Two reports, different totals: where to start
Period, granularity and record status change the result. Find the cause of a discrepancy before rebuilding the dashboard.
DATA THAT INFORMS ACTION
Your data.
Clear decisions.
Help your business trust its numbers, find knowledge in documents and apply AI with verifiable results.
Known sourcesAgreed definitionsVerifiable results
CONSULTING AND IMPLEMENTATION OF INTECRACY GROUP SOLUTIONS
A5.ERPScriptum.DMSScriptumDeals
FROM INFORMATION TO UNDERSTANDING
Less reconciliation. More clarity.
A reliable decision needs more than another dashboard. We work with the foundations behind it.
Trust the numbers
Find the cause of a discrepancy before it reaches the report.
See what matters
Connect each metric to the decision it supports.
Find the source
Retrieve knowledge from the current document with a verifiable reference.
Know who decides
Make ownership and access clear to the whole team.
A FAMILIAR SITUATION
One question. Three different answers.
Sales, finance and operations report different totals. The meeting becomes a search for the right spreadsheet.
We examine the period, source and definition. Then agree a metric the team can reproduce.
Build understandable analytics ↗ILLUSTRATIVE RECONCILIATION
SourceWhich records do we count?
PeriodWhen does the event enter the report?
DefinitionWhat does “complete” mean?
One reproducible metric
OUR WORK
Make information work for the business.
01 /02 /03 /04 /05 /06 /
Data quality and consistency
Find duplicates, gaps and conflicting records. Agree the checks that data must pass before reporting.
Data with traceable originsAnalytics for business decisions
Turn business questions into metric definitions, a data model and reports that the team can explain.
From a metric to an informed actionDocument intelligence and AI
Prepare corporate documents for search and AI processing. Answers should cite current sources within access boundaries.
An answer you can verifyData ownership and access
Define who approves meaning, sees data and changes the rules. Keep decisions traceable.
Governance without unnecessary approvalsData pipelines for analytics
Connect business systems, documents and analytical datasets. Control completeness, freshness and change at each transition.
A metric with a traceable pathImplementation and data adoption
Pilots, training and operating rules help teams use analytics after launch.
Tools that become part of the workA MANAGEABLE FIRST STEP
Start small. Verify the whole path.
One business decision sets the scope. Evidence from the pilot determines what comes next.
- 01
Define the decision
Which decision is delayed, and what facts does it require?
Question, scope and owner - 02
Examine the sources
Examine a data sample, its origins and discrepancies.
Quality and rule map - 03
Build and validate
Configure one scenario and test ordinary and difficult cases.
Pilot with acceptance criteria - 04
Make it operational
Hand over operating rules, training and improvement ownership.
Operating routine and next steps
INTECRACY GROUP
The right tools. A shared context.
Configure products around your data and decisions. Verify feature and integration availability before choosing the scope.
BUSINESS DATA
A5.ERP
Business records behind analytics
A5 Solutions subsystems for accounting, purchasing, contracts and HR. Align reference data, data scope and analytical dimensions.
About the solutionDOCUMENT KNOWLEDGE
Scriptum.DMS
Documents that can answer a question
Document management and AI-assisted processing. Define metadata, versions, access and result checks against your corpus.
About the solutionDATA WORKFLOWS
Scriptum
Data rules within a workflow
Forms, registers and routes for collecting, checking and approving information. Configure exception-resolution workflows.
About the solutionSIGNED EVIDENCE
Deals
Signed documents as evidence
Electronic exchange and signing. Define which document and status data your registers need.
About the solutionUnityBase is the enterprise platform layer. Product scope, modules and licenses are agreed after discovery.
WORKING WITH DATA
Read. Question. Make an informed choice.
Similar customer records: who decides to merge
A similar name is a review signal. Examine authority, evidence and the consequences of changing a master record.
The document was extracted. What still needs checking?
Text in a field is not yet a verified business record. Design AI extraction acceptance around error consequences.
BEFORE THE FIRST MEETING
Good questions. Clear answers.
Do we need a new data warehouse?
Not always. First review sources, volume and required decisions. An agreed dataset and checks within an existing system may support the first scenario.
Can we start with one report?
Yes. Choose a management question, define metrics and validate the path from source record to total. This gives the pilot testable boundaries.
When is AI appropriate?
When sources, versions and access are understood and an evaluation set exists. Define separately when a person must check the answer.
How do you define success?
Agree acceptance checks before work starts: dataset completeness, reproducible metrics, access and exception handling. Estimate expected economic effects using the client’s data.