What is Data, Analytics and Applied AI?
Data becomes valuable when it changes a decision. Data, Analytics and Applied AI builds the foundation for that to happen: data architecture and governance, predictive models and agents that reach production.
We prioritize use cases by value and feasibility, organize the data each case needs and build models and agents together with the teams that will use them.
Each case is measured by its effect on the business, and the data platform grows case by case instead of waiting for a large project to solve everything.
The problems clients bring us
Do we have lots of data and few answers?
Information spread across systems, with no reliable source for decision-making.
Does nobody trust the numbers?
Each area has its own version of the indicators and the data is debated before the decision.
Do models stay in the lab?
Analytics tests that are not integrated into processes or maintained.
Which AI cases are worth pursuing?
Plenty of interest in AI and little clarity about where it generates real value.
Are we compliant with data protection rules?
Personal data without clear policies on access, consent and use.
Do we have the team to sustain it?
Dependence on a few people or on vendors for any analysis.
Our approach, applied to Data, Analytics and Applied AI
The same four phases of «Our approach», with what we do and what we deliver in each one.
The path of a data and AI program
- 01Source inventorySystems, data quality and the decisions that benefit most.
- 02Prioritized casesBy domain, value and feasibility.
- 03Data and minimum architectureWhat each case needs, with its governance rules.
- 04Models and agentsBuilt with the users, with the business case for each one.
- 05Production and monitoringCases integrated into processes, with value metrics.
We use a problem-solving structure to frame hypotheses.
We take stock of sources and systems, assess data quality and gather, with each area, the decisions that would benefit most from analytics and AI.
DeliverableData and use case diagnosisWe break the problem down into a MECE structure.
Cases are organized by domain and prioritized by value and feasibility, together with the data and minimum architecture each one needs.
DeliverablePortfolio of prioritized casesWe build the business cases and the structure of each initiative.
We build the first models and agents with the users, with the business case for each and the data governance rules they require.
DeliverablePredictive modelsWe provide support with a dedicated, full-time on-site team within the organization.
Cases move into production integrated into processes, with monitoring, value metrics and a client team able to sustain them.
DeliverableAgents and copilots
What does the diagnosis review?
The data value chain
- 01DataRecords from systems, channels and sensors.
- 02InformationClean, integrated data with common definitions.
- 03InsightPatterns and predictions that explain what is happening.
- 04DecisionA different action in the process, measured by its effect.
- Use cases and valueDecisions that would improve with data, and their impact.
- Sources and qualityOrigin, completeness and reliability of the data.
- Data architecturePlatform, integration and access to data.
- Governance and privacyData owners, definitions, security and personal data protection.
- Models and analyticsPredictive capabilities, generative AI and how they are operated.
- Talent and adoptionTeams, roles and the use of data in decisions.
How are data and AI organized?
AI rests on layers of well-governed data, and use cases are prioritized by value and feasibility.
Data and AI layers
- 01Products, agents and dashboardsWhat people use to decide and operate.
- 02Models and analyticsPrediction, optimization and generative AI.
- 03Governance and qualityDefinitions, owners, security and privacy.
- 04Data platformIntegration, storage and access.
- 05SourcesTransactional systems, channels, sensors and external data.
How do we prioritize use cases?
What stays in your organization
- Data governance
- Owners, common definitions, quality and personal data protection.
- Predictive models
- Models in production that anticipate demand, risk, churn or other key events.
- Agents and copilots
- AI assistants integrated into the team’s processes and tools.
- Data architecture
- A platform that integrates the sources and grows with each use case.
- Decision dashboards
- Reliable indicators for management and the business areas.
Where do we apply it?
Led by Benká Digital LAB, with the rest of the holding when the challenge calls for it.
