{"id":4925,"date":"2026-01-30T14:57:24","date_gmt":"2026-01-30T14:57:24","guid":{"rendered":"https:\/\/dev.iwis.io\/?p=4925"},"modified":"2026-01-31T19:51:09","modified_gmt":"2026-01-31T19:51:09","slug":"proiekty-biznes-analityky","status":"publish","type":"post","link":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/","title":{"rendered":"Why 90% of business analytics projects fail"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-4925","post","type-post","status-publish","format-standard","hentry"],"acf":{"blog_custom_title":"Why 90% of business analytics projects fail","blog_featured_image":4935,"blog_custom_excerpt":"","blog_external_url":"","blog_categories":false,"blog_tags":false,"blog_featured_post":false,"blog_content_blocks":[{"acf_fc_layout":"text_block","text_content":"

Shocking Statistics: Most BI Projects Never Make It to Real Impact<\/strong><\/h2>\r\nEvery year, companies invest billions of dollars in business intelligence. Yet in practice, BI rarely becomes a true driver of change. Reports quickly become outdated, dashboards are opened a few times a year at best, and key decisions are still made intuitively and by inertia.\r\n\r\nAccording to Gartner, by 2027 eight out of ten analytics projects will fail. In most cases, the root cause is a poorly defined business objective and a lack of clarity around which decisions analytics is supposed to influence.\r\n\r\nBI affects the very logic of management: how a company works with information, how quickly it reacts to deviations, and what it bases its actions on. This systemic approach is exactly what many organizations lack. Instead, they treat analytics as a standalone technology initiative rather than an integral part of the business model.\r\n\r\nIn this article, we\u2019ll explore why business intelligence initiatives fail, the early warning signs that appear at the start, how successful companies approach BI, and how to build a BI solution that actually works.\r\n

7 Key Reasons Why BI Projects Fail<\/h2>\r\nLet\u2019s look at the most common challenges BI initiatives face \u2014 even within experienced teams.\r\n

1. Lack of Clear Vision and Strategy<\/h3>\r\nThe most common reason BI projects fail is launching without a clear management question. The decision to implement analytics is often driven by a desire to \u201csee the numbers,\u201d without a real understanding of what should change once those numbers are available.\r\n\r\nWhen it\u2019s unclear from the start what should improve and who is responsible for using analytics, BI turns into a technical project with no business impact. As a result, the analytics system exists in isolation: teams collect data, build reports, visualize metrics \u2014 but none of it is embedded into decision-making processes.\r\n

2. Low Stakeholder Engagement<\/h3>\r\nBI development often becomes overly technical: analysts integrate data sources and build dashboards while the business continues to operate as usual, barely involved in the process.\r\n\r\nThe issue is that effective analytics starts with the right questions. When stakeholders join too late \u2014 or not at all \u2014 BI ends up answering abstract or secondary questions. The data may be correct and the reports well structured, but using them for real management decisions becomes difficult.\r\n

3. Ignoring Change Management<\/h3>\r\nAny new approach inevitably meets resistance or confusion. Some employees stick to familiar workflows, while others don\u2019t fully understand how the new system affects their daily work. Without a prepared environment, BI easily becomes a formal add-on to old processes.\r\n\r\nChange doesn\u2019t stick automatically. Without clear communication, training, and updated rules for working with data, analytics never becomes part of everyday actions. In this scenario, BI exists, reports are available \u2014 but nothing actually changes.\r\n

4. Focusing on Technology Instead of People<\/h3>\r\nWhen BI projects focus solely on tools, a gap quickly forms between the system and its users. Data becomes overloaded, interfaces feel complex, and metric logic is understandable only to a small group of specialists. Analytics turns into a closed expert domain.\r\n\r\nThe value of BI lies in supporting everyday management decisions. If users don\u2019t understand what the numbers mean or how to act on them, the system won\u2019t integrate into workflows. BI tools must be designed around real user needs.\r\n

