Show Datalena the result. Connect what you have. We’ll build the path.
Describe the output you need, upload a sample result, paste a field list, or connect your existing data. Datalena maps the requirements, combines the sources, identifies what is missing, and builds a governed workflow your team can review and repeat.
Build this monthly performance package from the files we receive from each location.
I found 17 of the 19 required fields. Region can be derived from the location reference. Two calculation rules still need your input.
The path between: evidence and requirements meet, a governed plan is approved, deliverables ship.
- 19 required fields
- 4 calculations
- 3 access scopes
- dashboard + workbook + API dataset
- 2 business questionsunresolved — needs your input
- 1Standardize branch files
- 2Resolve location identifiers
- 3Join CRM performance
- 4Apply monthly calculations
- 5Validate unmatched locations
- 6Create governed dataset
- 7Publish approved outputs
Start with the result, the data, or both.
Describe the output you need, hand Datalena a sample, or connect what you already have. Whichever end you start from, the same governed path meets in the middle.
Start with a sample result
Upload the report, workbook, or feed you need to produce — or just describe it.
Datalena decomposes it into required fields, calculations, and delivery rules, then hunts for the evidence.
Start with your data
Connect the files and exports you already have, messy layouts and all.
Datalena maps what each source can prove and shows which outputs are already within reach.
Start with both
Bring the target and the evidence together.
Datalena works backward from the result and forward from the sources until they meet in one governed plan.
Whichever door you pick, the same governed path meets in the middle.
Everyone solves the ends. Datalena owns the path between.
Most tools help you collect data or display it. Datalena designs and governs the path between what you have and what you need — requirements moving one way, evidence the other, meeting in a plan you approve.
Evidence moves forward
Branch workbooks, exports, references, and adjustments — different formats, different schedules, all kept identifiable.
Matched, derived, and gapped
Datalena works backward from the target and forward from the sources: direct bindings, derivations, transformations — and open gaps held visibly for you.
Requirements move back
The output contract decomposes into fields, calculations, access scopes, and delivery — each one hunting for its evidence.
Five steps. One evolving picture.
Define, connect, build, review, deliver — each step adds a layer to the same composition, and nothing ships without an approved, versioned workflow behind it.
Define — the same composition, one layer further.
- 01Define
Tell Lena the result you need — or hand it a sample. The target becomes an explicit output contract.
Monthly Performance Packageone row per location per month - 02Connect
Point Datalena at the evidence you already have. Every source stays identifiable by type and purpose.
Monthly Performance Packageone row per location per monthBranch workbooksCRM exportLocation referencePrior-period datasetManual adjustments - 03Build
Requirements and evidence meet: direct matches, derivations, transformations — and the gaps, kept visible.
Monthly Performance Packageone row per location per monthBranch workbooksCRM exportLocation referencePrior-period datasetManual adjustments17 of 19 requirements matched2 open questions kept visible - 04Review
Your team inspects readiness, open questions, and held rows. Nothing proceeds on AI say-so alone.
Monthly Performance Packageone row per location per monthBranch workbooksCRM exportLocation referencePrior-period datasetManual adjustments17 of 19 requirements matched2 open questions kept visible98.7% location match31 rows require reviewAwaiting your approval - 05Deliver
After your approval, the workflow runs deterministically and produces every governed output — every cycle.
Monthly Performance Packageone row per location per monthBranch workbooksCRM exportLocation referencePrior-period datasetManual adjustments17 of 19 requirements matched2 open questions kept visible98.7% location match31 rows require reviewApproved — by youSecure dashboardMonthly Excel packagePublished API datasetdeterministic run · workflow v12 · lineage recorded
Many sources. One governed result.
Every source is standardized to one shared structure, combined and reconciled, and validated before anything ships — no source loses its identity along the way.
- StandardizeEvery source is mapped to one shared schema.
- CombineRows are merged and reconciled across sources.
- ValidateRequired fields and lookups are checked before anything ships.
The same mechanism, whatever you deliver.
What arrives, what Datalena builds, what gets delivered — the pattern holds for operations packages, client reporting, and published data products alike.
- What arrivesBranch workbooks, a CRM export, a location reference, and manual adjustments — every month.
- What Datalena buildsA standard location structure with reusable bindings, monthly calculations, and match-rate validation.
- What gets deliveredAn executive dashboard, a location workbook, an exception report, and a published governed dataset.
- What arrivesCustomer exports in varying layouts, shared reporting definitions, and recurring emailed files.
- What Datalena buildsA reusable reporting package with workspace-specific bindings, explicit exceptions, and approval rules.
- What gets deliveredA branded reporting package, a secure customer portal, and approved extracts with release history.
- What arrivesA research workbook, a geography reference, customer-access rules, and quarterly corrections.
- What Datalena buildsA governed national dataset with geography enrichment, version comparison, and customer-scoped access.
- What gets deliveredRegional dashboard access, a downloadable dataset, and a traceable quarterly portal release.
Governed, not guessed.
Datalena sits between rigid data platforms and unreliable chat tools. You collaborate in plain English, but execution is deterministic — repeatable runs, validation, approval, and a full record of how every deliverable was made.
- Reusable mappingsMap a customer's fields once; every future file follows the same rules.
- Validation that blocksRequired fields, formats, and lookups are checked before anything ships.
- Rejected-row resolutionSee which rows failed, why, and how to fix the mapping for good.
- Explicit approvalNothing is produced until you approve the run and its results.
Explain any deliverable.
When something looks off, ask in plain English. Lena answers from the run itself — never a guess.
- “Why did this row get rejected?”Location ID was blank — required by the output contract. Held in run #248.
- “What changed since last quarter?”One branch renamed “Region” to “Territory.” Mapping updated to v4.
- “Which file fed this deliverable?”branch_workbooks_jun.xlsx, approved by you on May 28.
Approved once. Repeatable every cycle.
Every deliverable passes the same gates, in order — source verified, spec approved, run approved, delivered — with versions and lineage kept for every cycle.
Source verified01
Every source is checked and verified before it can feed a mapping or a run.
Spec approved02
You review the mapping and validation rules and approve them before they run.
Run approved03
Nothing is produced until you review and approve this specific run.
Delivered04
The trusted output ships, with a full record of the source, spec, and approval behind it.
A dashboard is just one thing Datalena can deliver.
The same governed run can produce whatever the business or downstream recipient actually needs.
Secure dashboards
Branded, access-controlled views — by customer, role, or geography.
Scheduled feeds
Mapped, validated feeds in the exact layout each recipient requires.
Flat files
CSV and fixed-width files, produced to spec every run.
Excel & CSV extracts
Clean workbooks and extracts your team can hand off directly.
Branded portals
A professional client portal under your name, not ours.
API datasets
Published datasets downstream systems can pull on a schedule.
Audit packages
A record of source, mapping, validation, and approval for every run.
Reconciliation files
Compare cycles, surface what changed, and flag it before it breaks.
Bring the result you need — or the data you have.
Set up one customer workspace, connect a real file, and produce a trusted deliverable in an afternoon.