{"id":3717,"date":"2026-10-30T23:41:19","date_gmt":"2026-10-30T20:41:19","guid":{"rendered":"https:\/\/niteducation.com\/resources\/?p=3717"},"modified":"2026-10-30T23:41:22","modified_gmt":"2026-10-30T20:41:22","slug":"what-does-a-data-analyst-do-at-work","status":"publish","type":"post","link":"https:\/\/niteducation.com\/resources\/what-does-a-data-analyst-do-at-work\/","title":{"rendered":"What Does a Data Analyst Do at Work? Real Daily Tasks Explained | NIT"},"content":{"rendered":"\n<!--\nNewton Institute of Technology \u2014 Article 213\nMaster Stylesheet v6.3 Premium production build \u2014 30 October 2026\nWordPress supplies the page's only semantic H1; the article hero uses .nit-hero__title.\nLoad the locked nit-articles-master-v6.3.css once at site level.\nNo article-specific CSS, inline styles, scripts, embedded article images or new component classes are used in this body.\nCanonical production URL: https:\/\/niteducation.com\/resources\/what-does-a-data-analyst-do-at-work\/\nPrimary search intent: show a beginner what Data Analysts actually do during real workplace tasks, including requests, recurring reports, ad-hoc analysis, reconciliation, data-quality problems, communication, documentation and automation.\nCannibalization boundary: Data Analyst Career in Kenya owns the broad occupation, demand, qualifications, skills and progression; Data Analyst Projects for Beginners owns portfolio-project execution; SQL for Data Analysis owns SQL teaching; Data Analyst vs Data Scientist owns role comparison. Article 213 owns real workplace execution and the daily operating workflow of a Data Analyst.\nNo article image is required: v6.3 hero, workflow, cards, tables and callouts communicate the workplace process more clearly than a decorative image.\n-->\n<article aria-labelledby=\"nit-article-title\" class=\"nit-article nit-article--software nit-article--pathway-guide nit-article--c-plus-degree\" itemscope itemtype=\"https:\/\/schema.org\/Article\">\n<meta itemprop=\"datePublished\" content=\"2026-10-30\">\n<meta itemprop=\"dateModified\" content=\"2026-10-30\">\n<meta itemprop=\"inLanguage\" content=\"en-KE\">\n<meta itemprop=\"author\" content=\"Newton Institute of Technology Editorial Team\">\n\n<div aria-labelledby=\"nit-article-title\" class=\"nit-pg-hero\">\n  <div>\n    <p class=\"nit-pg-kicker\">Data Analyst Workplace Guide \u00b7 2026<\/p>\n    <p class=\"nit-hero__title\" id=\"nit-article-title\" itemprop=\"headline\">What Does a Data Analyst Actually Do at Work?<\/p>\n    <p class=\"nit-pg-hero__line\">request <i>\u2192<\/i> clarify <i>\u2192<\/i> analyse <i>\u2192<\/i> verify <i>\u2192<\/i> explain<\/p>\n    <p class=\"nit-pg-hero__summary\" itemprop=\"description\">Go beyond the job description. Follow real workplace situations\u2014from a manager&#8217;s urgent question and a Finance mismatch to a broken weekly report\u2014and see how a Data Analyst turns raw records into an answer people can trust.<\/p>\n    <div class=\"nit-pg-hero__actions\">\n      <a class=\"nit-pg-button nit-pg-button--gold\" href=\"#quick-answer\">See the direct answer<\/a>\n      <a class=\"nit-pg-button nit-pg-button--outline\" href=\"#finance-mismatch\">Follow a real workflow<\/a>\n    <\/div>\n  <\/div>\n  <div aria-label=\"Data Analyst workplace workflow\" class=\"nit-deg-hero__art\">\n    <p>The software changes. The responsibility does not.<\/p>\n    <div aria-hidden=\"true\" class=\"nit-deg-orbit\">\n      <strong>5<\/strong>\n      <span class=\"nit-deg-orbit__chip nit-deg-orbit__chip--tech\">ASK<\/span>\n      <span class=\"nit-deg-orbit__chip nit-deg-orbit__chip--health\">DATA<\/span>\n      <span class=\"nit-deg-orbit__chip nit-deg-orbit__chip--business\">CHECK<\/span>\n      <span class=\"nit-deg-orbit__chip nit-deg-orbit__chip--stem\">SHARE<\/span>\n    <\/div>\n    <div class=\"nit-deg-hero__note\">\n      <span>Core responsibility<\/span>\n      <strong>Turn questions into checked evidence<\/strong>\n      <small>A chart is useful only when the data, definition and conclusion behind it are sound.<\/small>\n    <\/div>\n  <\/div>\n  <div aria-label=\"Article summary\" class=\"nit-pg-hero__meta\">\n    <p><span>Main focus<\/span>Real daily workplace tasks<\/p>\n    <p><span>Examples<\/span>3 practical situations<\/p>\n    <p><span>Best for<\/span>School leavers \u00b7 students \u00b7 beginners<\/p>\n    <p><span>Last reviewed<\/span>30 October 2026<\/p>\n  <\/div>\n<\/div>\n\n<div class=\"nit-article__body\">\n<section aria-labelledby=\"quick-answer-title\" class=\"nit-quick-answer\" id=\"quick-answer\">\n  <div class=\"nit-quick-answer__head\">\n    <span aria-hidden=\"true\" class=\"nit-quick-answer__mark\">A<\/span>\n    <div><p class=\"nit-quick-answer__label\">Quick answer<\/p><p class=\"nit-quick-answer__kicker\" id=\"quick-answer-title\">What does a Data Analyst actually do at work?<\/p><\/div>\n  <\/div>\n  <div class=\"nit-quick-answer__body\">\n    <p class=\"nit-quick-answer__text\"><strong>A Data Analyst turns questions and raw records into checked evidence that people can use.<\/strong> The work can include clarifying a manager&#8217;s request, retrieving data, checking quality, calculating and comparing results, reconciling numbers, updating reports, investigating broken dashboards, documenting definitions and explaining findings. Microsoft\u2019s current Power BI Data Analyst framework likewise covers stakeholder requirements, data preparation, modelling, analysis, visualisation, data quality, refresh and governance\u2014not dashboard design alone. <a href=\"#source-1\" aria-label=\"Reference 1\">[1]<\/a><\/p>\n    <ul aria-label=\"Core Data Analyst workplace activities\" class=\"nit-quick-answer__signals\">\n      <li>Clarify the real question<\/li>\n      <li>Check the right records<\/li>\n      <li>Analyse and reconcile<\/li>\n      <li>Explain what the evidence supports<\/li>\n      <li>Maintain repeatable reporting<\/li>\n    <\/ul>\n  <\/div>\n<\/section>\n\n<nav aria-label=\"Article contents\" class=\"nit-toc\">\n  <h2>Contents<\/h2>\n  <ol>\n    <li><a href=\"#work-arrives\">What actually lands on the analyst&#8217;s desk?