5. Poor Communication and Lack of Collaboration<\/h3>\r\nEffective BI implementation requires alignment across teams. When each department operates independently with its own reports and definitions, the analytical picture falls apart.\r\n\r\nThe situation worsens when there is no shared understanding of key metrics. The same indicator may be interpreted differently by different teams, undermining trust in data and rendering analytics useless. Without a single version of the truth, BI may look convincing but actually amplifies confusion.\r\n

6. Insufficient Budget and Resources<\/h3>\r\nBI is often underestimated, despite being deep, ongoing work with data at every level. When companies expect to deliver BI in a few weeks with one person, the outcome is predictably poor.\r\n\r\nSuch projects may appear functional at first: data loads, dashboards open, something is used. But as soon as complexity grows or load increases, the system starts to break.\r\n\r\nUnderfunded BI projects are like houses built from cheap materials: they look fine until the first serious storm.\r\n

7. Rushed Launch Without Proper Planning<\/h3>\r\nThere\u2019s always a temptation to \u201cstart with something\u201d and figure things out later. In BI, this approach rarely works.\r\n\r\nWithout a solid data model, a clear understanding of future users, and defined dependencies between metrics, the system cannot scale. Small changes break report logic, and any complexity requires manual fixes. Over time, BI loses value instead of gaining it.\r\n

Warning Signs: How to Know Your BI Project Is at Risk<\/h2>\r\nMost BI projects lose value gradually, but early signals often appear long before the project is officially considered a failure.\r\n\r\nFor example, new reports are regularly created but rarely used in real scenarios. Analytics exists, but it doesn\u2019t influence actions. This usually means BI has detached from the business context and started living its own life.\r\n\r\nAnother red flag is constant disputes over numbers. If different teams see different values for the same metrics, trust in the system disappears. BI stops being a reliable source of truth and becomes a topic of debate.\r\n\r\nYou should also be concerned if every new request requires manual workarounds or temporary fixes. This indicates that the underlying data model doesn\u2019t support growth and that migration merely moved old problems to a new platform.\r\n\r\nFinally, a serious indicator is declining executive interest. When BI is no longer the foundation for strategic planning and is used only operationally, its influence is fading.\r\n\r\nBI that changes nothing is like a painting in an office that has blended into the background. It exists, it was once carefully chosen, paid for, and even argued over. Then everyone got used to it \u2014 and now all it does is collect dust.\r\n

The 10% That Succeed: What Successful BI Looks Like<\/h2>\r\nBI projects don\u2019t survive on intuition or enthusiasm alone. If they succeed, it\u2019s because the company did several fundamentally right things \u2014 most of which have nothing to do with technology.\r\n\r\nHere are four key success factors.\r\n

1. Strong Leadership and Clear Ownership<\/h3>\r\nSuccessful BI initiatives always have one or more owners who don\u2019t fully delegate responsibility to IT. They clearly define why BI exists and keep it in focus not just at launch, but every day.\r\n\r\nThis is personal accountability for success. Where leadership prioritizes BI, it survives and evolves. Where it\u2019s handed off without oversight, the project slowly stalls \u2014 even if it technically continues to exist.\r\n

2. Flexible, Iterative Approach<\/h3>\r\nMany BI problems begin with trying to cover everything at once. Systems are designed as universal solutions meant to answer all business questions. In reality, this almost always leads to delays and loss of focus before any tangible result appears.\r\n\r\nProjects that survive take a different path. They start with one specific scenario \u2014 where money is lost, transparency is lacking, or something doesn\u2019t add up.\r\n\r\nThis approach delivers quick, visible results. Teams see that the system works, skepticism fades, and only then does BI expand \u2014 carefully, step by step, without breaking what already works. These iterations, not grand designs, create scalability.\r\n

3. Focus on Quick Wins<\/h3>\r\nIf the first tangible BI benefit appears only after a year, the moment is already lost. Interest fades, and the system is seen as another long, drawn-out initiative with unclear value.\r\n\r\nTeams where BI sticks show results within 30\u201360 days. Not a full system \u2014 just one improvement that removes manual work, clarifies a metric, or simplifies a daily action. That\u2019s when the sense of value emerges, and people start asking new questions themselves.\r\n

