{"id":210,"date":"2026-09-02T15:14:08","date_gmt":"2026-09-02T15:14:08","guid":{"rendered":"https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/"},"modified":"2026-09-02T15:50:30","modified_gmt":"2026-09-02T15:50:30","slug":"data-maturity-gap-findings","status":"publish","type":"post","link":"https:\/\/agileassetreliabilitysolutions.com\/en\/data-maturity-gap-findings\/","title":{"rendered":"What a data maturity gap actually costs: findings from an eight-plant programme"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.27.8&#8243; background_color=&#8221;#FBF9FC&#8221; custom_padding=&#8221;40px||60px||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row module_class=&#8221;aars-row&#8221; _builder_version=&#8221;4.27.8&#8243; width=&#8221;92%&#8221; max_width=&#8221;1140px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.27.8&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_code _builder_version=&#8221;4.27.8&#8243; global_colors_info=&#8221;{}&#8221;]<\/p>\n<style>\n.aq{font-family:Literata,Georgia,serif;color:#4A4553;line-height:1.75;font-size:17px}\n.aq h2{font-family:Archivo,Helvetica,Arial,sans-serif;color:#16111C;font-size:1.75rem;font-weight:700;letter-spacing:-.025em;line-height:1.12;margin:2.6rem 0 .9rem}\n.aq h3{font-family:Archivo,Helvetica,Arial,sans-serif;color:#16111C;font-size:1.05rem;font-weight:700;margin:0 0 .35rem}\n.aq p{max-width:78ch;margin:0 0 1.15rem}\n.aq .lead{font-size:1.14rem}\n.aq figure{margin:1.8rem 0 2rem;border-top:2px solid #16111C;padding-top:1.2rem}\n.aq figcaption{font-size:.93rem;line-height:1.6;color:#4A4553;max-width:82ch;margin-top:1rem;padding-top:.9rem;border-top:1px solid #DFD8E6}\n.aq .bar{margin:0 0 1.3rem;max-width:none}\n.aq .bar-l{display:flex;justify-content:space-between;font-family:Archivo,sans-serif;font-size:.9rem;font-weight:600;color:#16111C;margin-bottom:.4rem}\n.aq .bar-t{position:relative;height:20px;background:#F3EFF7}\n.aq .bar-f{position:absolute;left:0;top:0;bottom:0;background:#B026D9}\n.aq .bar-f.lo{background:#E27894}\n.aq .bar-m{position:absolute;top:-4px;bottom:-4px;width:2px;background:#16111C}\n.aq .bar-n{font-family:Literata,Georgia,serif;font-size:.88rem;color:#4A4553;margin-top:.35rem;max-width:78ch}\n.aq .split{display:flex;height:42px;border:1px solid #DFD8E6}\n.aq .split span{display:flex;align-items:center;justify-content:center;font-family:Archivo,sans-serif;font-size:.8rem;font-weight:600}\n.aq .s1{background:#B026D9;color:#fff}\n.aq .s2{background:#F3EFF7;color:#4A4553}\n.aq .gap{border-top:1px solid #DFD8E6;padding:.95rem 0}\n.aq .gap p{margin:0;font-size:.98rem;max-width:78ch}\n.aq .note{background:#F3EFF7;border-left:2px solid #B026D9;padding:1rem 1.1rem;margin:1.8rem 0}\n.aq .note p{margin:0;font-size:.97rem;max-width:82ch}\n.aq .rng{position:relative;height:48px;background:#F3EFF7;border:1px solid #DFD8E6}\n.aq .rng-b{position:absolute;top:0;bottom:0;background:#B026D9}\n.aq .rng-x{display:flex;justify-content:space-between;font-family:Archivo,sans-serif;font-size:.78rem;color:#4A4553;margin-top:.4rem}\n.aq .cn{display:flex;flex-wrap:wrap;gap:0;margin:1.3rem 0 .8rem;border-top:2px solid #16111C}\n.aq .cn-i{flex:1 1 150px;padding:.85rem 0;border-right:1px solid #DFD8E6}\n.aq .cn-i:not(:first-child){padding-left:.9rem}\n.aq .cn-i:last-child{border-right:0}\n.aq .cn-i span{font-family:Archivo,sans-serif;font-size:.85rem;font-weight:700;color:#16111C;line-height:1.25;display:block}\n.aq .cn-i.last{background:#F3EFF7;padding-left:.9rem;padding-right:.9rem}\n.aq .cn-i.last span{color:#B026D9}\n.aq .cn-cap{font-size:.92rem;line-height:1.6;color:#4A4553;max-width:82ch;margin:0}\n.aq .gap:last-of-type{border-bottom:1px solid #DFD8E6}\n@media(max-width:720px){.aq .cn{display:block}.aq .cn-i{border-right:0;border-bottom:1px solid #DFD8E6;padding:.7rem 0!important}.aq .cn-i.last{padding:.7rem .9rem!important;border-bottom:0}}\n<\/style>\n<div class=\"aq\"><pee class=\"lead\">We were brought into an eight-plant polymers and chemicals producer to run a data maturity programme ahead of an S\/4HANA implementation. What follows is the gap analysis and the outcome model, with the client removed and every figure converted to US dollars.<\/pee>\n<h2>The five gaps<\/h2>\n<pee>The asset register looked healthy from the executive dashboards and was not. Five measurable gaps carried almost all of the risk, and one of them was not being counted at all.