<\/a><\/li>\n    <li><a href=\"#before-analysis\">Before analysis: clarify, define and understand<\/a><\/li>\n    <li><a href=\"#finance-mismatch\">Example 1: Finance says your dashboard is wrong<\/a><\/li>\n    <li><a href=\"#urgent-request\">Example 2: urgent management question<\/a><\/li>\n    <li><a href=\"#broken-report\">Example 3: the weekly report breaks<\/a><\/li>\n    <li><a href=\"#workday-types\">Four kinds of Data Analyst workdays<\/a><\/li>\n    <li><a href=\"#tools-and-people\">Tools, SQL, meetings and collaboration<\/a><\/li>\n    <li><a href=\"#deliverables\">What the analyst actually delivers<\/a><\/li>\n    <li><a href=\"#automation-ai\">Automation and AI in 2026<\/a><\/li>\n    <li><a href=\"#junior-work\">What a junior Data Analyst may do<\/a><\/li>\n    <li><a href=\"#workflow\">The complete workplace workflow<\/a><\/li>\n    <li><a href=\"#faq\">Frequently asked questions<\/a><\/li>\n  <\/ol>\n<\/nav>\n\n<section class=\"nit-section-panel\" id=\"work-arrives\">\n  <h2>What actually lands on a Data Analyst&#8217;s desk?<\/h2>\n  <p>Real requests are rarely written like textbook exercises. A manager may ask, <strong>\u201cWhy did sales fall yesterday?\u201d<\/strong> Finance may say, <strong>\u201cYour dashboard does not match our total.\u201d<\/strong> A scheduled report may stop refreshing five minutes before a meeting. The analyst has to determine what kind of problem has arrived before choosing a tool.<\/p>\n  <div class=\"nit-module-grid\">\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">01<\/span><h3>Recurring reporting<\/h3><\/div><p>Daily, weekly or monthly reports that must use consistent definitions and dependable data.<\/p><p class=\"nit-module-card__outcome\">Typical concern: Did the refresh complete, and do the totals still reconcile?<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">02<\/span><h3>Ad-hoc questions<\/h3><\/div><p>A new question that is not already answered by an established report: \u201cWhich branch caused the decline?\u201d<\/p><p class=\"nit-module-card__outcome\">Typical concern: Define the question quickly without jumping to a cause.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">03<\/span><h3>Data-quality problems<\/h3><\/div><p>Duplicates, missing records, changed categories, impossible values or two systems reporting different numbers.<\/p><p class=\"nit-module-card__outcome\">Typical concern: Is the apparent business problem actually a data problem?<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">04<\/span><h3>Report maintenance<\/h3><\/div><p>A file name, column, credential or upstream process changes and a once-reliable report no longer behaves as expected.<\/p><p class=\"nit-module-card__outcome\">Typical concern: Find where the reporting chain failed and retest it.<\/p><\/article>\n  <\/div>\n<\/section>\n\n<section id=\"before-analysis\">\n  <h2>Before analysis: clarify the request, define the number and understand the records<\/h2>\n  <p>Good analysts do not begin with software. They begin with meaning. Microsoft\u2019s current analyst framework explicitly places business requirements and data acquisition before preparation, modelling and analysis. <a href=\"#source-1\" aria-label=\"Reference 1\">[1]<\/a><\/p>\n  <div aria-label=\"Before-analysis sequence\" class=\"nit-pg-progress\">\n    <div><span>01 \u00b7 Clarify<\/span><strong>What decision or question needs an answer?<\/strong><\/div><i aria-hidden=\"true\">\u2192<\/i>\n    <div><span>02 \u00b7 Define<\/span><strong>What exactly does the metric mean?<\/strong><\/div><i aria-hidden=\"true\">\u2192<\/i>\n    <div><span>03 \u00b7 Understand<\/span><strong>What does one row represent?<\/strong><\/div>\n  <\/div>\n\n  <h3>1. Clarify vague requests<\/h3>\n  <p>Suppose someone says, <strong>\u201cCustomers are reducing.\u201d<\/strong> That is not yet a safe analytical question. Which customers? Compared with which period? Is a customer someone who registered, someone who ordered, or someone who completed a purchase?<\/p>\n  <div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Turn the complaint into a measurable question<\/p><p><strong>\u201cDid the number of customers completing at least one purchase decline in October compared with September, and which branches contributed most to the change?\u201d<\/strong> Now the analyst has a measure, a period and a scope.<\/p><\/div>\n\n  <h3>2. Define what the number means<\/h3>\n  <p>Words such as <strong>sales<\/strong>, <strong>active customer<\/strong>, <strong>profit<\/strong>, <strong>completed order<\/strong> and <strong>late delivery<\/strong> can have several valid definitions. Sales may mean gross sales, net sales after refunds, invoiced sales or money actually received. Two departments can therefore calculate different answers from the same organisation without either formula being mathematically wrong.<\/p>\n\n  <h3>3. Know what one row represents<\/h3>\n  <p>This is sometimes called the <strong>grain<\/strong> or level of detail of the data. Consider this fictional extract:<\/p>\n  <div class=\"nit-table-wrap\"><table class=\"nit-table\">\n    <thead><tr><th>Order<\/th><th>Product<\/th><th>Quantity<\/th><\/tr><\/thead>\n    <tbody><tr><td>1001<\/td><td>Cement<\/td><td>4<\/td><\/tr><tr><td>1001<\/td><td>Paint<\/td><td>2<\/td><\/tr><tr><td>1002<\/td><td>Cement<\/td><td>5<\/td><\/tr><\/tbody>\n  <\/table><\/div>\n  <p class=\"nit-table-note\">On a small screen, swipe the table sideways to view every column. There are three rows but only two orders. One row represents an order item. Counting rows as orders would produce the wrong answer even if the formula itself worked.<\/p>\n<\/section>\n\n<section class=\"nit-section-panel\" id=\"finance-mismatch\">\n  <p class=\"nit-pg-eyebrow\">Practical workplace situation 1<\/p>\n  <h2>Finance says your dashboard is wrong. What does the analyst do?