4. Continuous Measurement and Adjustment<\/h3>\r\nSuccessful BI evolves alongside the business. What was useful a month ago may no longer be relevant \u2014 and that\u2019s normal.\r\n\r\nAnalytics is constantly tested in practice. Teams observe what users actually use and what they ignore, refine metrics, and remove excess. When processes or priorities change, the data model changes too.\r\n\r\nImportantly, attention is paid not only to business metrics but also to the system itself. If BI slows down, stops updating, or loses trust, it\u2019s noticed immediately. This continuous feedback loop keeps analytics embedded in daily operations.\r\n

How to Avoid Repeating Common Mistakes: A Step-by-Step Approach<\/h2>\r\nThere\u2019s no magic button for perfect BI. But there is a clear sequence of decisions that helps overcome the main challenges.\r\n\r\nStep 1. Define a specific business problem<\/strong>\r\nBI must start with a clear question \u2014 a loss, a blind spot, or an inefficiency. One problem, one BI scenario, one real change.\r\n\r\nStep 2. Assign a business owner<\/strong>\r\nThe project needs an owner with influence who understands why BI is being implemented.\r\n\r\nStep 3. Build an MVP around one micro-task<\/strong>\r\nThe first working use case should appear quickly \u2014 ideally within 30 days. One action or metric that becomes easier or faster. This is the first win that builds trust.\r\n\r\nStep 4. Involve end users before launch<\/strong>\r\nUsers must be part of the process. Otherwise, dashboards risk being built and never used.\r\n\r\nStep 5. Define how BI will change behavior<\/strong>\r\nEvery report must have a practical outcome \u2014 what is done differently now. If there\u2019s no answer, the report has no value.\r\n\r\nStep 6. Set up feedback and adaptation<\/strong>\r\nLaunch is the beginning, not the end. Regular reviews of what works and what doesn\u2019t keep BI relevant and alive.\r\n

From Failure to Success: Real-World BI Cases<\/h2>\r\n

Coca-Cola Bottling Company: Structured Data, Actionable Decisions<\/h3>\r\nOperating in an environment where delays quickly turn into losses, Coca-Cola Bottling Company needed up-to-date visibility across production, logistics, inventory, and sales.\r\n\r\nBefore BI, the company relied on manual reports and spreadsheets. Data came from multiple systems, updated with delays, and failed to provide a holistic view. Problems became visible only after impacting results.\r\n\r\nBI centralized key data and shifted from static reports to real-time metrics. Managers gained direct access to insights without constant IT dependency.\r\n\r\nAs a result, response times shortened, planning became more accurate, and operations more stable. BI stopped being a reporting tool and became part of daily operations.\r\n

NYSHEX: Faster Decisions Through Accessible BI<\/h3>\r\nNew York Shipping Exchange operates in global logistics, where access to information directly affects efficiency. After rapid growth in 2019, data handling became a bottleneck.\r\n\r\nAnalytics was slow and closed off. Data from product and cloud services was manually consolidated in Excel, and most requests landed on engineers.\r\n\r\nBI was implemented with a focus on accessibility: data was centralized, and tools were designed for non-technical users. Teams could explore metrics independently and get answers quickly.\r\n\r\nDecisions accelerated, engineers focused on product development, and BI became the foundation of operational speed.\r\n

Starbucks: BI for Customer Understanding<\/h3>\r\nStarbucks doesn\u2019t rely on intuition when choosing new locations. It analyzes demographics, income levels, traffic patterns, and nearby businesses to assess real potential.\r\n\r\nBI also powers local campaigns. During heatwaves in Memphis, cold drink promotions were launched; in areas with higher alcohol consumption, Starbucks Evenings was tested.\r\n\r\nLoyalty data enables precise timing and personalization of offers. BI creates the feeling that the brand understands and responds to customers in real time.\r\n

