Most of the SMEs I work with have far more data than they think: turnover, sales by product, margins, delivery times, returns, web traffic, support tickets. The problem is almost never a lack of data, but that this data is scattered, unmeasured and never turned into decisions. The Kit Consulting data analytics advisory, part of Red.es's digital advisory programme, tackled exactly that bottleneck. In this article I explain what the analytics category covered, what business intelligence means applied to an SME, what deliverables you got, and how it differs from the artificial intelligence category. This is informational content: the application period is closed, so you won't find an "apply now" here, but the real mechanics of the service so you understand how it worked and how to make the most of a data advisory, with or without a grant.
What did the data analytics advisory cover?
According to the Acelera Pyme portal, the data analytics service advised the SME on rolling out a data analytics plan tailored to its business, which included studying the required investment, training, and designing the processes needed to deploy it. In concrete terms, the advisor worked across these fronts:
- Inventory of data sources. What data the company generates, where it lives (ERP, CRM, spreadsheets, web) and in what quality state.
- Defining the business questions. What decisions you want to make better: which product carries more margin, which customer is about to leave, where the process gets stuck?
- Selecting metrics and indicators. The KPIs that genuinely matter, avoiding the "vanity dashboard" full of pretty but useless numbers.
- Data architecture and tools. What you need to integrate sources and visualise: a BI tool, a data warehouse, the associated investment.
- Processes and training. How the system stays alive and how the team is trained so decisions are based on data instead of intuition.
The deliverable was a data analytics plan, not an already-live dashboard connected to all your systems. The advisory put the what and the how in order; the subsequent technical implementation could be carried out with your own resources or with other forms of support.
Basic and advanced: which fits your company?
Kit Consulting's data analytics category had two levels, and choosing correctly made the difference between a useful service and a wasted one:
- Basic data analytics. For SMEs starting from zero or close to it: deciding by intuition, living in scattered spreadsheets, with no dashboard at all. The plan lays the groundwork: what to measure, which tool to start with and how to build the habit.
- Advanced data analytics. For SMEs that already have a basic analytics system and need to take a step up: integrating more sources, modelling data, building richer dashboards or introducing predictive analytics.
| Criterion | Basic | Advanced |
|---|---|---|
| Starting point | Deciding by intuition, scattered data | A basic analytics system already exists |
| Goal | Start measuring and deciding with data | Integrate sources, model and predict |
| Typical deliverable | Analytics plan and first KPIs | Advanced dashboards and models |
| Company profile | SME starting its data culture | SME with some analytics maturity |
My practical recommendation is not to over-engineer it: most SMEs who "measure nothing" need the basic level done well, not a big-data project. Analytics maturity is built in layers, not all at once.
What is business intelligence for an SME?
Business intelligence (BI) sounds like something for a multinational, but for an SME it's something very concrete: having your business information in dashboards that update themselves and that any manager can read in thirty seconds. Instead of asking admin for a report that takes three days and is already out of date, you open your panel and see the month's sales, margin by product or the collections pipeline instantly.
BI isn't the tool, it's the discipline of deciding with data. An SME with good BI knows which customer concentrates too much risk, which product drags down margin, which day the warehouse gets clogged, or why returns are growing. And it knows this without wrestling with spreadsheets. Kit Consulting's advisory laid exactly that foundation. If you want to dig deeper into the management tool that organises these indicators at a strategic level, you'll want my article on the 30 essential business performance KPIs.
Does it include dashboards?
The analytics plan defined the dashboards: which indicators go on each panel, for which manager, at what update frequency and from which sources. In other words, the conceptual design of the dashboard was part of the advisory. The technical build of a dashboard live-connected to all your systems was already execution, which could be done afterwards.
This distinction matters because a badly designed dashboard is worse than not having one: it creates noise, measures what doesn't matter and ends up abandoned. That's why the advisory's value was in the design: deciding, with business judgement, what goes in and what stays out. In my sales dashboard guide with 20 commercial indicators you'll see a concrete example of how a panel that actually gets used is built.
What's the difference from the AI category?
It's the most frequent doubt, because analytics and artificial intelligence touch each other. The boundary is this: data analytics answers "what happened and what's happening?" (descriptive and diagnostic), while artificial intelligence answers "what's going to happen and what should be done?" (predictive and prescriptive). Analytics gives you the dashboard; AI, on top of that same data, adds models that predict, classify or automate decisions.
