How to Use the DataHub Cloud Value Estimator

Use this business value estimator to build a credible business case, grounded in third-party research, for what DataHub Cloud can deliver for your organization.

As data teams take on more — more pipelines, more AI projects, more compliance obligations — the question of what it all costs becomes harder to answer. But the harder question is often the opposite one: what does not having the right data infrastructure actually cost?

Slow data discovery, failed AI projects, compliance scrambles, and data downtime all carry real price tags. They just rarely appear as line items in a budget. This estimator surfaces that hidden value. 

Independent research from IDC validates the scale of that impact. In its Solution Brief “The Business Value of DataHub Cloud” (March 2026), IDC studied five enterprise DataHub Cloud customers and found that data searches that previously took 50 minutes dropped to 5 minutes. Teams shipped 119% more AI models to production. Data outages fell by 48%, and when incidents did occur, teams resolved them 58% faster. These are the benchmarks behind the numbers in this estimator.

How to use the estimator

Step 1: Your team

Start by filling in your team structure. The estimator uses four headcount categories that reflect how data work is actually organized: data consumers, data engineers, analytics staff, and governance and compliance FTEs. Each category drives different value calculations, so it is worth taking a moment to enter accurate numbers rather than rough approximations.

If you are unsure about a specific number, use the tooltip next to each field. Each one explains exactly what the input covers and how to avoid double-counting across categories.

Step 2: Your data environment

These inputs capture how your team works with data today — how often people search for data, how long it takes, and what data downtime and storage cost your organization annually.

Search time defaults to 50 minutes per search. IDC measured this directly across five enterprise DataHub Cloud customers. With DataHub, the same search takes an average of 5 minutes. This single improvement, multiplied across your data consumers, often becomes the largest driver in the productivity calculation.

Data downtime cost is pre-filled at $7M annually. Using Gartner’s estimate of approximately $5,600 per minute of downtime, annual impact varies based on outage frequency and duration. Applying conservative assumptions, this equates to approximately $7M in annual cost exposure. If your organization has experienced significant data incidents, the actual cost is likely higher.

All values are pre-filled with conservative defaults. You are not expected to accept them as-is — adjust any value that you know to be different for your organization. The estimator is only as credible as the inputs behind it.

Step 3: AI / ML program

Enter the number of active AI or ML projects your organization is currently running – at any stage of development, testing, or production.

AI project failure is estimated at $300K per failed project — a midpoint based on typical sunk development costs and opportunity cost for mid-market organizations. DataHub customers experience 24% fewer AI project failures, according to IDC research [add hyperlink to the report]. More broadly, MIT Sloan Management Review (State of AI in Business, 2025) reports only ~5% of enterprise GenAI pilots reach production and extract real value.

Step 4: Compliance risk

Enter how many compliance obligations your organization manages and your estimated financial exposure per breach or violation.

Compliance breach impact is pre-filled at $1.5M per incident. Enforcement data shows GDPR fines average $2.5M–$3M per case, with significantly higher penalties in extreme cases. To remain conservative, the estimator models breach impact at $1.5M, accounting for regulatory fines, legal costs, remediation, and business disruption. If your compliance exposure is higher, adjust accordingly.

Step 5: Review your results

The right side of the estimator updates in real time as you adjust inputs. You will see two primary outputs: total annual value and team capacity gained. Below those, the value is broken down across five drivers — productivity, AI operationalization, data quality, governance and compliance, and infrastructure.

Each driver has an info icon that explains both what it measures and how the number was calculated. These explanations are designed to be shared with a manager, a finance partner, or anyone who will ask “where does this number come from?”

Once you are satisfied with your estimate, enter your email to receive a detailed value report. It includes a full breakdown of every driver — the exact formulas used, the benchmarks behind each number, and the subcategory calculations that add up to your total. It is designed to be shared with stakeholders who want to understand the methodology, not just the headline figure.