{"id":3684,"date":"2020-11-23T18:56:24","date_gmt":"2020-11-23T23:56:24","guid":{"rendered":"https:\/\/prolifics.com\/us\/?p=3684"},"modified":"2025-10-27T16:42:35","modified_gmt":"2025-10-27T11:12:35","slug":"how-to-inventory-your-analytic-ecosystem","status":"publish","type":"post","link":"https:\/\/prolifics.com\/usa\/resource-center\/blog\/how-to-inventory-your-analytic-ecosystem","title":{"rendered":"How to Inventory Your Analytic Ecosystem"},"content":{"rendered":"\n<p><em><strong>By Michael Gonzales, PhD<\/strong><\/em><\/p>\n\n\n\n<p>An Analytic Ecosystem Inventory (AEI) helps organizations document and quantify their current analytics landscape. It collects metrics about <a href=\"https:\/\/prolifics.com\/usa\/ai-powered-expertise\/data-engineering-and-analytics\" data-type=\"link\" data-id=\"https:\/\/prolifics.com\/usa\/ai-powered-expertise\/data-engineering-and-analytics\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">analytic applications<\/mark><\/a>, supporting technologies, and data sources \u2014 offering a clear view of how analytics is implemented and consumed across the business.<\/p>\n\n\n\n<p>Unlike general assessments that capture opinions from analytic users, the AEI focuses on quantitative insights. It measures the technologies, applications, and user communities that shape your organization\u2019s analytic maturity.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What the Analytic Ecosystem Inventory Includes<\/h2>\n\n\n\n<p>The AEI framework typically covers four major components:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Antecedents:<\/strong> Documentation related to analytics, corporate objectives, and standards.<\/li>\n\n\n\n<li><strong>Applications:<\/strong> Analytic applications, their user base, and life stage.<\/li>\n\n\n\n<li><strong>Technologies:<\/strong> Tools and platforms supporting analytic operations.<\/li>\n\n\n\n<li><strong>Data:<\/strong> Information about data sources, size, type, and frequency of use.<\/li>\n<\/ul>\n\n\n\n<p>To ensure consistency, it\u2019s best to use a structured instrument \u2014 such as a spreadsheet \u2014 for gathering data. This enables accurate, repeatable collection even when multiple teams are involved.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1.<\/strong> <strong>Understanding Antecedents<\/strong><\/h3>\n\n\n\n<p>Antecedents are formal documents used to evaluate the maturity of analytics in an organization.<br>For example, a business strategy that references analytics demonstrates that the enterprise recognizes data as a competitive advantage.<\/p>\n\n\n\n<p>Below are key artifacts assessment teams should review when determining analytic maturity:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Business strategy documents referencing analytics (can be redacted if necessary)<\/li>\n\n\n\n<li>Analytic strategy documentation<\/li>\n\n\n\n<li>Organization charts for analytics and data governance teams<\/li>\n\n\n\n<li>Analytic development and implementation standards<\/li>\n\n\n\n<li>Example of a requirements document for the analytic environment<\/li>\n\n\n\n<li>Example of a test plan for an implemented analytic application<\/li>\n\n\n\n<li>Example of a Service Level Agreement (SLA) with user communities<\/li>\n\n\n\n<li>Education curriculum or training offered by the analytics team<\/li>\n\n\n\n<li>Course evaluation forms used post-training<\/li>\n<\/ul>\n\n\n\n<p>Gathering these documents requires minimal resources. Teams can request documents during the kickoff session, follow up with examples via email, and confirm titles shared by internal stakeholders.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2.<\/strong> <strong>Technical, Data, and Application Examination<\/strong><\/h3>\n\n\n\n<p>An Excel-based AEI spreadsheet helps structure and standardize information collection. It should include columns for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Technology licenses<\/li>\n\n\n\n<li>Data sources and size<\/li>\n\n\n\n<li>Supported user communities<\/li>\n\n\n\n<li>Applications built on each technology<\/li>\n<\/ul>\n\n\n\n<p><strong>Table 1<\/strong>: Architecture Inventory (not shown)<\/p>\n\n\n\n<p>This format provides clarity when evaluating relationships among applications, technologies, and users.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter is-resized\"><img decoding=\"async\" width=\"1024\" height=\"812\" data-src=\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Architecture-inventory-1024x812.png\" alt=\"\" class=\"wp-image-3685 lazyload\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/812;width:818px;height:auto\" title=\"\" data-srcset=\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Architecture-inventory-1024x812.png 1024w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Architecture-inventory-300x238.png 300w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Architecture-inventory-768x609.png 768w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Architecture-inventory.png 1427w\" data-sizes=\"auto\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" data-original-sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Techniques for Analyzing the AEI<\/h3>\n\n\n\n<p>The AEI involves two primary areas of assessment:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Formal antecedent documentation<\/li>\n\n\n\n<li>The inventory of analytic applications, supporting technology, and data<\/li>\n<\/ol>\n\n\n\n<p>To gain accurate insights, teams should not analyze these areas in isolation. Instead, compare findings across information sources\u2014such as surveys, interviews, and inventory data\u2014to validate consistency.<\/p>\n\n\n\n<p>For instance:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If SMEs mention specific analytics technology standards, those should appear in the technology inventory.<\/li>\n\n\n\n<li>If SMEs claim there\u2019s no formal training but the organization offers a structured curriculum, that discrepancy needs investigation.<\/li>\n<\/ul>\n\n\n\n<p><strong>Figure 1<\/strong> \u2013 Overlapping Information Channels highlights how these data sources intersect.