Contents
The importance of a clear Data & AI service offering
Let's take the example of an industrial group that has deployed a Data platform to centralize and enhance its data. The IT teams have built up a robust technological base, but on the ground, the business units are wondering :
- What services are available?
- How do you approach them?
- What commitments are associated with each service?
This lack of clarity creates frustration, limits ownership and slows down the company's data-driven transformation. The result? Under-utilized technological investment, duplication of initiatives and limited adoption by the business.
Faced with this situation, the IT department tried to alleviate the problem by organizing training sessions and distributing user guides. However, without a structured service offering and clear governance, these efforts remain insufficient. Businesses need a simple, readable and operational framework to quickly understand what they can use and how.
In this context, structuring a Data & AI service offering becomes a strategic imperative.
It's all about defining accessible services, aligned with business uses, and establishing a governance framework that enables effective, scalable management. A well-designed offering transforms the platform into a powerful lever for innovation and productivity, rather than a simple receptacle for under-utilized data.
What are the consequences of a poorly structured Data and AI service offering?
- Low business adoption: Without a clear understanding of the services available and how they can be accessed, users become disengaged and continue to use obsolete or parallel solutions.
- Loss of efficiency and productivity: The absence of a structured framework leads to project duplication, siloed development and recurring requests to IT teams for needs already covered.
- Increased complexity in governance: Without clear rules and established processes, data management becomes chaotic, with a lack of visibility on the quality, compliance and security of the information used.
- Missed opportunities for innovation: A poorly structured service offering hinders the exploration of new data-driven initiatives, as users are reluctant to invest time and resources in an environment deemed too uncertain.
And what about profits?
Conversely, a clear, well-structured service offering transforms a Data & AI platform into a true gas pedal of performance and innovation: When a Data & AI service offering is poorly defined or insufficiently formalized, negative impacts are quickly felt within organizations.
- Streamlined processes: A well-defined framework avoids redundant requests, and enables users to access the services they need directly.
- Business empowerment: With a clear, well-documented offering, users can get to grips with the tools made available to them more quickly, reducing dependency on technical teams.
- Improved data quality: reinforced governance ensures better monitoring of data flows and guarantees their reliability.
- Accelerating innovation: By simplifying access to Data & AI services, companies create fertile ground for rapidly testing new ideas and encouraging experimentation.
The challenges of a well-defined service offering ⚡
A well-structured service offering transforms a Data & AI platform into a genuine performance lever for the business. It enables us to respond to the concrete challenges faced by users.
Before diving into the practical aspects, it's essential to understand what a clear structuring of services can do for organizations. The aim is to guarantee a platform where every user, whether novice or expert, can navigate easily and exploit the full potential of the data made available.
- Clarify the offer and make it understandable: A well-organized service catalog, with precise, accessible descriptions, enables business users to quickly identify the services best suited to their needs. It also avoids redundancies and inappropriate requests.
- Facilitating adoption and use: An easy-to-understand, intuitive offering, backed up by dedicated training and support, reduces the complexity perceived by users. The aim is to make them want to use these services on a daily basis.
- Optimize governance and management: A structured offering includes monitoring indicators, invoicing rules and service level agreements (SLAs), guaranteeing cost control and optimal alignment with business needs.
- Ensuring ongoing scalability: An offer designed from the outset to be modular and adaptable makes it easy to integrate new needs and evolve in line with technological advances and user expectations.
Key success factors for structuring a Data & AI offering 🏆
For a Data & AI service offering to be effective and sustainable, it must be based on several fundamental principles. Far from being a simple showcase of services, a well thought-out offering is a genuine structuring and optimization tool for the company.
- A clear, defined scope: Precise mapping of services and their functional scope is essential. Each offer must be documented, with its objectives, access procedures and the roles of the various stakeholders.
- An approach centered on business needs: The offer must not be a simple technological declination, but a direct response to end-users' expectations. This implies a phase of active listening and co-construction with the business to guarantee the relevance of the proposed services.
- A modular, scalable structure: The organization of services into distinct commitment levels and modules enables us to meet a variety of needs and anticipate future developments.
- Simplified adoption thanks to dedicated support: The implementation of clear user paths, tailored training courses and communication media encourages rapid, effective appropriation of the services.
A 5-step methodology for structuring and deploying a Data & AI offering 🛠️
Deploying an effective service offering doesn't happen overnight. It requires a structured, iterative approach, involving both IT and business teams.
1. Framing and initial structuring
Before structuring the offer, it's crucial to establish a solid foundation. This first step involves analyzing the existing situation, understanding expectations and setting objectives.
- Gathering business needs and analyzing current usage
- Mapping existing services and defining the target perimeter
- Definition of governance principles and stakeholder roles
2. Detailed offer design
Once the scope has been clarified, it's time to model the offer, taking into account specific organizational and business requirements.
- Precise definition of services, functionalities and commitment levels
- Development of a business model and a pricing structure tailored to customer needs
- Formalization of subscription processes and SLAs to guarantee quality service
3. Documentation and support
- Creation of clear, accessible user guides and communication media
- Defining use cases and setting up concrete usage scenarios
- Integration of services into existing collaborative tools and IT platforms
4. Pilot deployment and fine-tuning
Testing the offer on a restricted perimeter enables us to identify any flaws and adjust them before large-scale deployment.
- Launch of the offer to a representative panel of users
- Collect feedback and adjust services according to identified needs
- Setting up usage monitoring and performance indicators
5. Global deployment and continuous improvement
A service offering evolves with its environment. The ultimate goal is to ensure regular monitoring and constant adjustment to meet user needs.
- Gradual expansion of the offering in line with business needs
- Track adoption and efficiency indicators to measure service performance
- Regular updates based on user feedback and technological developments
A case study in the social protection sector 🏥
Challenges encountered
- A fragmented range of services, making it difficult to understand and access available solutions.
- Lack of governance leading to disparities in data quality and security
- Limited uptake due to lack of appropriate support and assistance
Our approach
- Mapping and clarification of services: a structured catalog has been defined, with clear descriptions and appropriate levels of commitment.
- Robust governance: monitoring indicators have been integrated to ensure effective steering
- User support: creation of teaching aids and organization of workshops to promote ownership
The results
- Simplified access to Data & BI services, organized around three major areas: Enterprise BI, Self-BI and Advanced Query, for increased use by business users.
- Streamlined processes reduce analysis delivery times
- Reinforced governance to ensure optimum alignment between business needs and technical capabilities
Conclusion: a strategic lever for data exploitation 🎯
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