<\/pee>\n<figure>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Master data quality<\/span><span>95.4%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f\" style=\"width:95.4%\"><\/div>\n<div class=\"bar-m\" style=\"left:66.75%\"><\/div>\n<\/div>\n<div class=\"bar-n\">Black marker shows the 66.75% starting point. This is the one gap that has been closed.<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>PM plan coverage on active assets<\/span><span>49%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:49%\"><\/div>\n<\/div>\n<div class=\"bar-n\">Just over half of running equipment had no maintenance plan attached.<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>BOM completeness<\/span><span>26%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:26%\"><\/div>\n<\/div>\n<div class=\"bar-n\">Three quarters of assets could not tell a planner which spare they needed.<\/div>\n<\/div><figcaption>Master data quality was fixed in the first phase. Plan coverage and BOM completeness were not, and they are where the remaining exposure sits.<\/figcaption><\/figure>\n<h2>What the gaps were costing<\/h2>\n<pee>Over the reference period the site recorded 81 days of downtime against roughly 61 million dollars of production loss exposure, an implied 757 thousand dollars for every day lost. Slightly over half of those days traced back to equipment failure rather than market, feedstock or planned outage.<\/pee>\n<figure>\n<div class=\"split\"><span class=\"s1\" style=\"width:50.6%\">41 days equipment failure<\/span><span class=\"s2\" style=\"width:49.4%\">40 days other causes<\/span><\/div><figcaption>The 41 equipment-failure days represent about 31 million dollars of exposure. That is the pool a data maturity programme can credibly work on. The other 40 days are outside its reach and were excluded from the model.<\/figcaption><\/figure>\n<h2>Gap findings, in order of consequence<\/h2>\n<div class=\"gap\">\n<h3>Maintenance plans absent on half the running assets<\/h3>\n<pee>Coverage at 49% means preventive work was being scheduled from memory and habit rather than from the system. Closing this and BOM completeness together accounts for more than 70% of the recoverable loss in the model.<\/pee><\/div>\n<div class=\"gap\">\n<h3>Bills of material largely unbuilt<\/h3>\n<pee>At 26% completeness, planners could not reserve parts against a job. Wrench time was lost to store room trips and emergency procurement rather than to maintenance.<\/pee><\/div>\n<div class=\"gap\">\n<h3>Inventory growing on the back of the missing BOMs<\/h3>\n<pee>This is the gap that shows up in the finance report rather than the maintenance one, and it is the direct downstream consequence of BOM completeness sitting at 26 percent. With no reliable link between an asset and the parts it consumes, stocking decisions get made defensively. Buyers order against memory and worst case rather than against a bill of material, reorder points are set without consumption history to justify them, and nothing has an owner who can prove a part is no longer needed.<\/pee><pee style=\"margin-top:.9rem\">The result compounds quietly. Inventory value climbs while availability of the parts actually wanted stays flat, slow and non moving stock accumulates against assets that may already have been modified or retired, and write off exposure builds on a balance sheet nobody is reviewing against the asset register. None of this appeared in the downtime model, because carrying cost and obsolescence are a separate loss pool from production loss.<\/pee>\n<div class=\"cn\">\n<div class=\"cn-i\"><span>No BOM on the asset<\/span><\/div>\n<div class=\"cn-i\"><span>No asset to spare link<\/span><\/div>\n<div class=\"cn-i\"><span>Defensive stocking<\/span><\/div>\n<div class=\"cn-i\"><span>Slow and non moving stock<\/span><\/div>\n<div class=\"cn-i last\"><span>Rising inventory value and write off exposure<\/span><\/div>\n<\/div>\n<pee class=\"cn-cap\">The chain runs one way. Nothing downstream of the first link can be fixed by inventory policy alone, which is why stock reduction drives that skip the bill of material tend to rebound within two years.<\/pee><pee style=\"margin-top:1.2rem\"><strong>The outcome.<\/strong> Inventory value and slow and dead stock had grown every year across the five years preceding the programme. Nine months after the data maturity work went live, inventory value fell year on year for the first time in five years. Of everything the programme produced, this is the number that reached the balance sheet rather than the maintenance report, and it is the one a finance director will ask about first.