Data readiness assessment
Pillar 1 of 5
0 of 20 answered
Master Data Accuracy
Whether products, customers, price lists and cost tables are accurate enough to price from.
Master data is the foundation of every pricing decision. A two-percent error rate in cost can erase the margin a whole optimization program was bought to find. Accuracy here is not a data-team metric — it is whether the number on the quote is the number the P&L will see.
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Transaction Data Completeness
Whether each booked deal carries the fields a waterfall needs.
Optimization needs the story behind the invoice: list, on-invoice discounts, rebates, freight, terms and cost-to-serve. Most companies capture the price. Few capture why that price happened. Without those fields, pocket margin is a reconstruction project, not a report.
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Data Timeliness & Freshness
Whether cost, inventory and market inputs are current when a quote is opened.
If cost is six weeks old when you quote, you are not pricing — you are guessing. In a volatile input market the lag is the leakage. Freshness is a property of the feed, not of the last project extract.
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Data Consistency Across Systems
Whether ERP, CRM, finance and the pricing tool agree on the same object.
When the ERP says one thing, the CRM another, and finance’s spreadsheet a third, every analysis starts with an argument about which number is right. A pricing platform will not resolve that. It will inherit it.
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System Integration Maturity
Whether pricing data moves between ERP, CRM, CPQ, rebates, commerce and BI on a feed, or by export and re-keying.
Pricing data typically lives across five to ten systems. Integration is the plumbing. Underestimating it is how implementations double in length — not because the pricing tool is hard, but because the extract is.
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Data Warehouse & Analytical Infrastructure
Whether pricing history lives in a queryable layer, or only in the ERP.
You cannot run pricing analytics on the transactional system and also expect the transactional system to stay fast. A warehouse or lake with pricing-shaped models is what turns “we have the data” into “someone can ask a question before the quarter ends.”
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ERP & Core System Readiness
Whether the ERP can execute the pricing you intend to design.
The ERP is where price becomes an invoice. Its age, configuration and data model decide which strategies are real. A modern pricing tool on a ten-year-old core does not remove that constraint — it documents it.
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External Data Accessibility
Whether competitive, index and input-cost signals reach the price decision.
Pricing does not live only inside the four walls. Commodity indices, competitive prices and input costs are what keep a list from being last year’s list. If those signals arrive as a forwarded email, they are not in the model.
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Statistical & ML Readiness
What analytical horsepower actually sits behind pricing decisions.
Elasticity, demand and segmentation are not dashboard features. They are models, with owners, refresh and a place they are allowed to change a price. Buying an “AI pricing” module before this exists is how software becomes a slide.
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Price Elasticity Measurement Capability
Whether anyone has measured how demand responds to price, by segment.
Without elasticity you are optimizing in the dark. “Customers are very price-sensitive” is not a measurement. Most B2B companies have never estimated elasticity systematically — and still debate discount as if they had.
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Historical Data Depth & Quality
Whether you have years of clean, consistently coded transactions, or a series a migration broke.
Models train on history. Pricing analytics typically wants two to three years at the grain of the deal, with stable product and customer codes and a win/loss flag. A migration that reset identifiers makes the years before it decorative.
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Visualization & Self-Service BI
Whether pricing, sales and finance can see margin without an analyst.
Insights that live in one analyst’s workbook do not change a deal. Self-service dashboards — waterfall, compliance, segment — are what shorten the time from “something leaked” to “someone did something.”
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Data Governance Framework
Who owns pricing data, who can change it, and what “good” is.
Governance is the difference between a clean extract and a clean extract that is still clean in eighteen months. Without owners, standards and a place disputes go, quality decays — and with it, every number the platform prints.
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Pricing Data Security & Access Control
Who can see or export price lists, costs and guidelines.
Pricing data is among the most sensitive commercial information you hold. Shared drives, emailed workbooks and “everyone in sales has the folder” are not an access model. They are how a list becomes a competitor’s input.
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Regulatory & Compliance Readiness
Whether pricing systems can show why a price was legal to offer.
Price intersects antitrust, trade rules, export controls and contract. The system has to support an audit trail, not a reconstruction from email. In some books this is optional hygiene. In others it is the reason the programme exists.
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Change Management & Version Control
Whether last month’s price list and the reason it changed still exist.
When a rule changes, who records it? When a list is updated, is the previous version kept? Version control is how you resolve a dispute, reverse a bad publish, and show an auditor something other than a shrug.
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Team Skills & Data Literacy
Who on the pricing side can read an extract and change a decision.
Perfect data and a capable platform still fail if nobody on the commercial team can interpret a waterfall or challenge a model. Literacy across pricing, sales and finance is what turns a scorecard into a habit.
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Process Documentation & Standardization
Whether the pricing process can be shown to a new hire without an apprenticeship.
Most companies cannot describe their pricing process end to end. Undocumented work means the knowledge leaves with the person, and improvement has no baseline. A platform will automate whatever you write down — or whatever you forget to.
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Technology Adoption Readiness
What usually happens here after a new commercial tool is switched on.
Pricing technology only pays if people use it. Organizations with a graveyard of unused licenses do not have a software problem. They have an adoption problem — and the next tool will join the pile unless that is named first.
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Executive Data-Driven Decision Support
Whether leadership opens the number or the anecdote when they set price.
Availability of data is not the same as use of data. If executives price from gut and relationships while a dashboard exists unused, the organization is not data-ready. It is data-equipped. A programme that cannot survive an override without a recorded reason will not change commercial behaviour.
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Not a shopping list. For each gap: what to put in place, which category of tool the market uses, and what to have before anyone buys another module.
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Each step names the level you are at and the one immediately above it. One rung at a time is what actually gets funded.
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This scores the data a platform would run on. The Challenges Diagnostic names the leak. Pricing Readiness scores the operating model. Pricing Challenges Diagnostic →
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