<\/h2>\n  <p>Imagine a fictional sales dashboard shows <strong>October revenue of KSh 8,420,000<\/strong>, but Finance reports <strong>KSh 8,010,000<\/strong>. The difference is <strong>KSh 410,000<\/strong>.<\/p>\n  <p>A professional analyst does not defend the dashboard simply because it is automated or attractive. The task is to <strong>reconcile<\/strong> the disagreement.<\/p>\n  <div class=\"nit-module-grid\">\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">01<\/span><h3>Confirm the definition<\/h3><\/div><p>Does each side mean invoiced revenue, paid revenue, gross sales or revenue after refunds and cancellations?<\/p><p class=\"nit-module-card__outcome\">First question: Are we measuring the same thing?<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">02<\/span><h3>Confirm the period<\/h3><\/div><p>Check transaction date, posting date, time boundaries and whether late-posted items fall into different months.<\/p><p class=\"nit-module-card__outcome\">A date rule can explain a difference before any formula changes.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">03<\/span><h3>Inspect filters<\/h3><\/div><p>Look for an omitted branch, excluded category, test transaction, cancelled order or unexpected status value.<\/p><p class=\"nit-module-card__outcome\">One hidden filter can change an otherwise correct report.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">04<\/span><h3>Reconcile record counts<\/h3><\/div><p>Compare source counts with the analytical dataset. If 12,480 transactions become 24,960 rows after a join, investigate the join before trusting any total.<\/p><p class=\"nit-module-card__outcome\">A query can be syntactically correct while multiplying records.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">05<\/span><h3>Break the difference down<\/h3><\/div><p>Compare branch by branch, date by date, category by category or status by status until the disagreement becomes small enough to trace.<\/p><p class=\"nit-module-card__outcome\">Do not stare only at two grand totals.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">06<\/span><h3>Correct and retest<\/h3><\/div><p>Suppose the fictional difference is KSh 410,000 of cancelled transactions left inside the dashboard data. Correct the rule, rerun the checks and document the change.<\/p><p class=\"nit-module-card__outcome\">The job ends with a verified correction, not merely a diagnosis.<\/p><\/article>\n  <\/div>\n  <div class=\"nit-callout nit-callout--success\"><p class=\"nit-callout__title\">A useful final message<\/p><p>\u201cThe dashboard included KSh 410,000 of cancelled October transactions. The reporting rule has been corrected to exclude cancelled transactions, bringing the October total to KSh 8,010,000.\u201d<\/p><\/div>\n<\/section>\n\n<section id=\"reconciliation\">\n  <h2>When numbers disagree, reconcile the whole path<\/h2>\n  <p>A useful checking model is <strong>Source \u2192 Analytical result \u2192 Published result<\/strong>. Agreement between only two stages does not prove correctness.<\/p>\n  <div aria-label=\"Reconciliation path\" class=\"nit-pg-progress\">\n    <div><span>01 \u00b7 Source<\/span><strong>3,125 completed deliveries<\/strong><\/div><i aria-hidden=\"true\">\u2192<\/i>\n    <div><span>02 \u00b7 Analysis<\/span><strong>3,125 rows after the intended filters<\/strong><\/div><i aria-hidden=\"true\">\u2192<\/i>\n    <div><span>03 \u00b7 Report<\/span><strong>3,125 published deliveries<\/strong><\/div>\n  <\/div>\n  <p>If the SQL result and dashboard both show <strong>6,250<\/strong> while the source contains <strong>3,125<\/strong>, the dashboard and query agree with each other but may both be wrong. A duplicated join could have doubled the records before the dashboard ever saw them.<\/p>\n  <div class=\"nit-callout nit-callout--warning\"><p class=\"nit-callout__title\">Analyst habit<\/p><p>Do not ask only, \u201cDo these two numbers match?\u201d Ask, <strong>\u201cWhat should this number match, and why?\u201d<\/strong><\/p><\/div>\n<\/section>\n\n<section class=\"nit-section-panel\" id=\"urgent-request\">\n  <p class=\"nit-pg-eyebrow\">Practical workplace situation 2<\/p>\n  <h2>An urgent management question arrives before a meeting<\/h2>\n  <p>It is 11:15 a.m. A manager has a 2:00 p.m. meeting and asks: <strong>\u201cSales dropped badly yesterday. Can you tell me what happened?\u201d<\/strong> The analyst cannot investigate every possible explanation in a few hours. The skill is to scope the question intelligently.<\/p>\n  <div class=\"nit-pg-reasons\" aria-label=\"Four steps for an urgent analysis\">\n    <div><span>01<\/span><h3>Choose a sensible baseline<\/h3><p>Compare with the same weekday, a recent average or the planned target\u2014not automatically with a day that behaves differently.<\/p><\/div>\n    <div><span>02<\/span><h3>Find where the decline sits<\/h3><p>Break the total down by branch, product, category, channel or time until the movement is concentrated.<\/p><\/div>\n    <div><span>03<\/span><h3>Test obvious explanations<\/h3><p>Check transaction count, average order value, missing records, cancellations and other operational signals available in the data.<\/p><\/div>\n    <div><span>04<\/span><h3>State what is known<\/h3><p>Give management the strongest supported finding and name the next evidence needed instead of inventing a cause.<\/p><\/div>\n  <\/div>\n  <p>Suppose the fictional total decline is <strong>KSh 320,000<\/strong> and Branch C alone accounts for <strong>KSh 270,000<\/strong>. Transaction count is broadly normal, but three normally high-value products recorded almost no sales.<\/p>\n  <div class=\"nit-pg-highlight\"><strong>Useful answer under time pressure<\/strong><p>\u201cYesterday&#8217;s decline was concentrated in Branch C. Transaction volume was broadly normal, but sales of three normally high-value products fell sharply. The sales data identifies where the decline occurred; it does not show whether stock availability caused it. Inventory records should be checked next.