<\/pee><\/div>\n<div class=\"gap\">\n<h3>Master data quality below usable threshold<\/h3>\n<pee>Starting at 66.75%, roughly a third of records could not be trusted for reporting. Rebuilt against an ISO 14224 taxonomy, this reached 95.4% in the first phase.<\/pee><\/div>\n<div class=\"gap\">\n<h3>Failure attribution never established<\/h3>\n<pee class=\"lastgap\">No sampled root cause review existed linking downtime days to data-addressable causes. Without it, any benefit case rests on an industry assumption rather than the plant&#8217;s own evidence. We flagged this as the single largest source of uncertainty in the model.<\/pee><\/div>\n<h2>The warehouse is where the strategy shows up<\/h2>\n<pee>If you want to know whether an asset management strategy is real, do not read the maintenance report. Walk the warehouse. Every weakness upstream lands there eventually, as stock that was bought against a guess, held against an asset nobody re-checked, and written off years later by someone who never knew why it was ordered. The warehouse is the slowest and most honest indicator of asset management performance there is.<\/pee><pee>On this programme it was also the first place the work showed up in money.<\/pee>\n<h3 style=\"margin-top:2rem\">The starting position<\/h3>\n<pee>Nine out of every ten items in stock were slow moving. The international benchmark sits near fifteen percent. That gap is not a procurement failure, it is what happens when parts are bought and stored without a maintenance strategy telling anyone which asset needs them.<\/pee>\n<figure>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Slow moving share of inventory, at start<\/span><span>90%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f\" style=\"width:90%\"><\/div>\n<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>International benchmark<\/span><span>15%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:15%\"><\/div>\n<\/div>\n<\/div><figcaption>Six times the benchmark. Stock was being held against equipment that in many cases had no bill of material, and in some cases no longer existed in the form the part was bought for.<\/figcaption><\/figure>\n<h3 style=\"margin-top:2rem\">What was built<\/h3>\n<pee>62,154 bills of material were rebuilt against vendor documentation and operational recommendations, taking BOM coverage of the maintainable asset base to 26 percent. Three spare classes were configured so the structure could carry a real stocking policy rather than a flat parts list.<\/pee>\n<figure>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Operational spares<\/span><span>46,312<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f\" style=\"width:100%\"><\/div>\n<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Life cycle and capital spares<\/span><span>13,989<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f\" style=\"width:30.2%\"><\/div>\n<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Single line BOM<\/span><span>2,563<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f\" style=\"width:5.5%\"><\/div>\n<\/div>\n<\/div><figcaption>Separating operational spares from life cycle and capital spares is what lets a planner stock differently for a part consumed monthly and a part held once for a ten year overhaul. Without that split, everything defaults to the cautious answer, and the cautious answer is what fills a warehouse.<\/figcaption><\/figure>\n<h3 style=\"margin-top:2rem\">What it moved<\/h3>\n<pee>Coverage at 26 percent is not a finished job. It was still enough to break a five year trend. Inventory value fell by 2.4 million dollars, the first reduction in five years, and the first driven by data rather than by a spending freeze. Modelled forward at a conservative two percent volume reduction a year, the same work carries roughly 17.9 million dollars over five years, and materially more if the stocking philosophy is implemented aggressively.<\/pee>\n<figure>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Realised in year one<\/span><span>USD 2.4M<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f\" style=\"width:4.5%\"><\/div>\n<\/div>\n<div class=\"bar-n\">Achieved. First inventory reduction in five years.<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Five year projection, conservative<\/span><span>USD 17.9M<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:33.6%\"><\/div>\n<\/div>\n<div class=\"bar-n\">Modelled at 2% inventory volume reduction per year.