Investigate an exception
Bring related data together. Identify what changed. Show the evidence to the process owner.
Cross-Functional Solution
Turn Business Data Into Governed Decisions And Action
Mimasa brings decision intelligence to the flow of work. Ask questions of approved business data, review the evidence, then move the right decision into a controlled workflow.
Connected data | Conversational analysis | Decision support | Human approval | Governed action
Definition
What Is Data And Decision Intelligence?
Data intelligence helps teams understand trusted business data. Decision intelligence connects that understanding with choices, controls and action.
Mimasa combines data analysis and decision making in one governed flow. It adds AI support around your existing systems.
This supports data-driven decision making. It does not turn every decision over to AI.
This Approach Can Help Teams
- Ask questions in plain language.
- Find trends, exceptions and possible causes.
- Compare options with visible assumptions.
- Review source evidence before acting.
- Route approved decisions into business workflows.
Business decision intelligence keeps artificial intelligence decision making inside clear limits.
Six Steps
One Connected Path From Data To Action
Analysis often happens in one place and follow-up work somewhere else. Mimasa connects these steps without replacing the systems that run your business.
- Step 1
Connect
In: Approved business and analytics sources
Out: A governed data scope with permissions kept
- Step 2
Ask
In: A business question in everyday language
Out: A question linked to the approved data
- Step 3
Analyse
In: Trends, changes, exceptions and comparisons
Out: A finding with data and assumptions shown
- Step 4
Review
In: The evidence and the business context
Out: A confirmed, refined or rejected finding
- Step 5
Act
In: An approved decision
Out: A task, alert or permitted system action
- Step 6
Monitor
In: Decision, owner and action status
Out: A record of what was decided and done
Connected Path
From Question To Monitored Outcome
Business data often sits across operational systems, reports and files. Each step below keeps the source, the owner and the control in view.
- 01
Connect approved data
Use governed access to relevant business data. Preserve source context and permissions.
- 02
Ask a business question
Teams use everyday language. The question stays linked to the approved data scope.
- 03
Analyse the evidence
Mimasa can support automated data analysis across trends, changes and exceptions. Results show the data and assumptions used.
- 04
Review the finding
An authorised person checks the evidence. They can refine the question or request more detail.
- 05
Approve the next step
The system creates an alert, task or approval request. Consequential action remains controlled.
- 06
Monitor the outcome
Track the decision, owner, action status and supporting record.
Conversational Analytics
Ask Questions In Plain Language
Conversational analytics makes business data easier to explore. Users can ask a question without starting from a blank report.
Depending on the configured source, Mimasa can support natural language analytics and natural language to SQL. Generated queries must respect data access rules.
This is conversational business intelligence with workflow context. For dashboard-led exploration, self-service analytics and text to SQL, see Visualization and Dashboards.
For a logistics example of the same approach, see freight and transportation analytics, where connected shipment, carrier and cost data supports governed investigation. When one operational change affects several functions, the supply chain control tower use case shows how impact analysis, prioritization and approvals stay connected.
In education, the same pattern supports business intelligence in higher education, where programme, enrolment, finance and placement data inform governed institutional decisions. At student level, student retention and academic risk management turns approved signals into explainable cases and human-led interventions.
In banking, teams can bring governed analytics into credit underwriting decisions. Source-linked evidence, policy exceptions, and human authority remain visible throughout the review.
Fraud teams can also connect fraud alerts, evidence and decisions with banking fraud analytics. Inferred links remain review points, not confirmed facts.
Reporting teams can connect regulatory data, evidence and sign-offs through banking reporting automation. Approved systems and authorised reviewers retain control.
Treasury teams can connect cash flow analysis and treasury forecasting with source context, assumptions, scenarios, and authorised review.
Investment teams can connect investment research and portfolio intelligence with approved evidence, monitoring, committee briefs, and human judgement.
Wealth firms can use wealth management intelligence for adviser preparation, portfolio-reporting workflows, and approved client communication.
Audit and control teams can connect audit evidence, configured tests, exceptions and remediation while authorised professionals retain control of conclusions.
Example questions
- Which orders are at risk of delay?
- Why did this cost category change?
- Which exceptions need review today?
- How does one scenario compare with another?
- What evidence supports this recommendation?
Augmented Analytics
Augmented Analytics With Business Context
Augmented analytics uses AI to support data preparation, exploration and explanation. It helps people reach a useful question faster.
Mimasa applies AI augmented analytics within a defined business process. The goal is a clear, reviewable next step, not another isolated chart.
An AI data analyst can assist the team. It does not replace accountable analysts or process owners.
- Trend and variance summaries.
- Exception and anomaly investigation.
- Comparison across approved groups or periods.
- Draft explanations for review.
- Automated business reporting.
- Reports, presentations and spreadsheet outputs.
Use Cases01 02 03 04 05 06 07 08
Use Cases Across Business Functions
Enterprise data analytics supports many teams. The question changes, but the need for evidence and control stays the same.
