{"id":86,"date":"2026-02-08T20:42:31","date_gmt":"2026-02-08T20:42:31","guid":{"rendered":"http:\/\/rlsnase.cluster030.hosting.ovh.net\/?page_id=86"},"modified":"2026-02-08T22:35:30","modified_gmt":"2026-02-08T22:35:30","slug":"how_do_i","status":"publish","type":"page","link":"https:\/\/dziadecki.com\/?page_id=86","title":{"rendered":"How do I translate business problem into an data science solution"},"content":{"rendered":"\n<p>Most data projects fail for a simple reason: they start with a model, not a decision. \u201cWe need a churn model\u201d is not a business problem. The business problem is: \u201cWhich customers should we prioritise this month to reduce churn at the lowest cost, and how will we measure success?\u201d<\/p>\n\n\n\n<p>A good data science solution is a <strong>decision system<\/strong>, not a spreadsheet or an algorithm. Here\u2019s a practical way to translate business questions into robust analytical work\u2014without overengineering.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator alignfull has-alpha-channel-opacity is-style-default\"\/>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Step 1: Start from the decision, not from the data<\/h3>\n\n\n\n<p>Define the decision you want to enable in one sentence:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em>Allocate budget across channels for next quarter.<\/em><\/li>\n\n\n\n<li><em>Prioritise customers for retention outreach.<\/em><\/li>\n\n\n\n<li><em>Select the best price point for a new product tier.<\/em><\/li>\n<\/ul>\n\n\n\n<p>Then define the \u201cso what\u201d:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Who makes the decision?<\/li>\n\n\n\n<li>How often?<\/li>\n\n\n\n<li>What\u2019s the cost of a wrong decision?<\/li>\n\n\n\n<li>What does \u201cbetter\u201d mean: revenue, margin, retention, brand KPIs, risk?<\/li>\n<\/ul>\n\n\n\n<p>This step determines whether you need forecasting, causal inference, optimisation, segmentation\u2014or something much simpler.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Step 2: Translate the decision into measurable outcomes<\/h3>\n\n\n\n<p>Turn the decision into <strong>metrics and a target variable<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Churn \u2192 churn within 30\/60\/90 days<\/li>\n\n\n\n<li>Growth \u2192 incremental revenue or conversion<\/li>\n\n\n\n<li>Effectiveness \u2192 baseline vs incremental impact<\/li>\n\n\n\n<li>Research \u2192 preference share, drivers, willingness-to-pay<\/li>\n<\/ul>\n\n\n\n<p>Make the metric operational: scope, time window, granularity, inclusion\/exclusion rules. If you can\u2019t define it precisely, you can\u2019t validate the result.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Step 3: Map constraints and \u201cmust-have\u201d business rules<\/h3>\n\n\n\n<p>Real-world solutions live inside constraints:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>data freshness (daily vs monthly)<\/li>\n\n\n\n<li>actionability (can we contact the customer? change price? shift spend?)<\/li>\n\n\n\n<li>legal\/brand constraints (GDPR, fairness, brand safety)<\/li>\n\n\n\n<li>operational limits (call-centre capacity, campaign volume)<\/li>\n<\/ul>\n\n\n\n<p>Constraints are not a nuisance\u2014they define the design. A model that is 2% better but impossible to deploy is worse than a simple rule that people trust and use.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Step 4: Audit the data for decision quality (not just completeness)<\/h3>\n\n\n\n<p>Before modelling, check whether the data can support the decision:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Is the outcome measurable and reliable?<\/li>\n\n\n\n<li>Are key drivers available (or proxy variables)?<\/li>\n\n\n\n<li>Is there leakage (features that \u201cknow the future\u201d)?<\/li>\n\n\n\n<li>Are there seasonality effects, cohort effects, or structural breaks?<\/li>\n<\/ul>\n\n\n\n<p>This is also where you set the evaluation strategy: holdout periods, backtesting, and sensitivity checks.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Step 5: Choose the simplest method that answers the question<\/h3>\n\n\n\n<p>Method follows the decision:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Segmentation<\/strong> when you need distinct groups and differentiated actions<\/li>\n\n\n\n<li><strong>Propensity \/ churn models<\/strong> when you need prioritisation<\/li>\n\n\n\n<li><strong>MMM \/ causal impact<\/strong> when you need incremental contribution and budget decisions<\/li>\n\n\n\n<li><strong>Conjoint \/ preference models<\/strong> when you need trade-offs and pricing guidance<\/li>\n<\/ul>\n\n\n\n<p>Start simple, prove value, then increase sophistication only if it changes decisions.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 6: Deliver the solution as a tool, not as a report<\/h3>\n\n\n\n<p>The output should be usable by non-technical stakeholders:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>a ranked list (who to target, why, and expected impact)<\/li>\n\n\n\n<li>response curves and scenarios (what happens if budget shifts)<\/li>\n\n\n\n<li>dashboards with a short \u201cinsights log\u201d<\/li>\n\n\n\n<li>clear recommendations with assumptions and limitations<\/li>\n<\/ul>\n\n\n\n<p>The goal is adoption. If it\u2019s not used, it\u2019s not a solution.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Step 7: Close the loop with measurement and iteration<\/h3>\n\n\n\n<p>Define what \u201csuccess\u201d means and how you will measure it:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>incremental lift, ROI, retention delta<\/li>\n\n\n\n<li>stability over time, drift monitoring<\/li>\n\n\n\n<li>periodic recalibration<\/li>\n<\/ul>\n\n\n\n<p>Data science is not a one-off deliverable\u2014it\u2019s a learning system.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>In short:<\/strong> translate business problems into data science solutions by anchoring the work in a decision, defining measurable outcomes, respecting constraints, choosing appropriate (often simple) methods, and delivering something people can actually use.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most data projects fail for a simple reason: they start with a model, not a decision. \u201cWe need a churn model\u201d is not a business problem. The business problem is: \u201cWhich customers should we prioritise this month to reduce churn at the lowest cost, and how will we measure success?\u201d A good data science solution [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-86","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/dziadecki.com\/index.php?rest_route=\/wp\/v2\/pages\/86","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dziadecki.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dziadecki.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dziadecki.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dziadecki.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=86"}],"version-history":[{"count":5,"href":"https:\/\/dziadecki.com\/index.php?rest_route=\/wp\/v2\/pages\/86\/revisions"}],"predecessor-version":[{"id":107,"href":"https:\/\/dziadecki.com\/index.php?rest_route=\/wp\/v2\/pages\/86\/revisions\/107"}],"wp:attachment":[{"href":"https:\/\/dziadecki.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=86"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}