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" width=\"406\" height=\"405\" data-src=\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/overlapping-information-channels.png\" alt=\"\" class=\"wp-image-3686 lazyload\" title=\"\" data-srcset=\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/overlapping-information-channels.png 406w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/overlapping-information-channels-300x300.png 300w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/overlapping-information-channels-150x150.png 150w\" data-sizes=\"auto\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 406px; --smush-placeholder-aspect-ratio: 406\/405;\" data-original-sizes=\"(max-width: 406px) 100vw, 406px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Analyzing Antecedents<\/h3>\n\n\n\n<p>To evaluate antecedent documents effectively, teams can:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Review each document and provide observations.<\/li>\n\n\n\n<li>Use a Likert Scale to rate maturity factors.<\/li>\n<\/ul>\n\n\n\n<p>This structured approach enhances repeatability and transparency, compared to subjective review methods.<br><em>(Refer to Table 2 \u2013 Assessing Antecedents for sample evaluation criteria.)<\/em><\/p>\n\n\n\n<p><strong>Table 2 \u2013 Assessing Antecedents<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter is-resized\"><img decoding=\"async\" width=\"1024\" height=\"663\" data-src=\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Assessing-Antecedents-1024x663.png\" alt=\"\" class=\"wp-image-3687 lazyload\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/663;width:749px;height:auto\" title=\"\" data-srcset=\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Assessing-Antecedents-1024x663.png 1024w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Assessing-Antecedents-300x194.png 300w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Assessing-Antecedents-768x497.png 768w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/11\/Assessing-Antecedents.png 1392w\" data-sizes=\"auto\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" data-original-sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5. Analyzing the Inventory<\/h3>\n\n\n\n<p>The AEI offers valuable insights based on quantitative patterns. Analysts can use these patterns to answer key questions such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Support for Standards<\/strong> \u2013 How consistent are the technologies supporting analytics? Are multiple versions in use?<\/li>\n\n\n\n<li><strong>Application Maturity<\/strong> \u2013 Are applications mostly new, expanding, mature, or legacy?<\/li>\n\n\n\n<li><strong>User Communities<\/strong> \u2013 Do these applications support broad user groups or niche teams?<\/li>\n\n\n\n<li><strong>Departmental Concentration<\/strong> \u2013 Are analytic applications centralized or spread across departments?<\/li>\n\n\n\n<li><strong>Data Latency<\/strong> \u2013 Is the data consumed in batches, real time, or on demand?<\/li>\n<\/ul>\n\n\n\n<p>These findings help organizations identify gaps, improve efficiency, and enhance governance across their analytics landscape.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>Conducting an Analytic Ecosystem Inventory provides organizations with a comprehensive snapshot of their analytic maturity. By documenting antecedents, technologies, applications, and data sources, teams can uncover improvement opportunities, align analytics with business goals, and support future scalability.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">About the Author<\/h4>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" width=\"200\" height=\"200\" data-src=\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/06\/michael-e1605640909943.png\" alt=\"\" class=\"wp-image-3577 lazyload\" title=\"\" data-srcset=\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/06\/michael-e1605640909943.png 200w, https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/06\/michael-e1605640909943-150x150.png 150w\" data-sizes=\"auto\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 200px; --smush-placeholder-aspect-ratio: 200\/200;\" data-original-sizes=\"(max-width: 200px) 100vw, 200px\" \/><\/figure>\n\n\n\n<p><a href=\"https:\/\/www.linkedin.com\/in\/michael-gonzales-231b9085\/\" target=\"_blank\" rel=\"noopener\">Michael L. Gonzales, Ph.D.,<\/a> is an IT industry veteran with over 30 years of experience as a Chief Architect and Senior Solutions Strategist. He specializes in leveraging business analytics for competitive advantage in global enterprises.<\/p>\n\n\n\n<p>His research and presentations have been featured at leading international conferences such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Decision Sciences Institute<\/li>\n\n\n\n<li>Americas Conference on Information Systems<\/li>\n\n\n\n<li>Hawaii International Conference on Systems Science<\/li>\n<\/ul>\n\n\n\n<p>Dr. Gonzales holds a Ph.D. in Information and Decision Science from the University of Texas.<br>He currently serves as Managing Partner at dss42, LLC, and Senior Data Scientist at Prolifics.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>By Michael Gonzales, PhD An Analytic Ecosystem Inventory (AEI) helps organizations document and quantify their current analytics landscape. It collects metrics about analytic applications, supporting technologies, and data sources \u2014 [&hellip;]<\/p>\n","protected":false},"author":34,"featured_media":29517,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[49],"tags":[282],"class_list":["post-3684","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-data-ai","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"builder_content":"","_links":{"self":[{"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/posts\/3684","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/users\/34"}],"replies":[{"embeddable":true,"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/comments?post=3684"}],"version-history":[{"count":0,"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/posts\/3684\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/media\/29517"}],"wp:attachment":[{"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/media?parent=3684"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/categories?post=3684"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prolifics.com\/usa\/wp-json\/wp\/v2\/tags?post=3684"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}