Empowering Efficiency: Building a Self-Service Platform for Analytics

In a previous blog, we explored the benefits of MLOps for business leaders and managers. Now, let’s have a look at the concept of self-service analytics platforms. Imagine a business user creating and launching their own Business Intelligence dashboard, or a citizen data scientist developing and deploying a Machine Learning model — without waiting for IT support! This autonomy not only drastically reduces time-to-market, but also drives innovation and agility within the organisation.

Self-service platforms are designed to empower users by automating tasks traditionally handled by IT. This automation leads to several key benefits, including:

• Increased efficiency: Automation reduces the time required to manage infrastructure and other pre-built components for platform users. This leads to faster development cycles and quicker insights.
• Improved quality: Automated processes minimise the risk of human error, ensuring consistency and reliability, especially when automated tests are included.
• Improved user experience: Empowering users to take control of their own projects creates a sense of ownership and responsibility, and contributes to a culture of respect and appreciation among colleagues. When users feel empowered and valued, they are more likely to engage deeply with their work, resulting in higher satisfaction and productivity.
• Enhanced operational stability: Standardised processes and tools maintain uniformity across the organisation. As employees come and go, having uniform processes and solutions ensures smoother handovers of development and maintenance tasks.

Self-service platforms are often used in combination with some form of DevOps, for example MLOps. To be able to fully manage and maintain their own solutions, it is essential for DevOps engineers to be able to manage their infrastructure, which is precisely the autonomy that self-service platforms can provide.

Industry giants lead the way

Cloud providers like Microsoft (Azure), Google (GCP), and Amazon (AWS) have set the gold standard for successful self-service platforms. To meet the demands of their massive user base, these platforms have been highly automated. Key features of industry-leading self-service platforms include:

• Automation and orchestration: Automated workflows and orchestration tools streamline complex processes (such as resource management), reducing the need for manual intervention.
• User-friendly interfaces: Intuitive dashboards and APIs make it easy to manage and deploy resources for both developers and non-technical users.
• Support and documentation: Comprehensive support options and detailed documentation assist users in troubleshooting and optimising their use of the platform.

Organisations aiming to build their own self-service platforms should incorporate these features to ensure efficiency and user satisfaction. However, the level of automation can vary, and each organisation must align its automation and implementation choices with its overall strategy. Fortunately, building a platform from scratch is usually not necessary.

Case studies The organisations I have worked with, have all built upon existing cloud platforms and open-source frameworks to provide these features to their users. In future blogs, we will explore detailed case studies of successful implementations for different clients, showcasing the practical benefits and challenges of building self-service platforms. These real-world examples will provide valuable insights into the strategies and best practices that have led to successful outcomes.

Developing a self-service platform

Building a self-service platform is a long-term endeavour. As with all large projects, the current best practice is to take an agile approach. By agile, I mean the ability to move and adapt quickly and easily. This means that a project to develop a self-service data or analytics platform should follow an iterative approach, starting with a single use case to be expanded upon in future iterations. This can be, for example, a single BI dashboard requiring data from a single source. Then, with the end goal in mind, organisations can take incremental steps, each worth their own individual investment, building use cases, features and entire capabilities one step at a time.

Modern self-service platforms should be designed with adaptability in mind. A modular design allows individual components to be swapped out and replaced with newer technologies as they become available. This ensures the platform can take in future technological advancements and meet evolving business requirements, allowing a step-by-step approach to developing the platform. The leading cloud platforms of Microsoft, Google and Amazon have all been built in such a modular fashion. This makes them an excellent foundation upon which you can build your own self-service platform. Alternatively, there are robust open-source solutions available that offer flexibility and cost-efficiency, allowing for customisation to meet specific organisational needs.

Example of a high level roadmap

• Define the level of self-service that the platform should provide at each stage of development. This should be aligned with your overall data strategy, and it may take the form of a roadmap.
• Create an architecture and modular design that can be delivered and extended incrementally. Start with a high-level description, only filling in more details when you need them.
• Define the initial use case, such as a simple report using data from easily accessible sources.
• Develop your data warehouse / data lake one data source at a time.
• Build your initial use case, typically a BI dashboard.
• Add machine learning as a capability, starting with a minimal MLOps implementation, adding functionalities and use cases in future steps.
• Optionally: connect with your corporate document management system to implement Generative AI solutions.

Choosing the target level of self-service

Self-service comes in many shapes and forms. The leading cloud providers offer the highest levels of self-service you can find, but for most companies creating those are neither feasible nor wanted.