<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Five year upside, aggressive strategy<\/span><span>USD 53.3M<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:100%\"><\/div>\n<\/div>\n<div class=\"bar-n\">Requires the full stocking philosophy, not just the data.<\/div>\n<\/div><figcaption>Only the first bar has happened. The other two are projections and are labelled as such deliberately, because the difference between them is execution, not analysis.<\/figcaption><\/figure>\n<h3 style=\"margin-top:2rem\">Where the value actually sits<\/h3>\n<pee>Worth noting what the BOM work pays for. Inventory reduction is the visible headline, but it is the smallest share. Most of the return comes from spares being findable and correct when a job is planned.<\/pee>\n<figure>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Spares optimisation<\/span><span>40%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f\" style=\"width:40%\"><\/div>\n<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Rework and repeat jobs avoided<\/span><span>20%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:20%\"><\/div>\n<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Downtime and MTTR reduction<\/span><span>20%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:20%\"><\/div>\n<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Planning efficiency<\/span><span>10%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:10%\"><\/div>\n<\/div>\n<\/div>\n<div class=\"bar\">\n<div class=\"bar-l\"><span>Inventory optimisation and dead stock<\/span><span>10%<\/span><\/div>\n<div class=\"bar-t\">\n<div class=\"bar-f lo\" style=\"width:10%\"><\/div>\n<\/div>\n<\/div><figcaption>The balance sheet result is the one that gets the meeting. The wrench time result is the one that pays for the programme.<\/figcaption><\/figure>\n<h2>What the model says is recoverable<\/h2>\n<pee>Against the 31 million dollar equipment-failure pool, and using a realisation rate between 25 and 40 percent, the programme models annual loss avoidance of roughly 7.8 to 12.4 million dollars once the remaining gaps are closed.<\/pee>\n<figure>\n<div class=\"rng\">\n<div class=\"rng-b\" style=\"left:25%;width:15%\"><\/div>\n<\/div>\n<div class=\"rng-x\"><span>0<\/span><span>USD 7.8M<\/span><span>USD 12.4M<\/span><span>USD 31M exposure<\/span><\/div><figcaption>The violet band is the modelled annual range. The bar as a whole is the equipment-failure exposure. We present a range rather than a single figure on purpose: a point estimate invites an argument about the number instead of a decision about the work.<\/figcaption><\/figure>\n<div class=\"note\"><pee><strong>What is delivered and what is modelled.<\/strong> The move from 66.75% to 95.4% master data quality is a delivered outcome. The loss avoidance range is a projection built on three adjustable inputs: the share of failure days addressable by data quality, the realisation rate, and the ramp. It should be recalibrated against a sampled root cause review of the 41 failure days before anyone commits to it. Neither figure includes inventory carrying cost, obsolescence or write off. Those sit outside the production loss pool entirely and were tracked separately, so the inventory result reported above is additional to the range shown here rather than part of it.<\/pee><\/div>\n<pee>Figures converted to US dollars at the fixed rate applying in the operating jurisdiction. The client, its location and its plant identifiers have been removed.<\/pee><\/div>\n<p>[\/et_pb_code][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Gap analysis and outcome model from a pre-S\/4HANA data maturity programme: plan coverage at 49%, BOMs at 26%, and what the shortfall was costing per day of downtime.<\/p>","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[6],"tags":[],"class_list":["post-210","post","type-post","status-publish","format-standard","hentry","category-asset-data"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What a Data Maturity Gap Actually Costs | Case Study<\/title>\n<meta name=\"description\" content=\"Gap analysis from a data maturity programme: BOM coverage, 90% slow moving stock, and the first inventory value reduction in five years. Figures in USD.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/agileassetreliabilitysolutions.com\/en\/data-maturity-gap-findings\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What a Data Maturity Gap Actually Costs | Case Study\" \/>\n<meta property=\"og:description\" content=\"Gap analysis from a data maturity programme: BOM coverage, 90% slow moving stock, and the first inventory value reduction in five years. Figures in USD.