Del problema a la
herramienta que funciona.

Data Mesh is a decentralised data architecture proposed by Zhamak Dehghani in 2019. Instead of a central team controlling a data warehouse, each business domain (sales, finance, operations) manages its data as a product. It makes sense when there are multiple domains with heterogeneous data, autonomous teams, and centralisation has become a bottleneck. In SMBs it is often applied in lightweight versions: clear domain owners and minimum quality standards.

Data architecture is the structure: how data is modelled, stored, integrated and governed across the organisation (sources, models, flows, quality). Business Intelligence is consumption: dashboards, reports and analyses built on top of that architecture. Without a solid architecture, BI reports tend to be inconsistent or hard to maintain.

An operational dashboard shows near real-time metrics so the team can decide today (open tickets, stock, production queue). A strategic one aggregates data over weeks or months for direction: revenue by segment, churn, acquisition. Mixing them usually produces dashboards nobody reads: operational ones with weekly data or strategic ones cluttered with granular detail.

When the task involves generating, summarising or rephrasing text, code or structures, the success criteria are clear, and you are willing to verify the output. Typical cases: drafting, classifying emails, extracting data from PDFs, prototyping interfaces. Not advisable for critical decisions without human oversight, or with highly confidential data unless deployed on-premises or in a private cloud.

DAMA-DMBOK (Data Management Body of Knowledge) is the most widely used reference framework for data management, maintained by DAMA International. It defines 11 areas: governance, architecture, modelling, quality, security, integration, BI, metadata, document management, master data and life cycle. It works as a checklist to assess maturity and plan improvements.

Data Lake: repository that stores data in its raw format, without a predefined schema, useful for exploratory analysis and machine learning. Data Warehouse: structured store with a defined schema, optimised for BI and historical reporting. Data Mart: thematic subset of the Data Warehouse (for example, marketing or finance) aimed at a specific team. The three layers can coexist.

EU Regulation 2024/1689 classifies AI systems into four levels: unacceptable risk (banned, e.g. social scoring), high risk (education, HR, healthcare, justice), limited risk (chatbots and deepfakes with mandatory labelling) and minimal risk (everything else). High-risk systems must meet requirements for risk management, data quality, transparency, human oversight and registration in a European database.

Few and shared. Examples: average time from lead to first invoice (crosses sales, operations and finance), customer retention rate (sales and support), average incident resolution time (support, product and operations), margin per product line (operations and finance). Each KPI must have a clear owner and a communication channel between areas.

SaaS works when the process is standard in your sector, the tool fits well and your differentiation is not in that process. Custom software works when the process is part of your competitive advantage, existing SaaS forces you to adapt the business to its model, or you need to integrate pieces that do not fit. A frequent hybrid: SaaS for the standard parts and a thin custom layer connecting them.

Common causes: diffuse responsibilities (many validate, no one decides), excess meetings replacing deliverables, dependence on one key person, lack of cross-team KPIs and disconnected tools that force manual data copying. The first step is usually to map the real flow (not the official one) and measure time per stage.

BPMN (Business Process Model and Notation) is an OMG standard for representing business processes with diagrams. It lets business and tech speak the same language: actors, tasks, decisions, events and messages with consistent notation. It is used to document current processes, design improvements and, in some engines, execute them directly.

Four blocks. Sales: pipeline, conversion rate, average ticket. Operations: average delivery time and error rate. Finance: gross margin, runway or cash on hand and collection/payment days. Customer: NPS or equivalent and retention rate. Better a few well-defined KPIs reviewed monthly than a hundred dashboards no one looks at.