1. Self-service product development Essential: The whole purpose of your self-service platform is for users to build their own data assets, dashboards, machine learning models, and so on. Ideally this is facilitated with development tools such as a version control system, testing tools, and so on.
2. Self-service product deployment Essential: Without a means for users to deploy their own products, I would say the platform really should not be called self-service. Continuous Deployment (CD) systems and automated deployment pipelines are an essential part of DevOps. Quick and reliable delivery of new features and fixes is vital for maintaining user satisfaction and operational efficiency.
3. Self-service product monitoring and operations Essential: Users must be able to monitor the performance and operations of their deployed products. This includes access to logs, performance metrics, and alerting systems to ensure their solutions run smoothly and efficiently.
4. Self-service infrastructure creation Nice-to-Have: Allowing users to create their own infrastructure can be beneficial for advanced teams. This includes provisioning servers, databases, and other resources as needed, providing greater flexibility and control. Still, plenty of organisations keep this reserved for a centralised IT team.
5. Self-service infrastructure monitoring and operations Nice-to-Have: Users should have the ability to monitor and manage the infrastructure supporting their applications. This includes tracking resource usage, performance, and potential issues to maintain optimal operation.
6. Self-service governance Optional: Offering tools for self-service governance allows teams to manage their own data policies, access controls, and compliance with organisational guidelines, promoting responsible data usage.
7. Self-service security and compliance Optional: While centralised control over security and compliance is often preferred, providing self-service options can empower teams to manage their own security settings and compliance requirements, ensuring they meet organisational standards.
8. Self-service cost management (FinOps) Optional: Providing tools for cost management enables users to track and optimise their spending on cloud resources, helping to control budgets and reduce unnecessary expenses.
9. Customised platform management tools Optional: Developing customised management tools tailored to the specific needs of your organisation can enhance the user experience, although this is often a significant investment and may not be necessary for most organisations.

Ultimately, it will be up to your organisation’s business strategy, aligned with your IT and Analytics strategy, to determine which features are most important. Without the essential elements, you can’t really speak of a self-service platform. Being able to create, monitor and/or manage the infrastructure for your projects could be nice to have for sufficiently capable and mature teams, but many organisations will prefer to deal with this in a centralised team. Even more organisations will want to have a strong control over security, compliance, governance and cost management, enforcing policies centrally without offering these as self-service. Finally, building customised platform management tools (such as Microsoft’s Azure CLI and SDK, or Google’s Cloud CLI and SDK) is an expensive endeavour that not many organisations will find worth the investment; they may, however, provide simple automation scripts for the most common tasks specific to their organisation.

Challenges and considerations

High initial costs
Implementing a self-service analytics platform presents several significant challenges. One of the biggest challenges is probably the high initial investment required. Even the first iteration of a data or analytics platform, creating an MVP for a single pilot use case, can cost quite a lot of time and money to build and maintain. Therefore, if there are not enough users and use cases to benefit from the platform, it may not deliver sufficient value to justify building it in the first place. This means that self-service platforms are typically only viable for larger organisations with bigger budgets and a larger user base.
Potential Solution: To mitigate high initial costs, consider leveraging existing cloud platforms and open-source frameworks. This approach can significantly reduce development time and costs while providing robust features.

User training
Additionally, to fully leverage the potential of a self-service platform, users must understand its features and capabilities well. This can be achieved through proper user training and documentation. Offering such extensive training can be resource-intensive and time-consuming, especially for a large user base.
Potential Solution: Integrate your custom training programs with generic online training courses, also known as massive open online courses (MOOCs), such as those offered on platforms like Coursera and edX. Implement a phased training program that starts with basic functionalities and gradually introduces advanced features.

Standardised template solutions
When planning for efficient use of the platform, it is important to offer standardised (template) solutions for generic use cases. By providing examples of how to use the platform, users can get up to speed and start deploying their own solutions quickly. Such standardisation ensures consistent usage across the organisation, leading to smoother transitions and handovers between different users. At the same time, it is equally important to allow users the flexibility to tailor these solutions to their specific needs. Without flexibility, users will walk away from a self-service platform, in favour of alternative solutions that fit their needs better. Balancing standardisation with customisation can be a complex undertaking, requiring careful planning and execution.

User satisfaction
To ensure user satisfaction, it is crucial to provide intuitive interfaces and comprehensive support. Regular user feedback sessions and continuous improvement based on this feedback can help achieve and maintain high user satisfaction levels.

Product maintenance
Finally, not all development teams using a self-service platform may have the same level of skill (or interest) in owning and maintaining their solutions. Having a centralised support team that can assist more business-oriented development teams can alleviate some of this challenge. In an upcoming blog, “Optimising AI Development with End-to-End Product Teams”, I will dive deeper into this topic and discuss strategies to reduce the burden on development teams that may lack strong technical skills.

Conclusion

In summary, self-service platforms revolutionise the way organisations can handle data and analytics. By automating tasks traditionally managed by IT, these platforms increase efficiency and reduce errors, and empower users to take control of their projects. This autonomy enables organisations to respond quickly to market changes and capitalise on new opportunities, making self-service platforms a crucial component of modern business strategy. Embracing this approach can lead to significant competitive advantages and drive innovation across the organisation.

To realise these benefits, it is essential to invest in user training, maintain a balance between standardisation and flexibility, and continuously gather user feedback for improvement. By doing so, organisations can create a thriving environment where innovation and efficiency go hand in hand.

Share Your Experience

Have you implemented a self-service platform in your organisation? What challenges did you face, and how did you overcome them? Share your insights and experiences in the comments below to help others on their journey.

About the author

Erik Jan de Vries is an award-winning ML product architect / engineer and freelance consultant. While AI tools were used in the writing process for this blog, all the ideas and arguments presented here are my own. I’m always up for a chat about AI and ML, feel free to contact me on LinkedIn.