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/agileassetreliabilitysolutions.com\/en\/data-maturity-gap-findings\/\" \/>\n<meta property=\"og:site_name\" content=\"Agile Asset Reliability Solutions\" \/>\n<meta property=\"article:published_time\" content=\"2026-09-02T15:14:08+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-09-02T15:50:30+00:00\" \/>\n<meta name=\"author\" content=\"Joshua Elaiho\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Joshua Elaiho\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/\"},\"author\":{\"name\":\"Joshua Elaiho\",\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#\\\/schema\\\/person\\\/004055869cececb52211f48a3e39e6b4\"},\"headline\":\"What a data maturity gap actually costs: findings from an eight-plant programme\",\"datePublished\":\"2026-09-02T15:14:08+00:00\",\"dateModified\":\"2026-09-02T15:50:30+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/\"},\"wordCount\":1495,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#organization\"},\"articleSection\":[\"Asset data\"],\"inLanguage\":\"en\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/\",\"url\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/\",\"name\":\"What a Data Maturity Gap Actually Costs | Case Study\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#website\"},\"datePublished\":\"2026-09-02T15:14:08+00:00\",\"dateModified\":\"2026-09-02T15:50:30+00:00\",\"description\":\"Gap analysis from a data maturity programme: BOM coverage, 90% slow moving stock, and the first inventory value reduction in five years. Figures in USD.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/#breadcrumb\"},\"inLanguage\":\"en\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/data-maturity-gap-findings\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"What a data maturity gap actually costs: findings from an eight-plant programme\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#website\",\"url\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/\",\"name\":\"Agile Asset Reliability Solutions\",\"description\":\"Reliability and asset management consulting for mining, oil and gas, and petrochemicals\",\"publisher\":{\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#organization\",\"name\":\"Agile Asset Reliability Solutions Inc.\",\"url\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en\",\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/wp-content\\\/uploads\\\/2026\\\/04\\\/cropped-Logo-Joshua-Elaiho_logo-scaled-1.png\",\"contentUrl\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/wp-content\\\/uploads\\\/2026\\\/04\\\/cropped-Logo-Joshua-Elaiho_logo-scaled-1.png\",\"width\":512,\"height\":512,\"caption\":\"Agile Asset Reliability Solutions Inc.\"},\"image\":{\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/#\\\/schema\\\/person\\\/004055869cececb52211f48a3e39e6b4\",\"name\":\"Joshua Elaiho\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/33eaa506e4e9af8237ee9a4c3855722fa234e179508934ad949fbb9f3a6cca63?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/33eaa506e4e9af8237ee9a4c3855722fa234e179508934ad949fbb9f3a6cca63?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/33eaa506e4e9af8237ee9a4c3855722fa234e179508934ad949fbb9f3a6cca63?s=96&d=mm&r=g\",\"caption\":\"Joshua Elaiho\"},\"url\":\"https:\\\/\\\/agileassetreliabilitysolutions.com\\\/en\\\/author\\\/joshua\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"What a Data Maturity Gap Actually Costs | Case Study","description":"Gap analysis from a data maturity programme: BOM coverage, 90% slow moving stock, and the first inventory value reduction in five years. Figures in USD.","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:\/\/agileassetreliabilitysolutions.com\/en\/data-maturity-gap-findings\/","og_locale":"en_US","og_type":"article","og_title":"What a Data Maturity Gap Actually Costs | Case Study","og_description":"Gap analysis from a data maturity programme: BOM coverage, 90% slow moving stock, and the first inventory value reduction in five years. Figures in USD.","og_url":"https:\/\/agileassetreliabilitysolutions.com\/en\/data-maturity-gap-findings\/","og_site_name":"Agile Asset Reliability Solutions","article_published_time":"2026-09-02T15:14:08+00:00","article_modified_time":"2026-09-02T15:50:30+00:00","author":"Joshua