAnalysis #BusinessAnalysis #DataAnalysisTips #AnalyticsCareer #DataScienceCommunity #AnalyticsTips #DataWorkflow #AnalyticsBestPractices #DataProcesses #DataTypes #AnalyticsDocumentation #US #UK #Europe #Singapore #Germany #SQL #Python #PowerBI #Excel #AmazonRedshift #ABTesting #Remotework #GlobalRemoteWork #SalesAnalyst #HealthcareAnalyst #MarketingAnalyst #FinancialAnalyst #SalesAnalysis #FinancialAnalysis #HealthcareAnalysis #MarketingAnalysis #RemoteDataAnalyst #RemoteWork #USRemote #UKRemote #EuropeRemote #SingaporeRemote

*Senior Data Analyst* *Communication: Must be STRONG* * Experience: 8+ Years* *Positions : 2* *Type: #Remote #Full time #Contractual* *Work Hours :* *1:- IST ( Regular )* *2:- US Time Zone: 5:30 PM to 2:30 AM IST* Contract Duration : 12 months(extendable) *Compensation: INR 75 kPM* *Note: Candidates must have experience in remote working environments. Those engaged in Hybrid-dual employment will not be accepted.* CV Submission Requirements: Please ensure the following information is included when submitting profiles: • Project Name • Designation • Duration: Specify starting and ending year and month. • Project Description • Technologies Used • Roles and Responsibilities • Ensure that the project duration matches the total years of experience. • Incorporate any JD-specific modifications if necessary. Job Description: Key Responsibilities: • Data Analysis: Expertise in Power BI, SQL, and Python to analyze and present data. • Dashboard Insights: Ability to provide valuable insights and share proper interpretations of the data. Apply analytical thinking and logical reasoning to the problem statement. • Stakeholder Interaction: Strong communication skills to explain solutions to stakeholders and ensure they understand the big picture and end-user perspective. • Data Presentation: Interpreting and presenting data effectively, not just preparing dashboards but understanding the resulting actions and who will act on them. • Program Management: Manage stakeholder relationships with good program management skills and drive value-added insights through data analysis. Key Requirements: • Communication Skills: Excellent communication to interact with stakeholders. • Power BI, SQL, Python Expertise: Must be highly skilled in these tools for data analysis and dashboard creation. • Analytical Thinking: Ability to apply logical reasoning to problem-solving and deliver actionable ins