American Express: Deep Behavioral Insights Through Data<\/h3>\r\nAMEX leverages its closed payment system to gain a comprehensive view of transactions from both merchants and cardholders.\r\n\r\nBI supports not only fraud detection but personalized benefits, recommendations for restaurants and events, and more effective digital marketing. This approach increased engagement while reducing reliance on costly offline campaigns.\r\n

Get an Expert Assessment of Your BI Project<\/h2>\r\nAs this article shows, most BI projects face the same issues \u2014 they\u2019re just not always noticed in time. Often, analytics technically works but is barely used day-to-day. Not because of the tool, but because of how BI is embedded into processes and what it\u2019s actually used for.\r\n\r\nIn many cases, a full rebuild isn\u2019t necessary. It\u2019s enough to identify what already works, what adds unnecessary complexity, and which changes could deliver real impact in your specific context.\r\n\r\nIf you want to understand where to start or what to rethink in your existing BI setup, you can get independent feedback by booking a free workshop with IWIS<\/strong>."}]},"yoast_head":"\nWhy 90% of business analytics projects fail - iwis<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why 90% of business analytics projects fail - iwis\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/\" \/>\n<meta property=\"og:site_name\" content=\"iwis\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/IWIS.UKRAINE\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-30T14:57:24+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-31T19:51:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dev.iwis.io\/wp-content\/uploads\/2026\/01\/cropped-main-favicon.png\" \/>\n\t<meta property=\"og:image:width\" content=\"512\" \/>\n\t<meta property=\"og:image:height\" content=\"512\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Content Manager\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Content Manager\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/\"},\"author\":{\"name\":\"Content Manager\",\"@id\":\"https:\/\/dev.iwis.io\/en\/#\/schema\/person\/efd31411c3bd8b56c6722f7e8a4fbf5c\"},\"headline\":\"Why 90% of business analytics projects fail\",\"datePublished\":\"2026-01-30T14:57:24+00:00\",\"dateModified\":\"2026-01-31T19:51:09+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/\"},\"wordCount\":6,\"publisher\":{\"@id\":\"https:\/\/dev.iwis.io\/en\/#organization\"},\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/\",\"url\":\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/\",\"name\":\"Why 90% of business analytics projects fail - iwis\",\"isPartOf\":{\"@id\":\"https:\/\/dev.iwis.io\/en\/#website\"},\"datePublished\":\"2026-01-30T14:57:24+00:00\",\"dateModified\":\"2026-01-31T19:51:09+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/dev.iwis.io\/en\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Why 90% of business analytics projects fail\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/dev.iwis.io\/en\/#website\",\"url\":\"https:\/\/dev.iwis.io\/en\/\",\"name\":\"IWIS\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/dev.iwis.io\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/dev.iwis.io\/en\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/dev.iwis.io\/en\/#organization\",\"name\":\"IWIS\",\"url\":\"https:\/\/dev.iwis.io\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/dev.iwis.io\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/dev.iwis.io\/wp-content\/uploads\/2026\/01\/cropped-main-favicon.png\",\"contentUrl\":\"https:\/\/dev.iwis.io\/wp-content\/uploads\/2026\/01\/cropped-main-favicon.png\",\"width\":512,\"height\":512,\"caption\":\"IWIS\"},\"image\":{\"@id\":\"https:\/\/dev.iwis.io\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/IWIS.UKRAINE\/\",\"https:\/\/www.linkedin.com\/company\/iwis-ukraine\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/dev.iwis.io\/en\/#\/schema\/person\/efd31411c3bd8b56c6722f7e8a4fbf5c\",\"name\":\"Content Manager\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/dev.iwis.io\/en\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/a316e2a99de1810f03f68ead79a2cc2f7896b10ba340cd925bc0541c19f83cda?