\u201d<\/p><\/div>\n<\/section>\n\n<section id=\"right-output\">\n  <h2>Not every question needs a new dashboard<\/h2>\n  <p>A manager may ask, \u201cCan you build a dashboard showing this?\u201d Before building it, the analyst should ask two simpler questions: <strong>What decision will it support?<\/strong> and <strong>How often will this question be asked?<\/strong><\/p>\n  <div class=\"nit-curriculum-grid\">\n    <article class=\"nit-curriculum-card\"><span class=\"nit-curriculum-card__period\">One-off question<\/span><h3>Use the smallest useful output<\/h3><p>A checked table, short explanation or one chart may be better than creating a report that nobody needs again.<\/p><\/article>\n    <article class=\"nit-curriculum-card\"><span class=\"nit-curriculum-card__period\">Repeated question<\/span><h3>Make the answer repeatable<\/h3><p>If the same request arrives every Monday, consider a saved query, reusable report or approved automation with validation checks.<\/p><\/article>\n  <\/div>\n<\/section>\n\n<section class=\"nit-section-panel\" id=\"broken-report\">\n  <p class=\"nit-pg-eyebrow\">Practical workplace situation 3<\/p>\n  <h2>The weekly report suddenly breaks<\/h2>\n  <p>A report has updated every Monday for six months. Today it fails. The Data Analyst investigates the whole reporting chain instead of assuming the dashboard software itself is the problem. Microsoft\u2019s current analyst objectives explicitly include changing data-source settings, resolving import errors, resolving null and data-quality issues, and configuring scheduled refresh. <a href=\"#source-1\" aria-label=\"Reference 1\">[1]<\/a><\/p>\n  <div class=\"nit-module-grid\">\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">01<\/span><h3>Did the source arrive?<\/h3><\/div><p>The expected file or database load may simply be missing.<\/p><p class=\"nit-module-card__outcome\">Start upstream before rebuilding downstream.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">02<\/span><h3>Did the name or path change?<\/h3><\/div><p>An automated process may expect one exact filename, folder or connection.<\/p><p class=\"nit-module-card__outcome\">A small naming change can break a reliable routine.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">03<\/span><h3>Did the columns change?<\/h3><\/div><p><code>Branch<\/code> becoming <code>Branch_Name<\/code> can break a transformation that still expects the old field.<\/p><p class=\"nit-module-card__outcome\">Schema changes are common causes of broken refreshes.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">04<\/span><h3>Did the data type change?<\/h3><\/div><p>A date that arrives as text may fail where a transformation expects a true date value.<\/p><p class=\"nit-module-card__outcome\">Inspect types, not only visible values.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">05<\/span><h3>Did access change?<\/h3><\/div><p>Credentials, permissions or a gateway connection can fail even when the report itself has not changed.<\/p><p class=\"nit-module-card__outcome\">A valid report still needs valid access to its source.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">06<\/span><h3>Did it refresh but become wrong?<\/h3><\/div><p>This is the more dangerous case. A report can refresh successfully while an upstream source silently omits a branch or changes a category.<\/p><p class=\"nit-module-card__outcome\">Successful automation is not proof of correct data.<\/p><\/article>\n  <\/div>\n  <div class=\"nit-callout nit-callout--warning\"><p class=\"nit-callout__title\">The dangerous report<\/p><p>A report that crashes announces its failure. A report that refreshes successfully and quietly publishes the wrong number can be more dangerous. Validation still matters after automation.<\/p><\/div>\n<\/section>\n\n<section id=\"workday-types\">\n  <h2>A Data Analyst&#8217;s day is not one fixed timetable<\/h2>\n  <p>There is no universal \u201c9:00 SQL, 10:00 dashboard, 11:00 meeting\u201d schedule. A better way to understand the job is to recognise the kinds of days that analytical work creates.<\/p>\n  <div class=\"nit-pg-reasons\" aria-label=\"Four kinds of Data Analyst workdays\">\n    <div><span>01<\/span><h3>Reporting day<\/h3><p>Refresh, verify, update and publish recurring figures. The key skill is consistency.<\/p><\/div>\n    <div><span>02<\/span><h3>Investigation day<\/h3><p>A number moved and nobody knows why. The key skill is analytical reasoning.<\/p><\/div>\n    <div><span>03<\/span><h3>Data-quality day<\/h3><p>Duplicates, missing records or broken joins make the numbers unreliable. The key skill is verification.<\/p><\/div>\n    <div><span>04<\/span><h3>Communication day<\/h3><p>Explain findings, agree definitions and answer questions. The key skill is clarity.<\/p><\/div>\n  <\/div>\n\n  <h3>Does a Data Analyst spend most of the day cleaning data?<\/h3>\n  <p>Sometimes data preparation takes a large share of the work; sometimes it does not. Avoid universal claims such as \u201cevery analyst spends 80% of the day cleaning data.\u201d A mature organisation with governed databases can behave very differently from one where several branches exchange manually edited spreadsheets. The reliable lesson is that <strong>data quality is a real part of the job, but its share varies by organisation, system and role.<\/strong><\/p>\n\n  <h3>What if a value looks suspicious?<\/h3>\n  <p>Suppose normal daily sales are between KSh 400,000 and KSh 600,000, then one day shows KSh 5,800,000. Do not delete it merely because it looks unusual. It could be a duplicate, a posting problem, several days loaded together\u2014or a genuine large transaction. Investigate the records that created the spike before deciding what to do.<\/p>\n  <div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Missing does not mean zero<\/p><p>A blank score, payment date or delivery time can mean absent, not entered, unknown, not applicable or import failure. The analyst must understand the business meaning before replacing a missing value. Microsoft\u2019s analyst objectives explicitly include resolving unexpected and null values as data-quality work. <a href=\"#source-1\" aria-label=\"Reference 1\">[1]<\/a><\/p><\/div>\n<\/section>\n\n<section class=\"nit-section-panel\" id=\"tools-and-people\">\n  <h2>What tools does a Data Analyst use\u2014and do they spend all day coding?