Elaiho","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Joshua Elaiho","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/#article","isPartOf":{"@id":"https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/"},"author":{"name":"Joshua Elaiho","@id":"https:\/\/agileassetreliabilitysolutions.com\/#\/schema\/person\/004055869cececb52211f48a3e39e6b4"},"headline":"What a data maturity gap actually costs: findings from an eight-plant programme","datePublished":"2026-09-02T15:14:08+00:00","dateModified":"2026-09-02T15:50:30+00:00","mainEntityOfPage":{"@id":"https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/"},"wordCount":1495,"commentCount":0,"publisher":{"@id":"https:\/\/agileassetreliabilitysolutions.com\/#organization"},"articleSection":["Asset data"],"inLanguage":"en","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/","url":"https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/","name":"What a Data Maturity Gap Actually Costs | Case Study","isPartOf":{"@id":"https:\/\/agileassetreliabilitysolutions.com\/#website"},"datePublished":"2026-09-02T15:14:08+00:00","dateModified":"2026-09-02T15:50:30+00:00","description":"Gap analysis from a data maturity programme: BOM coverage, 90% slow moving stock, and the first inventory value reduction in five years. Figures in USD.","breadcrumb":{"@id":"https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/#breadcrumb"},"inLanguage":"en","potentialAction":[{"@type":"ReadAction","target":["https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/agileassetreliabilitysolutions.com\/data-maturity-gap-findings\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/agileassetreliabilitysolutions.com\/"},{"@type":"ListItem","position":2,"name":"What a data maturity gap actually costs: findings from an eight-plant programme"}]},{"@type":"WebSite","@id":"https:\/\/agileassetreliabilitysolutions.com\/#website","url":"https:\/\/agileassetreliabilitysolutions.com\/","name":"Agile Asset Reliability Solutions","description":"Reliability and asset management consulting for mining, oil and gas, and petrochemicals","publisher":{"@id":"https:\/\/agileassetreliabilitysolutions.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/agileassetreliabilitysolutions.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en"},{"@type":"Organization","@id":"https:\/\/agileassetreliabilitysolutions.com\/#organization","name":"Agile Asset Reliability Solutions Inc.","url":"https:\/\/agileassetreliabilitysolutions.com\/","logo":{"@type":"ImageObject","inLanguage":"en","@id":"https:\/\/agileassetreliabilitysolutions.com\/#\/schema\/logo\/image\/","url":"https:\/\/agileassetreliabilitysolutions.com\/wp-content\/uploads\/2026\/04\/cropped-Logo-Joshua-Elaiho_logo-scaled-1.png","contentUrl":"https:\/\/agileassetreliabilitysolutions.com\/wp-content\/uploads\/2026\/04\/cropped-Logo-Joshua-Elaiho_logo-scaled-1.png","width":512,"height":512,"caption":"Agile Asset Reliability Solutions Inc."},"image":{"@id":"https:\/\/agileassetreliabilitysolutions.com\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/agileassetreliabilitysolutions.com\/#\/schema\/person\/004055869cececb52211f48a3e39e6b4","name":"Joshua Elaiho","image":{"@type":"ImageObject","inLanguage":"en","@id":"https:\/\/secure.gravatar.com\/avatar\/33eaa506e4e9af8237ee9a4c3855722fa234e179508934ad949fbb9f3a6cca63?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/33eaa506e4e9af8237ee9a4c3855722fa234e179508934ad949fbb9f3a6cca63?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/33eaa506e4e9af8237ee9a4c3855722fa234e179508934ad949fbb9f3a6cca63?s=96&d=mm&r=g","caption":"Joshua Elaiho"},"url":"https:\/\/agileassetreliabilitysolutions.com\/en\/author\/joshua\/"}]}},"_links":{"self":[{"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/posts\/210","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/comments?post=210"}],"version-history":[{"count":4,"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/posts\/210\/revisions"}],"predecessor-version":[{"id":215,"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/posts\/210\/revisions\/215"}],"wp:attachment":[{"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/media?parent=210"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/categories?post=210"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/agileassetreliabilitysolutions.com\/en\/wp-json\/wp\/v2\/tags?post=210"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}