Use cases: where teams lose hours, and what changes
Five problems that slow teams down, what DataBridge changes in each, and a way to work out what the lost time costs you.
Five problems that slow teams down, what DataBridge changes in each, and a way to work out what the lost time costs you.
It starts with a demo on a sample of your own documents and data.
Teams told us where the hours go
The biggest obstacle to using advanced data analysis isn't skill base or technology; it's plain old access to the data.
Months before a project can start
Each new data or AI project can spend months gathering data from scattered systems. The next project starts the hunt again.
Access that arrives late
Data approvals drag on, so interns, new joiners and project teams sit waiting for the data they need.
Tools chosen by old systems
When your data sits inside one supplier's system, your teams stay on that supplier's tools, even when better ones are available.
Knowledge that leaves with people
When a senior person changes project or leaves, the handover note holds only part of what they knew.
The hold-ups teams describe to us.
Five problems, and what changes
Projects start sooner
- Today
- Every new data or AI project begins with months of data gathering.
- With DataBridge
- Sources connect once, and the next project starts from the same record
Access set once, by role
- Today
- Access requests hold up new joiners and project teams.
- With DataBridge
- Access follows each person's role, set once by your administrators
Freedom to change tools
- Today
- Old systems decide which tools teams can use.
- With DataBridge
- Data is read where it sits and fed to the tools you choose
Knowledge that stays
- Today
- Know-how leaves when people move on.
- With DataBridge
- Decisions, corrections and context stay in the record, with their evidence
Hours back every week
- Today
- Routine emails and reports are put together by hand.
- With DataBridge
- Ask in one sentence, and a draft waits for a person to check and send
What the lost time costs, and how to work it out
- Hours per person per week
- Value
- 5.3 hours
- People doing the work
- Value
- 40
- Loaded hourly rate
- Value
- S$60
- Weekly cost
- Value
- 5.3 × 40 = 212 hours; 212 × S$60 = S$12,720
- A year, at 52 weeks
- Value
- S$661,440 a year, about S$661,000
Take the 5.3 hours a week that Panopto with YouGov found in 2018, in a survey of 1,001 US employees at organisations of 200 people or more, and 40 people at S$60 an hour. That is 5.3 × 40 = 212 hours, and 212 × S$60 = S$12,720 a week, or about S$661,000 a year.
It is the cost of the problem today. How much of it you get back is measured in the pilot, against the baseline you set.
Customer conversations, renewals and delivery
- Prepared customer conversations
- Account history, open issues and past agreements in one view before every call.
- Renewals and follow-ups on time
- Contract dates and last contact surface the next action for each client.
- Faster delivery to clients
- New work starts as soon as the client agrees, with the data already in place.
Where it fits
- Financial services
- Finance teams work across ledgers, bank files, contracts and email; DataBridge links them into one record where each figure read from a file opens to its source.
- Insurers
- Policy, claims and correspondence records about one policyholder are linked into one record, and every version is kept.
- Growing businesses
- Each customer's invoices, payments, agreements and email sit in one record, and follow-ups arrive as drafts to approve.
Pick one task your team does every week, and we will scope a pilot on it.
It starts with a demo on a sample of your own documents and data.