<\/h2>\n  <p>No single tool defines the job. The best tool depends on where the records live, how often the task repeats and what the audience needs.<\/p>\n  <div class=\"nit-table-wrap\"><table class=\"nit-table\">\n    <thead><tr><th>Workplace need<\/th><th>Common tool<\/th><th>What the analyst is trying to do<\/th><\/tr><\/thead>\n    <tbody>\n      <tr><td>Quickly inspect a manageable file<\/td><td>Excel or another spreadsheet<\/td><td>Filter, calculate, pivot, spot-check and compare.<\/td><\/tr>\n      <tr><td>Retrieve relational records<\/td><td>SQL<\/td><td>Select, filter, join, group and aggregate structured data.<\/td><\/tr>\n      <tr><td>Repeatable cleaning or automation<\/td><td>Python or another scripting tool<\/td><td>Make transformations repeatable and reduce manual work.<\/td><\/tr>\n      <tr><td>Recurring interactive reporting<\/td><td>Power BI or another BI platform<\/td><td>Publish measures, trends and monitored views for authorised users.<\/td><\/tr>\n      <tr><td>Explain a finding<\/td><td>Report, presentation, email or meeting<\/td><td>Turn the result into language another person can act on.<\/td><\/tr>\n    <\/tbody>\n  <\/table><\/div>\n  <p class=\"nit-table-note\">On a small screen, swipe the table sideways to view every column. A real workflow may use one tool or several. The objective is a reliable answer, not maximum software use.<\/p>\n\n  <h3>What does SQL actually do during the day?<\/h3>\n  <p>When important records are stored in a relational database, SQL can retrieve selected rows, join related tables and calculate grouped summaries. PostgreSQL&#8217;s official tutorial covers querying tables, joins and aggregate functions as core SQL operations. <a href=\"#source-2\" aria-label=\"Reference 2\">[2]<\/a><\/p>\n  <p><strong>But SQL cannot decide what \u201ccompleted sale\u201d means for your organisation.<\/strong> A query can run successfully while using the wrong date, wrong status, wrong join or wrong business definition.<\/p>\n  <div class=\"nit-action-strip\"><p>Want the SQL part step by step?<\/p><div class=\"nit-action-strip__buttons\"><a class=\"nit-button nit-button--secondary\" href=\"https:\/\/niteducation.com\/resources\/what-is-sql-for-data-analysis\/\">Read NIT&#8217;s SQL for Data Analysis guide<\/a><\/div><\/div>\n\n  <h3>Do Data Analysts attend meetings?<\/h3>\n  <p>Often, yes. Meetings can prevent analytical errors: clarifying the request, agreeing a metric definition, understanding a surprising result, presenting findings or deciding what evidence to collect next. A ten-minute discussion about what \u201cactive customer\u201d means can save hours of analysing the wrong definition.<\/p>\n<\/section>\n\n<section id=\"deliverables\">\n  <h2>What does the Data Analyst actually give the manager?<\/h2>\n  <p>The final product is not always a dashboard. Sometimes the most useful output is three verified numbers and a clear explanation. A strong handoff usually contains five pieces.<\/p>\n  <div class=\"nit-pg-reasons\" aria-label=\"Five parts of an analytical handoff\">\n    <div><span>01<\/span><h3>The answer<\/h3><p>What happened?<\/p><\/div>\n    <div><span>02<\/span><h3>The evidence<\/h3><p>Which calculation, comparison, chart or records support it?<\/p><\/div>\n    <div><span>03<\/span><h3>The definition<\/h3><p>What exactly was measured?<\/p><\/div>\n    <div><span>04<\/span><h3>The limitation<\/h3><p>What does the available evidence not establish?<\/p><\/div>\n    <div><span>05<\/span><h3>The next action<\/h3><p>What should the reader do or investigate next?<\/p><\/div>\n  <\/div>\n\n  <h3>Documentation is part of the work<\/h3>\n  <p>If a dashboard says <strong>Active customers = 12,410<\/strong>, another analyst should not have to guess what \u201cactive\u201d means six months later.<\/p>\n  <div class=\"nit-table-wrap\"><table class=\"nit-table\">\n    <thead><tr><th>Documentation item<\/th><th>Example<\/th><\/tr><\/thead>\n    <tbody>\n      <tr><td>Metric<\/td><td>Active customer<\/td><\/tr>\n      <tr><td>Definition<\/td><td>Customer with at least one completed purchase in the previous 90 days<\/td><\/tr>\n      <tr><td>Source<\/td><td>Orders database<\/td><\/tr>\n      <tr><td>Exclusions<\/td><td>Cancelled and test orders<\/td><\/tr>\n      <tr><td>Refresh<\/td><td>Daily<\/td><\/tr>\n      <tr><td>Last definition change<\/td><td>12 October 2026<\/td><\/tr>\n    <\/tbody>\n  <\/table><\/div>\n  <p class=\"nit-table-note\">On a small screen, swipe the table sideways to view every column. Documentation makes a recurring number easier to check, explain and maintain.<\/p>\n<\/section>\n\n<section class=\"nit-section-panel\" id=\"automation-ai\">\n  <h2>When should a Data Analyst automate a task?<\/h2>\n  <p>If somebody asks every Monday, <strong>\u201cPlease send last week&#8217;s sales by branch,\u201d<\/strong> repeating the same manual steps forever may not be sensible. The analyst might save and parameterise a query, create a reusable report or automate an approved refresh.<\/p>\n  <div class=\"nit-pg-check\">\n    <div class=\"nit-pg-check__intro\"><p class=\"nit-pg-eyebrow\">Before automating<\/p><h3>Make the logic stable first<\/h3><p>An automated wrong definition simply produces the wrong answer faster.<\/p><\/div>\n    <ul>\n      <li>Confirm the metric definition and source.<\/li>\n      <li>Make the transformation repeatable.<\/li>\n      <li>Add checks for expected dates, row counts or duplicates.<\/li>\n      <li>Monitor refreshes and source changes.<\/li>\n      <li>Keep a way to investigate unexpected totals.<\/li>\n    <\/ul>\n  <\/div>\n\n  <h2>Where does AI fit into Data Analyst work in 2026?