Qualifying Questions for Dawiso Prospects

More in depth:

1. How many people in your company work with data (analysts, engineers, governance teams, etc.)?

• A) dedicated data team => continue to 2. question

• B) ad-hoc across departments => possible to go to 4. Question OR:
  
  B) When data is managed across multiple teams without a central governance function, we often hear challenges like inconsistent definitions, difficulty finding the right data, or issues with KPI accuracy. Does that sound familiar?

1. Struggling with inconsistent definitions:
   → “One way to solve this is through a Business Glossary, where all departments align on shared definitions. Dawiso makes this easy by linking definitions to actual data sources.”

2. Finding data is difficult across teams:
   → “Many companies in your situation benefit from treating their key datasets as Data Products—clearly defined, reusable, and well-documented. Dawiso’s Data Products Catalog makes this possible without extra workload.”

3. Concerned about compliance but lack governance processes:
   → “Even without a formal governance team, compliance shouldn’t be a burden. Dawiso helps by automatically tracking data lineage and ensuring transparency in how data is used, which simplifies audits.”

2. A) Which tools, databases, or platforms do you currently use for storing, analyzing, and managing data?
   – connectors and integrations – we have a wide list of connectors. Plus in pricing - with no limits on connections or tables.
   B) What tools do you currently use for managing metadata, documentation, or lineage (e.g., Excel, Confluence, Collibra, homegrown solutions)?

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Continue:

1. Using Excel/Confluence/manual documentation:
   → “Many teams start with spreadsheets and wikis, but as data grows, these methods become hard to scale. Dawiso helps by automating documentation, ensuring consistency, and making lineage transparent.”

2. Using a competitor like Collibra but struggling with adoption or cost:
   → “We often hear that traditional governance tools can be complex and expensive to maintain. Dawiso is designed to be more accessible, helping teams implement governance without excessive overhead.”

3. Looking for better collaboration between IT and business users:
   → “Even with a dedicated team, governance is most effective when business users also engage. Dawiso makes it easy for both technical and non-technical teams to collaborate, ensuring that data governance isn’t just an IT initiative. We have a business-friendly design plus a lot features helping business people with their work (possible to mention some AI features like Help me Write, Summary, or mention Slackbot search…”).

3. Is your company in a regulated industry?

• (finance, healthcare, insurance, energy, etc.)

• (GDPR, AI Act, DORA, BCBS 239, etc.)?  

4. What are your main challenges when working with data? (Let them answer first, then dig deeper.) – This is probably the main question…

1. Can your teams easily find the data they need? (If no, Dawiso’s cataloging helps.)

2. Do you trust the origin and accuracy of your reports and KPIs? (If no, this points to lineage and governance issues.)

3. Are you facing issues with inconsistent definitions across teams? (If yes, Dawiso’s Business Glossary can help.)

4. How much time do teams spend manually documenting, verifying, or reconciling data? (If it's excessive, Dawiso’s automation adds value.)

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When to Bring Up Data Products:

1. If they lack centralization → “It sounds like different teams manage data independently. Have you considered structuring your key datasets as reusable data products? This can help standardize definitions and make data easily accessible while maintaining ownership.”

2. If they struggle with inconsistent data definitions → “A common issue with decentralized data management is that every team defines metrics differently. A Data Products Catalog can help ensure that everyone uses the same trusted data assets without duplication.”

3. If they are trying to improve self-service analytics → “Instead of every department working in silos, structuring data as data products allows teams to find, trust, and use data more efficiently, which speeds up decision-making.”

4. If they are manually managing access & documentation → “When teams work ad-hoc, granting access to data or understanding its history can be difficult. By organizing data into data products, you get clear documentation, ownership, and access rules in one place.”

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When NOT to Mention Data Products:

• If the client has a low data maturity level and isn’t aware of governance concepts. Instead, you might frame it around finding and trusting data before introducing structured approaches.

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When to Talk About AI Governance

• If the company actively uses AI or ML models and needs better data lineage, trust, or compliance.

• If they are in a regulated industry with upcoming AI compliance concerns.

• If they mention AI-related risks like data bias, transparency, or decision traceability.

We can also talk about AI features, data modeling, semantics layer, and consistent KPIs… based on the person's position.  

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