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/a316e2a99de1810f03f68ead79a2cc2f7896b10ba340cd925bc0541c19f83cda?s=96&d=mm&r=g\",\"caption\":\"Content Manager\"},\"sameAs\":[\"https:\/\/dev.iwis.io\"],\"url\":\"https:\/\/dev.iwis.io\/en\/author\/iwis-content-manager\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Why 90% of business analytics projects fail - iwis","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/","og_locale":"en_US","og_type":"article","og_title":"Why 90% of business analytics projects fail - iwis","og_url":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/","og_site_name":"iwis","article_publisher":"https:\/\/www.facebook.com\/IWIS.UKRAINE\/","article_published_time":"2026-01-30T14:57:24+00:00","article_modified_time":"2026-01-31T19:51:09+00:00","og_image":[{"width":512,"height":512,"url":"https:\/\/dev.iwis.io\/wp-content\/uploads\/2026\/01\/cropped-main-favicon.png","type":"image\/png"}],"author":"Content Manager","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Content Manager"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/#article","isPartOf":{"@id":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/"},"author":{"name":"Content Manager","@id":"https:\/\/dev.iwis.io\/en\/#\/schema\/person\/efd31411c3bd8b56c6722f7e8a4fbf5c"},"headline":"Why 90% of business analytics projects fail","datePublished":"2026-01-30T14:57:24+00:00","dateModified":"2026-01-31T19:51:09+00:00","mainEntityOfPage":{"@id":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/"},"wordCount":6,"publisher":{"@id":"https:\/\/dev.iwis.io\/en\/#organization"},"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/","url":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/","name":"Why 90% of business analytics projects fail - iwis","isPartOf":{"@id":"https:\/\/dev.iwis.io\/en\/#website"},"datePublished":"2026-01-30T14:57:24+00:00","dateModified":"2026-01-31T19:51:09+00:00","breadcrumb":{"@id":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/dev.iwis.io\/en\/blog\/proiekty-biznes-analityky\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/dev.iwis.io\/en\/"},{"@type":"ListItem","position":2,"name":"Why 90% of business analytics projects fail"}]},{"@type":"WebSite","@id":"https:\/\/dev.iwis.io\/en\/#website","url":"https:\/\/dev.iwis.io\/en\/","name":"IWIS","description":"","publisher":{"@id":"https:\/\/dev.iwis.io\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/dev.iwis.io\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/dev.iwis.io\/en\/#organization","name":"IWIS","url":"https:\/\/dev.iwis.io\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/dev.iwis.io\/en\/#\/schema\/logo\/image\/","url":"https:\/\/dev.iwis.io\/wp-content\/uploads\/2026\/01\/cropped-main-favicon.png","contentUrl":"https:\/\/dev.iwis.io\/wp-content\/uploads\/2026\/01\/cropped-main-favicon.png","width":512,"height":512,"caption":"IWIS"},"image":{"@id":"https:\/\/dev.iwis.io\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/IWIS.UKRAINE\/","https:\/\/www.linkedin.com\/company\/iwis-ukraine\/"]},{"@type":"Person","@id":"https:\/\/dev.iwis.io\/en\/#\/schema\/person\/efd31411c3bd8b56c6722f7e8a4fbf5c","name":"Content Manager","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/dev.iwis.io\/en\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/a316e2a99de1810f03f68ead79a2cc2f7896b10ba340cd925bc0541c19f83cda?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/a316e2a99de1810f03f68ead79a2cc2f7896b10ba340cd925bc0541c19f83cda?s=96&d=mm&r=g","caption":"Content Manager"},"sameAs":["https:\/\/dev.iwis.io"],"url":"https:\/\/dev.iwis.io\/en\/author\/iwis-content-manager\/"}]}},"_links":{"self":[{"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/posts\/4925","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/comments?post=4925"}],"version-history":[{"count":2,"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/posts\/4925\/revisions"}],"predecessor-version":[{"id":4927,"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/posts\/4925\/revisions\/4927"}],"wp:attachment":[{"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/media?parent=4925"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/categories?post=4925"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dev.iwis.io\/en\/wp-json\/wp\/v2\/tags?post=4925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}