In practice, analytics is the foundation and AI is the floor above it: without clean, well-measured data, no AI model works. That's why many SMEs did well to start with the data category before jumping to AI. If you're interested in the other side, I cover it in Kit Consulting's artificial intelligence advisory. And to see how AI applies concretely to SMEs, my article on AI for SMEs with Kit Digital covers real cases.
What deliverables did you get?
The adhered digital advisor left documented deliverables, because the programme required the service to be justified. In data analytics, the usual ones were:
- Analytics maturity diagnosis and data source map.
- Data analytics plan with the investment, the recommended tool and the timeline.
- KPI catalogue prioritised by area (management, sales, operations, finance).
- Dashboard design (which indicators, which panels, which frequency).
- Training plan for the team to sustain the data culture.
In my experience, the training plan is what separates a data project that survives from one that dies within three months. Technology doesn't fail; adoption does. That's why a good advisory spends time on people, not just tools.
What type of company did it make most sense for?
From my work with SMEs in Castilla y León and Las Palmas, this advisory delivered most value for:
- Companies that decide by intuition and notice they're "flying blind" despite having enough volume to measure.
- Businesses with many products or customers, where the real margin per line is invisible without analytics.
- Growing SMEs that need to professionalise management and stop depending on the manager's memory.
- Companies with siloed data (ERP on one side, web on another, Excel on another) that need a unified view.
To understand how analytics fits into a broader transformation, my digital transformation roadmap will help, and to place this category within the whole catalogue, see the guide on what Kit Consulting is and the guide to the programme's digital advisory.
From data to decision: an example
A typical case: a distributor with hundreds of SKUs and the feeling that "we sell a lot but don't make money". After an analytics advisory, the dashboard revealed that 20% of the SKUs generated losses due to logistics cost, and that three customers accounted for 60% of unpaid invoices. With those two facts — which were always sitting in the ERP, but no one had cross-referenced — the company renegotiated terms and trimmed its catalogue. That's the real return on analytics: not pretty charts, but decisions that change the bottom line. And Kit Consulting's advisory financed exactly the design of that dashboard, the one that makes the invisible visible.
It's worth stressing an idea I repeat often in my projects: data without a question is useless. Before choosing a tool or building panels, the first step is deciding which decisions you want to make better. An SME doesn't need to measure everything; it needs to measure what moves its business. A well-framed advisory starts with that conversation — what keeps you up at night, what are you deciding blind? — and only then moves down to the metrics. That order, business first and technology second, is what distinguishes an analytics project that gets used from just another forgotten panel.
Frequently asked questions
What does the data analytics advisory cover?
It covers rolling out a data analytics plan tailored to the business: an inventory of sources, defining the business questions and relevant KPIs, a recommended BI tool and architecture, a study of the investment, and the design of processes and training to sustain a data culture. The deliverable is an analytics plan with the dashboard design, not necessarily a dashboard already built and connected in production.
What is business intelligence for an SME?
For an SME, business intelligence means having your business information in dashboards that update themselves and that any manager reads in seconds: monthly sales, margin by product, collections pipeline, returns. It isn't the tool itself, but the discipline of deciding with data instead of intuition. It lets you see which customer concentrates risk, which product drags down margin, or where a process gets stuck, without wrestling with spreadsheets.
Does it include dashboards?
The advisory defines the dashboards: which indicators go on each panel, for which manager, at what frequency and from which sources. The conceptual design is part of the service. The technical build of a dashboard live-connected to all systems is later execution. This distinction matters because a badly designed dashboard measures what doesn't matter and ends up abandoned; the value lies in deciding, with business judgement, what goes in and what doesn't.
What's the difference from the AI category?
Data analytics answers what happened and what's happening (descriptive and diagnostic), while artificial intelligence answers what's going to happen and what should be done (predictive and prescriptive), adding models on top of that same data. Analytics is the foundation and AI is the floor above it: without clean, well-measured data, no AI model works. That's why many SMEs did well to start with the data category before jumping to AI.
Sources
- Acelera Pyme — Kit Consulting programme (advisory services catalogue)
- Red.es — Kit Consulting
- BOE — Orden TDF/436/2024, regulatory basis of Kit Consulting
- Recovery, Transformation and Resilience Plan — Get to know the Kit Consulting programme
If you want to turn your data into decisions with a dashboard that's actually useful, get in touch.