What is your company's data worth?
Answer 9 quick questions to get an estimate of what your data assets are worth, how much money they lose you every year, and your next three steps toward AI.
- No credit card, no commitment
- Every number explained with a source
- Calculated in your browser
Question 1 of 9
What industry are you in?
Question 2 of 9
What is your annual revenue?
Question 3 of 9
How many contacts and leads are in your CRM?
All records: customers, leads, suppliers and contacts
Question 4 of 9
How many active customers do you have?
Who bought from you in the last year
Question 5 of 9
How many systems does your data live in?
CRM, ERP, accounting, spreadsheets, website, operations tools
Question 6 of 9
How many hours a week does your team spend on manual data work?
Across the team: copy-paste, Excel reports, fixing records
Question 7 of 9
How often do you clean and update your data?
Question 8 of 9
How are decisions made in your company?
Question 9 of 9
Do you store sensitive personal data?
ID numbers, health or financial information
Your results are ready
Leave your details to see the full report now:
- The value broken down by type of data
- How much money you lose every year, and on what
- Your AI readiness score
- Your next three steps
Your data balance sheet
Assets
- Customer and contact recordsWhat they would cost to replace
- Data-driven decisionsEconomic value over 2-4 years
Annual loss
- Manual work AI could take over
- Outdated records
- Incidents from disconnected systems
Capped at 15% of revenue, the low end of Redman's research.
Opportunity
- Untapped AI and analytics upside, per year
Risk
- Cost of a data breach
Based on IBM, up to the industry average and to 10% of annual revenue.
AI readiness
0/100
An estimate, not an accounting valuation. Where a line shows one amount, it is the middle of its range.
We have your details. One of our data and AI engineers will get back to you within one business day to go through the results with you.
Your next three steps
How we calculate
Most calculators show one number without explaining it. We show the whole calculation. It is built on Doug Laney's information valuation models (Infonomics, Gartner) and published benchmarks. Anything that is our own assumption is marked as one.
| Line | Calculation | Source |
|---|---|---|
| Customer and contact records | Up-to-date contacts (less the 30%, 15%, 6% or 3% that are out of date, by cleaning cadence) × $0.25 to $3, plus active customers × cost per lead × 1 to 3. Smaller companies pay less per lead: $146 under $5M revenue, $180 under $20M. Consumer records are scaled down by the industry cost per lead over $188. Gradually capped at one year of revenue. | Laney, Cost Value of Information; B2B record prices (Datarade); cost per lead by industry and company size, Sopro 2025. The consumer scaling: our assumption. |
| Data-driven decisions | Revenue × industry operating margin × share of profit that comes from data (2%, 5%, 10%, 20% by decision level) × 2 to 4 years. | Laney, Economic Value of Information; McKinsey 2019 (leaders credit data with 20% of EBIT); Damodaran margins, 2026, adjusted down for smaller companies (our adjustment). The steps below 20%: our assumption. |
| Manual work | Hours a week × 46 weeks × hourly cost × 40% that can be automated. Hourly cost: $45 in dollars, €40 in euros, ₪105 in shekels. | ₪105: Israel's average wage (CBS 2026, ₪14,409 a month) with employer costs. $45, €40 and the 40%: our assumption, between the 35% and 45% automation vendors report. |
| Outdated records | Contacts × 22.5% that go out of date each year × cost per record by cleaning cadence: continuous $0.5 to $1, quarterly $1 to $3, yearly $3 to $10, never $10 to $30. Consumer records are scaled down as in the assets line. | 22.5% yearly decay (MarketingSherpa); the 1-10-100 rule of Labovitz and Chang ($1 to prevent, $10 to correct, $100 on failure). The levels: our assumption. |
| Disconnected systems | Revenue per hour × 10% dependency on data × systems × 4 incidents a year × 19 hours each × share of systems not connected: 100% at levels 1-2, 50% with central dashboards, 25% with automation. | Monte Carlo survey, 2023 (15 hours to resolve and about 4 to detect; 10% is the middle dependency in their calculator). 4 incidents per system and the connected shares: our assumption. |
| Loss cap | Total annual loss never exceeds 15% of revenue. When the cap applies, the lines shrink in proportion. | Redman, MIT Sloan Management Review 2017: poor data quality costs 15%-25% of revenue. |
| Untapped upside | Revenue × 5% to 15% × distance from the top level (100%, 66%, 33%, 10%) × 25% that is realistic to capture. | McKinsey (5%-15% revenue lift from personalization). The 25%: our assumption. |
| Breach cost | Records (contacts include customers) × $160, plus 20% with sensitive data. Up to the industry average and to 10% of annual revenue. | IBM Cost of a Data Breach: $160 a record from the 2025 report, industry averages from 2026. The 20% for sensitive data: our assumption. The 10%-of-revenue cap: our assumption, as IBM's averages come from large organizations. |
| AI readiness | Decision level (40 points), cleaning (30), number of systems (15), manual work (15). Capped by decision level at 40, 65, 85 or 100. | Our own index. |
| Industry figures | Margin, cost per lead and average breach: services 8%, $501, $4.99M. Software 15%, $188, $5.5M. Finance 18%, $461, $6.29M. Healthcare 13%, $150, $6.64M. Ecommerce 7%, $40, $4.99M. Manufacturing 12%, $391, $5.5M. Other 10%, $188, $4.99M. | Damodaran, Sopro and IBM as above. Healthcare and ecommerce cost per lead, and the "other" margin and cost per lead: our assumption. |
Each answer range is calculated at its midpoint. Exchange rates: $1 = ₪3.6, $1 = €0.9.
Questions
How accurate is the estimate?
It is a conservative estimate, not a valuation. That is why you get a range rather than one number, and why the annual loss is capped at the low end of the research. A precise number needs a look at the data itself, which is what the free data review is for.
What happens to my answers?
The calculation runs in your browser. When you leave your details to see the full results, your answers and results reach us with them, so one of our engineers can go through them with you. We don't pass them on to anyone.
If data is worth money, why isn't it on the balance sheet?
Accounting standards (IAS 38) don't allow internally built customer lists to be recorded as assets. Even so, according to Ocean Tomo more than 90% of S&P 500 company value comes from intangible assets, and data is a large part of them.
What does data value have to do with AI?
AI models are only as good as the data they work with. Clean, connected, current data is what turns an AI tool into real hours saved and better decisions. Scattered, outdated data turns it into one more tool nobody trusts.
What happens after I leave my details?
One of our data and AI engineers gets back to you within one business day for a short free call: we go through your results, learn which systems you run and what shape your data is in, and suggest 2-3 AI use cases with clear ROI. No commitment.