<\/h2>\n  <p>AI can help with parts of the workflow. Microsoft Power BI Copilot, for example, can generate summaries and answer questions about report content, while Microsoft\u2019s current analyst objectives also include Copilot-assisted report creation and summarisation. <a href=\"#source-1\" aria-label=\"Reference 1\">[1]<\/a> <a href=\"#source-4\" aria-label=\"Reference 4\">[4]<\/a><\/p>\n  <p>That does not transfer responsibility for the final number to the tool. A generated query, summary or visual still needs the same questions:<\/p>\n  <div class=\"nit-curriculum-grid\">\n    <article class=\"nit-curriculum-card\"><span class=\"nit-curriculum-card__period\">AI-generated query<\/span><h3>Check the logic<\/h3><p>Correct tables? Correct join? Correct filters? Correct date? Correct treatment of missing values?<\/p><\/article>\n    <article class=\"nit-curriculum-card\"><span class=\"nit-curriculum-card__period\">AI-generated summary<\/span><h3>Check the evidence<\/h3><p>Does the wording match the report, its filters and the actual figures? Microsoft documents limitations and report-design considerations for Copilot summaries. <a href=\"#source-4\" aria-label=\"Reference 4\">[4]<\/a><\/p><\/article>\n  <\/div>\n  <div class=\"nit-callout nit-callout--success\"><p class=\"nit-callout__title\">Simple 2026 rule<\/p><p><strong>Use AI to accelerate approved analytical work\u2014not to outsource responsibility for whether the answer is correct.<\/strong><\/p><\/div>\n<\/section>\n\n<section id=\"privacy\">\n  <h2>Data Analysts also have responsibilities around personal information<\/h2>\n  <p>Customer, student, employee, patient and financial datasets can contain personal information. Kenya&#8217;s Data Protection Act requires principles including lawful, fair and transparent processing; explicit and legitimate purpose; data minimisation; accuracy; and appropriate limits on retention and transfer. <a href=\"#source-3\" aria-label=\"Reference 3\">[3]<\/a><\/p>\n  <div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Practical questions before sharing or exporting data<\/p><p>Do you actually need names for this summary? Should phone numbers appear in the analytical file? Who is authorised to see the raw records? Can the report use aggregated information instead? Follow the organisation&#8217;s approved privacy and access procedures.<\/p><\/div>\n<\/section>\n\n<section class=\"nit-section-panel\" id=\"junior-work\">\n  <h2>What does a junior Data Analyst actually do?<\/h2>\n  <p>A junior analyst is often given more clearly defined pieces of the wider workflow. That can include checking a refresh, cleaning an export, running or modifying an established query, reconciling two totals, updating a recurring report, investigating a small discrepancy, preparing a chart or documenting a metric.<\/p>\n  <div class=\"nit-module-grid\">\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">01<\/span><h3>Check<\/h3><\/div><p>Confirm dates, row counts, missing fields and obvious inconsistencies.<\/p><p class=\"nit-module-card__outcome\">Learn what reliable organisational data looks like.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">02<\/span><h3>Update<\/h3><\/div><p>Refresh or prepare established reports using agreed definitions.<\/p><p class=\"nit-module-card__outcome\">Build consistency and repeatability.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">03<\/span><h3>Investigate<\/h3><\/div><p>Trace a mismatch or unusual value without jumping to a conclusion.<\/p><p class=\"nit-module-card__outcome\">Build analytical judgement.<\/p><\/article>\n    <article class=\"nit-module-card\"><div class=\"nit-module-card__head\"><span class=\"nit-module-card__number\">04<\/span><h3>Explain<\/h3><\/div><p>Turn the result into a clear note, table, visual or short presentation.<\/p><p class=\"nit-module-card__outcome\">Build stakeholder communication.<\/p><\/article>\n  <\/div>\n  <p>As experience grows, the questions usually become less defined and the analyst takes more ownership of deciding what should be measured, how it should be checked and how the result should be used.<\/p>\n  <div class=\"nit-action-strip\"><p>Looking for qualifications, career progression and Kenya job context?<\/p><div class=\"nit-action-strip__buttons\"><a class=\"nit-button nit-button--secondary\" href=\"https:\/\/niteducation.com\/resources\/data-analyst-career-in-kenya\/\">Read NIT&#8217;s Data Analyst Career in Kenya guide<\/a><\/div><\/div>\n<\/section>\n\n<section id=\"workflow\">\n  <h2>The complete Data Analyst workplace workflow<\/h2>\n  <p>The exact order can loop\u2014an unexpected result may send you back to the source or definition\u2014but this is a useful mental model for real work.<\/p>\n  <div class=\"nit-pg-reasons\" aria-label=\"Twelve-stage Data Analyst workflow\">\n    <div><span>01<\/span><h3>Request<\/h3><p>Someone needs an answer.<\/p><\/div>\n    <div><span>02<\/span><h3>Clarify<\/h3><p>Turn vague wording into a precise question.<\/p><\/div>\n    <div><span>03<\/span><h3>Define<\/h3><p>Agree what the metric and terms mean.<\/p><\/div>\n    <div><span>04<\/span><h3>Scope<\/h3><p>Choose the period, records and groups that belong.<\/p><\/div>\n    <div><span>05<\/span><h3>Retrieve<\/h3><p>Get the required data from approved sources.<\/p><\/div>\n    <div><span>06<\/span><h3>Inspect<\/h3><p>Look for missing, duplicated or suspicious records.<\/p><\/div>\n    <div><span>07<\/span><h3>Analyse<\/h3><p>Calculate, compare and investigate.<\/p><\/div>\n    <div><span>08<\/span><h3>Reconcile<\/h3><p>Check important totals against trusted sources.<\/p><\/div>\n    <div><span>09<\/span><h3>Interpret<\/h3><p>State what the evidence supports\u2014and what it does not.<\/p><\/div>\n    <div><span>10<\/span><h3>Communicate<\/h3><p>Explain the finding in language the audience understands.<\/p><\/div>\n    <div><span>11<\/span><h3>Deliver<\/h3><p>Use the right output: report, dashboard, workbook, query, email or presentation.<\/p><\/div>\n    <div><span>12<\/span><h3>Follow up<\/h3><p>Answer questions, document changes or automate recurring work.