Data and BI lead: reliable data, defined once, and dashboards that show the same figures everywhere.
The data and BI lead moves data from the business applications — ERP, CRM, POS, payroll, e-commerce — through to the dashboards of leadership and the business. In between come loads, quality checks, calculation rules and access rights. When these steps are manual, every figure is debated before it is used: that is where automation changes daily work.
Between IT, finance leadership, the business and general management.
The data and BI lead looks after the data warehouse, the semantic model and the catalogue of indicators. The role works with management control so that management figures agree with accounting, with the business to define its indicators, and with IT on flows, security and hosting. In a mid-sized company, the data lead often works alone or nearly so: the more time loads and corrections absorb, the less is left to help teams read and use their data.
A typical day.
- MorningCheck that overnight loads succeeded and that dashboards show up-to-date data.
- MorningHandle a data anomaly reported by the business: a duplicate, an unknown code, a figure that does not reconcile.
- MiddayAnswer a one-off extract or analysis request from a department.
- AfternoonExtend the data model to take in a new source or a new indicator.
- Every weekSettle with the business the definition of an indicator that two departments calculate differently.
- Every monthSupport the close: check that BI agrees with accounting before the reporting is distributed.
What the role is measured on.
- Freshness of data in dashboards
- Rate of successful loads
- Quality of master data
- Agreement between BI and accounting
- Actual use of dashboards by the business
- Response time to analysis requests
Indicators are named, never quantified: levels depend on each company, and the diagnostic measures them.
The problems that come up most.
Several versions of the same figure
Revenue does not have the same value in financial reporting, the sales dashboard and the e-commerce extract. Each has its own rules, and the meeting starts with working out who is right.
Fragile loads
Stacked scripts, files dropped by hand, dependence on an extract run by someone else: one change in the ERP is enough to break the chain, and it is noticed when a dashboard stays empty.
Data quality suffered rather than managed
Errors arise in the source applications but are discovered in BI. The data lead fixes them downstream without being able to act at the source.
Indicators with no written definition
Margin, service level, average basket: the calculation rule lives in a formula or in its author’s head, and nobody can say what is included.
Endless one-off requests
Extracts, cross-analyses, tables for a meeting: urgent requests take priority over building a foundation that would answer them for good.
Access rights that are hard to maintain
A regional director must only see their region, a sales rep only their customers. Managing these rights report by report quickly becomes impossible to sustain.
One role, several realities.
- Fashion and luxury
The data lead brings together boutique, wholesale and e-commerce sales, stock by country and customer data to follow collections and clientele. Customers’ personal data requires strict access rights and precise retention rules.
- Retail
Volumes of till receipts, e-commerce lines and stock movements are high and arrive every day. The data lead builds the view by store, family and channel, and keeps periods comparable when the network or the product hierarchy changes.
- Cosmetics and beauty
Sales to retailers, sell-out reported by distributors, production and quality data: the data lead reconciles sources with different formats and master data, notably the item codes specific to each retailer.
- B2B distribution and wholesale
BI tracks margin by customer and product family, commercial terms, rebates and service level. The data lead maintains large customer and item masters, fed by EDI and supplier catalogues.
- Construction and property
Data comes from job tracking, accounting, payroll and site tools. The data lead builds a view by job, branch and site manager, with field data that is often entered late.
- Industry
The data lead connects business data with production data: work orders, times, scrap, maintenance, sometimes machine readings. Industrial performance indicators are crossed with costs and margins.
- Pharma and healthcare
Production, quality and distribution data are often subject to integrity and traceability requirements. The data lead documents the origin of every indicator and controls changes to the model.
Automate the task, keep the judgement.
What gets automated
- Loading of application data into the data warehouse, scheduled, monitored and restarted on failure, with an alert to the owner.
- Quality checks at each load — volumes, duplicates, unknown codes, agreement with accounting — that block distribution when an anomaly is found.
- The catalogue of indicators and the data dictionary kept up to date from the model itself, with their definition and source.
- Data access rights applied automatically from the directory and the organisation: region, store, customer portfolio.
- Answers to business questions in natural language by an AI agent connected to the semantic model, within each person’s rights.
What stays human
- The definition of indicators and their arbitration with the business and finance leadership.