Compare business scenarios
Test defined assumptions and compare possible outcomes. Keep estimates separate from known facts.
Prepare recurring reports
Create governed summaries for teams and leaders. Review them before distribution.
Prioritise work
Use rules and approved signals to rank cases. Route the final queue to an authorised owner.
Support a cross-functional decision
Bring finance, sales, operations or supply data into one review. Keep access limited to each user's role.
Move insight into action
Create a task, alert or approval from the finding. Execute only the approved step.
Review private-equity evidence
Connect approved deal evidence, source-linked findings, review workflows and portfolio monitoring.
Explore Review private-equity evidence →Prioritise insurance renewals
Connect approved renewal dates, readiness checks, service signals and governed follow-up work.
Explore Prioritise insurance renewals →Banking Example
Explore Banking Early-Warning MonitoringConnect Portfolio Signals With Governed Review
See how credit portfolio monitoring for banks links approved analytics, source evidence, alerts, and authorised human decisions.
Process Intelligence
Process Intelligence For Better Decisions
Process intelligence shows how work moves through a business process. Process analytics can reveal delays, repeated exceptions and hand-off gaps.
Mimasa can combine business process intelligence with approved operational data. Teams can investigate a problem and route a response.
It works around existing process intelligence software and process intelligence tools. It adds controlled AI assistance and workflow action where configured.
Business process analytics can support
- Tracking cycle times and waiting points.
- Finding recurring exceptions.
- Comparing expected and observed steps.
- Identifying cases that need review.
- Measuring the status of approved actions.
Operational intelligence brings signals from several systems into a shared decision context. It remains subject to source refresh times, so treat timing as configured, not real time.
Human Control
Decision Automation With Human Control
Decision automation should match the risk of the decision. A low-risk routing step may be automated. A consequential decision should require an authorised reviewer.
| Decision Stage | AI Support | Human Control |
|---|---|---|
| Detect | Find a defined exception or change | Confirm the signal is relevant |
| Explain | Summarise evidence and possible factors | Check data and business context |
| Recommend | Compare configured options | Select, change or reject the option |
| Approve | Route the request to the right role | Authorise consequential action |
| Execute | Trigger the approved workflow step | Retain permissions and override rights |
| Monitor | Track status and new exceptions | Review outcomes and adjust rules |
Intelligent decision automation moves an approved choice into action. AI decision automation must not hide uncertainty, and human in the loop decision making keeps accountability clear.
AI Agents
AI Agents For Decision Workflows
AI agents can gather approved context, run defined checks and prepare a decision brief. They can also create the next task after approval.
Agentic decision making does not mean unrestricted autonomy. Each agent needs a defined role, data scope, tool access and approval boundary.
- Evidence collection.
- Exception investigation.
- Scenario preparation.
- Decision-brief drafting.
- Approval routing.
- Approved workflow execution.
Governance
Decision Governance You Can Inspect
Decision governance defines who can see evidence, change assumptions, approve a choice and execute an action.
Mimasa can support evidence based decision making with visible sources and review steps. The workflow retains the question, analysis, decision and action record.
- Role-based access.
- Approved data scopes.
- Visible assumptions and source context.
- Human approval gates.
- Action permissions.
- Review and execution records.
- Exception escalation.
AI for decision making should help people judge evidence. It should not disguise a guess as a fact.
Around Your Systems
Keep Your Existing Data And Analytics Systems
Mimasa is not a replacement for your analytics stack. It adds a decision and action layer around approved systems.
| Existing Capability | Its Role | Mimasa's Role |
|---|---|---|
| Data warehouse or lake | Stores and organises enterprise data | Uses approved data for defined questions |
| BI and dashboards | Displays measures and supports exploration | Adds decision context, review and workflow action |
| ERP, CRM or operational system | Runs transactions and keeps records | Reads approved context and writes approved actions |
| Data-governance platform | Manages policies, quality and lineage | Applies configured access and preserves workflow evidence |
| Process-intelligence platform | Analyses process events and flow | Helps teams investigate and act on selected findings |
A decision intelligence platform should connect insight with action. It should also preserve the controls of each source system.
Adoption
Start With One Decision That Matters
Begin with a clear question and a known owner. Define the evidence, review step and permitted action before adding automation.
Discuss Your Decision Workflow- Step 1
Discover
Map the decision, data sources, assumptions and current follow-up process.
- Step 2
Configure
Connect approved data. Define questions, checks, roles and approval rules.
- Step 3
Validate
Test outputs with real cases. Review errors, uncertainty and edge cases.
- Step 4
Expand
Add more questions or workflows after the controls work as intended.
Connected Platform
Related Platform Capabilities
Related Functions
Related Business Functions
Frequently Asked Questions
Common questions about decision intelligence, augmented analytics, process intelligence and human control.
Get Started
Move From Business Questions To Governed Action
See how Mimasa can connect approved data, decision support and human-controlled workflows around your existing systems.
See the operating model behind connected data, AI agents, governed workflows and human approval.
See How Mimasa Works →