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.
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.
See what changes: Months before a project can start -
Access that arrives late
Data approvals drag on, so interns, new joiners and project teams sit waiting for the data they need.
See what changes: Access that arrives late -
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.
See what changes: Tools chosen by old systems -
Knowledge that leaves with people
When a senior person changes project or leaves, the handover note holds only part of what they knew.
See what changes: Knowledge that leaves with people
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 × people doing the work × loaded hourly rate = the weekly cost of searching, waiting and redoing work
| Line | Value |
|---|---|
| Hours per person per week | 5.3 hours |
| People doing the work | 40 |
| Loaded hourly rate | S$60 |
| Weekly cost | 5.3 × 40 = 212 hours; 212 × S$60 = S$12,720 |
| A year, at 52 weeks | 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.
Enter a number from 0 to 60.
Count only the people who do this kind of work.
Enter a number from 1 to 100,000.
Salary plus overheads, per hour
Enter a number from 1 to 10,000.
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.
Plan a pilot
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.