- The qualification of anomalies and the choice to fix them at the source rather than downstream.
- Helping teams read and use their data.
How to automate, case by case.
Each case starts from a question leadership teams ask. The starting point, what gets automated, the tools involved, and what stays in the teams’ hands.
How do you build a shared semantic model so that every BI report shows the same figures?
- Today
- Each department has built its reports with its own calculations: finance, sales and e-commerce show three different revenue figures for the same month. The gaps come from returns, discounts, the dates used, and nobody knows them all.
- What gets automated
- Shared indicators are rebuilt in a single semantic model: definition, scope, source, calculation rule. Existing reports are reconnected to this model, and an automatic check compares old and new figures during the transition to explain each gap. Any new measure goes through the model before it appears in a report.
- What stays human
- The departments concerned settle the definition used for each shared indicator.
- reporting tool (BI)
- data warehouse
- catalogue of indicators
How do you automate loading data into the data warehouse?
- Today
- Data arrives through scripts written as needs arose, scheduled extracts in the ERP and a few files dropped by hand every Monday. When a load fails, nobody is told and the dashboard shows the previous day’s figures without saying so.
- What gets automated
- Loads are described in an orchestration tool (ETL or ELT): source, transformation, dependencies, frequency, owner. They run in order, restart on a temporary failure and alert the owner otherwise. Each dashboard shows the freshness date of its data, and manual files are replaced by direct connections where the application allows it.
- What stays human
- The data lead decides in which order fragile loads are rebuilt and the frequency suited to each use.
- data integration tool (ETL)
- data warehouse
- ERP
- monitoring tool
How do you automate data quality checks before dashboards are distributed?
- Today
- An unknown store code, a missing day of sales or a duplicated invoice reach the dashboards without any alert. The error is spotted by a director in a meeting, and confidence in BI drops.
- What gets automated
- Checks run at each load: expected volumes, values outside usual ranges, codes missing from master data, duplicates, totals agreeing with accounting. A blocking anomaly suspends distribution and notifies the source owner; a minor anomaly is flagged on the report. An AI agent spots unusual variations that fixed rules miss.
- What stays human
- The data lead qualifies anomalies and decides whether to distribute, together with the data owner.
- data quality tool
- data warehouse
- reporting tool (BI)
- messaging
How do you automate the data dictionary and the catalogue of indicators?
- Today
- Indicator definitions are scattered across notes, formulas and the memory of a few people. A newcomer does not know what the “net revenue” field contains, and two teams discover in a meeting that they are not talking about the same thing.
- What gets automated
- The catalogue is generated from the data model itself: tables, fields, measures, sources and lineage, from the originating application to the report. An AI agent proposes a first plain-language definition of each indicator from its formula, which the owner approves. The catalogue updates with every change to the model and can be opened from the reports.
- What stays human
- Each business owner approves the definition of their indicators.
- data catalogue
- data warehouse
- reporting tool (BI)
- AI agent
How do you automate data access rights in the BI tool?
- Today
- Each report has its own rights, granted by hand as requests come in. A regional director whose scope changed still sees their former region, and a report shared by link circulates beyond its audience.
- What gets automated
- Rights are carried by the model: row-level security by region, store, company or customer portfolio, fed by the directory and the organisation held in the HRIS. Staff moves update scopes automatically. A periodic review lists access by report and by person, for approval.
- What stays human
- Data owners decide who sees what, and the data lead approves the security rules.
- reporting tool (BI)
- directory and access management
- HRIS
How do you let business teams query their data in natural language with AI?
- Today
- One-off questions — sales of a family over a period, customers who stopped ordering, a comparison between two stores — reach the data lead by e-mail, who runs the extract when time allows.
- What gets automated
- An AI agent is connected to the semantic model, not to raw tables: it uses the approved indicator definitions and the user’s access rights. It answers in natural language, shows the query and filters applied, and says when a question falls outside the model’s scope. Frequent questions are identified to enrich the dashboards.
- What stays human
- The data lead decides which data is open to the agent and regularly checks the accuracy of its answers.
- AI agent
- reporting tool (BI)
- data warehouse
- directory and access management
How do you automate anomaly detection in management indicators?
