Data analytics
Transforming data into reliable, actionable, and scalable decisions for business, BI, and AI
Data analytics encompasses all the practices that allow data to be exploited to inform decision-making, monitor performance, and improve operations. It covers data modelling, visualisation, reporting, and analysis, within Business Intelligence environments such as Power BI, as well as in more advanced analytics and decision support systems.
The challenge is no longer just about having access to dashboards. It's about having reliable, shared, understandable, and truly used indicators. It is this articulation between data assets, modelling, design, usage, and governance that enables Data Analytics to create sustainable value in the organisation. For us, there are four complementary uses: visualising, understanding, analysing, and moving towards a data-as-a-service approach.
Data Analytics has become strategic because organisations need to make decisions faster, more reliably, and based on shared information. Data often exists in large volumes, but it remains difficult to mobilise when sources are heterogeneous, calculation rules are misaligned, models are insufficiently structured, or tools are poorly suited to business uses.
In many companies, Business Intelligence has long been approached as a reporting subject. This approach quickly reaches its limits. Static reports, indicators discussed from one department to another, long display times, or low adoption of dashboards ultimately reduce the perceived value of analytics projects. Employees then spend more time searching for, reprocessing, or disputing data than actually using it.
A structured Data Analytics approach, on the contrary, makes it possible to tackle this subject as a whole. It involves consolidating sources, modelling data according to its uses, securing access, building shared indicators, and designing interfaces that are genuinely useful for reading and taking action. Data visualisation should help to understand data and take steps based on the observed trends, not just display figures.
Purely technical approaches once again show their limitations here. Deploying Power BI or another tool is not enough to create value if the data model is not coherent, if the uses are not defined, or if the end-users do not adopt the solution. This is why an effective Data Analytics approach must incorporate architecture, modelling, user experience, performance, governance, and change management. The "Our Method" page combines data storytelling, design, and user experience to optimise the adoption and effectiveness of reporting services.
How does this expertise translate at JEMS?
At JEMS, Data Analytics is approached as a business-oriented steering service, connecting data, modelling, visualisation and adoption in an industrial logic.
With this approach, we don't just deploy dashboards: we build reliable, high-performing, and genuinely useful analytical solutions for business management.
Audit
We analyse the sources, data models, loading chains, security rules and performance to identify bottlenecks and optimisation levers. The JEMS method also begins with a data audit and recommendations to verify the alignment between the data model, BI tool and user needs.
Modelling
We structure data according to actual usage, working on KPIs, business rules, modelling, performance, and security, particularly in Power BI environments. This approach aligns with JEMS's promise to deliver dashboards suited to different levels of use, from simple indicator tracking to genuine decision-support tools.
Experience
We place users at the heart of the process to design interfaces that are readable, useful, and adopted. For us, the fundamental points are interviews, surveys, prototypes, and user testing, as well as the combination of UX, design, and data storytelling.
Delivery
We implement solutions using an agile, incremental approach, with framing, a backlog, sprints, progressive validation, and onboarding support. Other key points include the delivery of turnkey reports, and the onboarding and industrialisation of dashboards over time.
Business Value
A well-structured Data Analytics approach improves the reliability of indicators, reduces the time to access information, and strengthens user autonomy. It allows business departments to manage their activity more finely, with tools designed to understand, analyse, and decide, rather than simply consult figures. We also position our data visualisation services as tools capable of illuminating the health of an activity and aiding decision-making.
This value is reinforced when the solution is considered over time, with coherent modelling, real business adoption, and the possibility of progressively enhancing analytical uses.
The indicators are becoming more reliable, more shared and more actionable.
Trades access comprehensible and useful information more quickly.
Dashboards are becoming more readable, performant, and adopted.
Financial, operational, or industrial analyses are based on more structured foundations.
Advanced uses connect more easily to data science and AI.
Scaling is made easier thanks to a more industrialised approach.
VISION & PERSPECTIVE
In the coming years, Data Analytics will continue to evolve towards more integrated, more autonomous, and more use-case-oriented solutions. Organisations will seek to move beyond simple reporting to build genuine analytical services, capable of supporting daily management, adapting to new use cases, and integrating more naturally with data platforms and AI usage.
This evolution will be based on several levers: improved upstream modelling, scaling up of supervised self-service, increasing automation of certain analyses, stronger integration between visualisation, governance and alerting, and a reinforced focus on user experience. As such, we distinguish between visualisation, understanding, analysis and data as a service, as well as by focusing on the industrialisation and modernisation of dashboarding services.
For us, a high-performing analytics solution doesn't start when we design a dashboard. It begins with data structuring and continues through to user adoption.
TO GO FURTHER...
Blog article
JEMS becomes «Data Analytics Specialist», the highest level of expertise at Google
FAQ
What is Data Analytics?
Data Analytics involves collecting, modelling, analysing, and reporting data to help organisations steer their business and make more reliable decisions.
What is Data Analytics used for in businesses?
It makes it possible to track performance, identify levers for improvement and have dashboards adapted to real-world usage.
Quelle est la différence entre l'intelligence d'affaires et l'analyse de données ?
Business Intelligence primarily focuses on reporting and visualisation, whereas Data Analytics also covers modelling, optimisation, more advanced analysis, and integration with other data uses.
Why is Power BI often used in Data Analytics?
Because it allows for powerful, secure, and accessible modelling, visualisation, and dissemination of analyses to business users, within a framework of BI and supervised self-service.
Is this a purely technical subject?
No. The value also depends on the definition of needs, the clarity of interfaces, alignment with KPIs, and adoption by end-users.
Why entrust this subject to JEMS?
Because JEMS combines audit data, modelling, UX, design, agility and industrialisation to build truly useful and adopted analytics solutions.
Data Analytics is a central lever for transforming data into operational, readable, and shareable decisions. When structured, governed, and designed for specific uses, it sustainably improves performance, business autonomy, and the quality of management. JEMS supports this transformation with a value-oriented approach, combining data architecture, modelling, visualisation, user experience, and industrialisation.