<\/p><\/div>\n  <\/div>\n\n  <h3>What beginners often imagine versus what happens at work<\/h3>\n  <div class=\"nit-table-wrap\"><table class=\"nit-table\">\n    <thead><tr><th>Beginner expectation<\/th><th>Workplace reality<\/th><\/tr><\/thead>\n    <tbody>\n      <tr><td>\u201cSomeone will give me a perfect dataset.\u201d<\/td><td>Records may be incomplete, inconsistent or poorly documented.<\/td><\/tr>\n      <tr><td>\u201cSomeone will give me a perfect question.\u201d<\/td><td>Requests are often vague and need clarification.<\/td><\/tr>\n      <tr><td>\u201cSQL returned results, so the answer is correct.\u201d<\/td><td>A valid query can still use the wrong records, join or definition.<\/td><\/tr>\n      <tr><td>\u201cA dashboard is the analysis.\u201d<\/td><td>The dashboard is usually one output from a larger checking and reasoning process.<\/td><\/tr>\n      <tr><td>\u201cMissing means zero.\u201d<\/td><td>Missing information can have several meanings.<\/td><\/tr>\n      <tr><td>\u201cAn unusual value is an error.\u201d<\/td><td>It might be an error\u2014or the most important finding.<\/td><\/tr>\n      <tr><td>\u201cAutomation means the report is safe.\u201d<\/td><td>Automated reports still need monitoring and validation.<\/td><\/tr>\n      <tr><td>\u201cAI can verify the answer for me.\u201d<\/td><td>The analyst remains responsible for the definition, data, logic and conclusion.<\/td><\/tr>\n    <\/tbody>\n  <\/table><\/div>\n  <p class=\"nit-table-note\">On a small screen, swipe the table sideways to view every column.<\/p>\n<\/section>\n\n<section id=\"before-send\">\n  <p class=\"nit-pg-eyebrow\">Before you press Send<\/p>\n  <h2>Five final checks before sharing an analysis<\/h2>\n  <div class=\"nit-pg-check\">\n    <div class=\"nit-pg-check__intro\"><h3>A technically impressive result can still fail<\/h3><p>Use this final check to catch a wrong question, wrong record set, weak verification or an overconfident conclusion before another person acts on it.<\/p><\/div>\n    <ul>\n      <li>Am I answering the question that was actually asked?<\/li>\n      <li>Do I understand what the records represent and which records belong?<\/li>\n      <li>Did I verify the important number against an appropriate source or independent check?<\/li>\n      <li>Does my conclusion go further than the evidence supports?<\/li>\n      <li>Can the reader understand what happened and what should happen next?<\/li>\n    <\/ul>\n  <\/div>\n<\/section>\n\n<section class=\"nit-section-panel\" id=\"faq\">\n  <h2>Frequently asked questions<\/h2>\n  <div class=\"nit-faq\">\n    <details class=\"nit-faq__item\"><summary>What does a Data Analyst do every day?<\/summary><div class=\"nit-faq__answer\"><p>The work can combine recurring reports, ad-hoc questions, SQL or spreadsheet analysis, data-quality checks, dashboard maintenance, reconciliation, meetings, documentation and communicating findings. The exact mixture varies by organisation and by day.<\/p><\/div><\/details>\n    <details class=\"nit-faq__item\"><summary>Do Data Analysts code every day?<\/summary><div class=\"nit-faq__answer\"><p>Not always. Some roles use SQL or Python heavily; others rely more on spreadsheets, BI tools, established reports and meetings. Coding is a tool for answering the analytical question, not the purpose of the job.<\/p><\/div><\/details>\n    <details class=\"nit-faq__item\"><summary>Do Data Analysts spend all day making dashboards?<\/summary><div class=\"nit-faq__answer\"><p>No. A dashboard is one possible output. Requirements, metric definitions, data retrieval, quality checks, analysis, reconciliation and communication can all happen before a dashboard is ready to trust.<\/p><\/div><\/details>\n    <details class=\"nit-faq__item\"><summary>Do Data Analysts have meetings?<\/summary><div class=\"nit-faq__answer\"><p>Often, yes. Meetings can clarify a request, agree definitions, explain a surprising result, present findings or decide which evidence should be collected next.<\/p><\/div><\/details>\n    <details class=\"nit-faq__item\"><summary>What is an ad-hoc analysis request?<\/summary><div class=\"nit-faq__answer\"><p>It is a question requested for a particular immediate need rather than an established scheduled report\u2014for example, \u201cWhy did refunds increase during the last three days?\u201d<\/p><\/div><\/details>\n    <details class=\"nit-faq__item\"><summary>What happens when a Data Analyst finds incorrect data?<\/summary><div class=\"nit-faq__answer\"><p>The analyst investigates the source of the problem, identifies which results were affected, corrects or escalates the issue appropriately, reruns the affected calculations and communicates any necessary correction.<\/p><\/div><\/details>\n    <details class=\"nit-faq__item\"><summary>Is Data Analysis mostly mathematics?<\/summary><div class=\"nit-faq__answer\"><p>Mathematics and statistics matter, but everyday work also requires understanding business questions, retrieving data, checking quality, defining measures, using appropriate software and communicating findings.<\/p><\/div><\/details>\n    <details class=\"nit-faq__item\"><summary>Does AI replace Data Analysts?<\/summary><div class=\"nit-faq__answer\"><p>AI can assist with queries, report creation, summaries and exploration, but the analyst still has to verify definitions, source data, filters, calculations, privacy, context and conclusions. <a href=\"#source-4\" aria-label=\"Reference 4\">[4]<\/a><\/p><\/div><\/details>\n  <\/div>\n<\/section>\n\n<section id=\"main-lesson\">\n  <h2>So, what does a Data Analyst actually do at work?<\/h2>\n  <p>The shortest accurate answer is:<\/p>\n  <div class=\"nit-pg-highlight\"><strong>A Data Analyst turns questions and raw records into checked evidence that people can use.<\/strong><p>Sometimes that means SQL. Sometimes Excel. Sometimes Power BI or Python. Sometimes it means a meeting. Sometimes it means spending two hours finding why two totals disagree. The tools change; the responsibility to understand, check, analyse, verify and explain does not.