- Today
- A drop in sales at one store, a sudden rise in returns or an abnormal margin on a product family are noticed when someone looks at the right report at the right time. Often, that is at month end.
- What gets automated
- A tool monitors key indicators daily at a fine level of detail and compares each value with its usual behaviour, seasonality included. Significant deviations are sent to the person responsible with context: scope, period, contribution of each subset. An AI agent drafts a first possible explanation from the data available.
- What stays human
- The business owner qualifies the alert and decides what action to take.
- reporting tool (BI)
- data warehouse
- AI agent
- messaging
How do you automate the purging and pseudonymisation of personal data in the data warehouse?
- Today
- The warehouse has kept customer, employee and prospect data since it was created, with no retention period applied. A customer’s erasure request is handled in the CRM but not in the warehouse.
- What gets automated
- Each table holding personal data is linked to a purpose and a retention period, taken from the record of processing activities. A scheduled job purges or aggregates expired data, pseudonymises data used for analysis that does not need to identify the person, and propagates erasure requests received in the source applications. Each run is logged.
- What stays human
- The data protection officer approves the periods and rules, and the data lead checks they are applied.
- data warehouse
- data integration tool (ETL)
- record of processing activities
- CRM
How do you automate monitoring of dashboard usage and clean up unused reports?
- Today
- Over the years, reports have multiplied: slightly modified copies, tables built for a single meeting, abandoned versions. Every change to the model means checking reports nobody opens any more.
- What gets automated
- The BI tool’s logs are collected to measure the use of each report: views, users, date last opened. Reports unused for a defined period and near-duplicates are listed, and their authors receive an archiving proposal. The most viewed reports are flagged as priorities when the model changes.
- What stays human
- Authors and the data lead decide whether to keep, merge or archive each report.
- reporting tool (BI)
- logging tool
- messaging
How do you keep year-on-year comparisons accurate when the organisation or master data changes?
- Today
- A store moves to another region, a product family is split, a sales rep takes over a portfolio. Dashboards reassign the whole history to the new organisation, or mix the two, and comparisons become wrong.
- What gets automated
- Master data is historised in the warehouse: each change of assignment is dated and the previous version is kept. The model offers both views, actual organisation and constant organisation, and each report states which it uses. Master data changes detected in the source applications feed the history with no manual action.
- What stays human
- Management control and business leadership choose the reference view for each use.
- data warehouse
- master data management (MDM)
- reporting tool (BI)
- ERP
What teams look to improve.
- How do you get the same figures in every dashboard?
- By defining each indicator once, in a shared semantic model used by every report. The calculation rule, scope and source are written in a searchable catalogue. A report no longer recalculates margin its own way: it reads the model’s.
- Do you need a data warehouse to do BI?
- As soon as data comes from several applications, yes, in one form or another. The warehouse keeps history, makes data comparable and relieves business applications of analytical queries. Its size fits the need: a mid-sized company does not need the infrastructure of a large group.
- How do you make data quality reliable?
- By checking on the way in rather than correcting on the way out. Automatic checks flag the anomaly at load time, assign it to the data owner in the source application, and track its correction. BI stops being the place where errors are discovered.
- How do you automate reporting with a BI tool?
- By automating the whole chain: extraction from applications, loading into the warehouse, checks, calculation of indicators in the model, report refresh and distribution. Each step is monitored, and a report is not sent if a check fails.
- Can AI replace dashboards?
- It complements them. An AI agent connected to the semantic model answers one-off questions in natural language, using the model’s definitions and rights. Dashboards remain useful for regular monitoring, shared by everyone on the same indicators.
- How do you manage access rights in BI?
- Through rules carried by the model, not by each report: row-level security by region, store or portfolio, fed by the directory and the organisation. A change of role updates the rights with no manual action.
- How do you reduce one-off extract requests?
- By analysing them: most come back in a similar form. The most frequent become documented, up-to-date self-service datasets that the business explores itself. The remaining questions go through the AI agent or a tracked request.
- Can personal data be held in the warehouse?
- Yes, if the purpose is defined, access is limited and the retention period is set and applied. Data that does not need to identify a person is pseudonymised or aggregated, in line with the GDPR and in agreement with the data protection officer.
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