<\/p><\/div>\n  <p>And sometimes the most professional conclusion is: <strong>\u201cThe data shows where the problem is, but it does not yet prove why.\u201d<\/strong><\/p>\n<\/section>\n\n<section aria-labelledby=\"register-title\" class=\"nit-pg-enrol\" id=\"register\">\n  <div class=\"nit-pg-enrol__copy\">\n    <p class=\"nit-pg-eyebrow\">Build practical analytical skills<\/p>\n    <h2 id=\"register-title\">Learn Data Science &amp; Analytics at Newton Institute of Technology<\/h2>\n    <p>NIT&#8217;s Data Science &amp; Analytics programme develops practical skills in Excel, statistics, Python, SQL, data cleaning, visualisation and introductory machine learning. The current programme has six months of institutional training followed by a compulsory three-month industrial attachment, with physical and online study options. <a href=\"#source-5\" aria-label=\"Reference 5\">[5]<\/a><\/p>\n  <\/div>\n  <div class=\"nit-pg-enrol__details\">\n    <p><span>Institutional training<\/span><strong>6 months<\/strong><\/p>\n    <p><span>Industrial attachment<\/span><strong>Compulsory 3 months<\/strong><\/p>\n    <p><span>School fee<\/span><strong>KSh 60,000 <a href=\"#source-5\" aria-label=\"Reference 5\">[5]<\/a><\/strong><\/p>\n  <\/div>\n  <div class=\"nit-pg-enrol__details\">\n    <p><span>Study options<\/span><strong>Physical \u00b7 evening \u00b7 online<\/strong><\/p>\n    <p><span>Location<\/span><strong>Migori Town, Kenya<\/strong><\/p>\n    <p><span>Call \/ WhatsApp<\/span><strong>+254 713 584 858<\/strong><\/p>\n  <\/div>\n  <div class=\"nit-pg-enrol__actions\">\n    <a class=\"nit-pg-button nit-pg-button--gold\" href=\"https:\/\/niteducation.com\/admissions\/\">Register at NIT<\/a>\n    <a class=\"nit-pg-button nit-pg-button--outline\" href=\"https:\/\/wa.me\/254713584858?text=Hello%20Newton%20Institute%20of%20Technology.%20I%20would%20like%20to%20register%20for%20the%20Data%20Science%20and%20Analytics%20Course.\" target=\"_blank\" rel=\"noopener\">Ask on WhatsApp<\/a>\n  <\/div>\n<\/section>\n\n<section class=\"nit-sources\" id=\"references\">\n  <h2>Sources and references<\/h2>\n  <ol>\n    <li id=\"source-1\"><strong>Microsoft Learn \u2014 Study guide for Exam PL-300: Microsoft Power BI Data Analyst.<\/strong> Current role framework and skills measured as of 20 April 2026, including stakeholder requirements, data preparation, modelling, analysis, visualisation, data quality, refresh and governance. <a href=\"https:\/\/learn.microsoft.com\/en-us\/credentials\/certifications\/resources\/study-guides\/pl-300\" target=\"_blank\" rel=\"noopener\">Microsoft Learn<\/a>.<\/li>\n    <li id=\"source-2\"><strong>PostgreSQL Global Development Group \u2014 PostgreSQL 18 Tutorial.<\/strong> Primary technical documentation for relational database concepts, SQL queries, joins and aggregate functions. <a href=\"https:\/\/www.postgresql.org\/docs\/current\/tutorial.html\" target=\"_blank\" rel=\"noopener\">PostgreSQL documentation<\/a>.<\/li>\n    <li id=\"source-3\"><strong>Kenya Law \u2014 Data Protection Act, 2019, section 25.<\/strong> Primary statutory source for principles including lawful and transparent processing, purpose limitation, data minimisation, accuracy, retention and safeguards for transfers. <a href=\"https:\/\/new.kenyalaw.org\/akn\/ke\/act\/2019\/24\/eng%402019-11-15\/source\" target=\"_blank\" rel=\"noopener\">Kenya Law<\/a>.<\/li>\n    <li id=\"source-4\"><strong>Microsoft Learn \u2014 Summarize a report with Copilot in Power BI.<\/strong> Current documentation for report-grounded Copilot summaries, questions, best practices, limitations and report-design considerations. <a href=\"https:\/\/learn.microsoft.com\/en-us\/power-bi\/create-reports\/copilot-pane-summarize-content\" target=\"_blank\" rel=\"noopener\">Microsoft Learn<\/a>.<\/li>\n    <li id=\"source-5\"><strong>Newton Institute of Technology \u2014 Data Science and Analytics Course in Kenya.<\/strong> Current institutional source for programme content, six-month institutional training, compulsory three-month attachment, study options and KSh 60,000 school fee. <a href=\"https:\/\/niteducation.com\/resources\/data-science-course-in-kenya\/\">View the programme<\/a>.<\/li>\n  <\/ol>\n<\/section>\n\n<div class=\"nit-article__footer\"><p><strong>Prepared by:<\/strong> Newton Institute of Technology Editorial Team \u00b7 <strong>Last reviewed:<\/strong> 30 October 2026 \u00b7 <strong>Contact:<\/strong> <a href=\"mailto:info@niteducation.com\">info@niteducation.com<\/a> \u00b7 +254 713 584 858<\/p><\/div>\n<\/div>\n<\/article>\n","protected":false},"excerpt":{"rendered":"<p>Data Analyst Workplace Guide \u00b7 2026 What Does a Data Analyst Actually Do at Work? request \u2192 clarify \u2192 analyse \u2192 verify \u2192 explain Go beyond the job description. Follow real workplace situations\u2014from a manager&#8217;s urgent question and a Finance mismatch to a broken weekly report\u2014and see how a Data Analyst turns raw records into [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[30],"tags":[767,768,769,766],"class_list":["post-3717","post","type-post","status-publish","format-standard","hentry","category-ict","tag-data-analyst-daily-tasks","tag-data-analyst-day-in-the-life","tag-junior-data-analyst-daily-tasks","tag-what-does-a-data-analyst-do-at-work"],"_links":{"self":[{"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/posts\/3717","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/comments?post=3717"}],"version-history":[{"count":1,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/posts\/3717\/revisions"}],"predecessor-version":[{"id":3718,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/posts\/3717\/revisions\/3718"}],"wp:attachment":[{"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/media?parent=3717"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/categories?post=3717"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/tags?post=3717"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}