In a world where data grows exponentially, choosing the right analytics solutions is akin to finding a needle in a haystack. Technology has made enormous leaps in the past decade, and it can be challenging to determine what your organization truly needs to stay competitive and relevant. This blog guides you through the crucial steps to set up an advanced analytics platform. This includes defining objectives, selecting appropriate technologies, and understanding the value that analytics can deliver for your organization.

Wat do you want to achieve?

The starting point for any organization is to establish the objectives for analytics. Take e-commerce as an example. Do you simply want insight into website visitors, or are you aiming for personalized recommendations? Distinguishing between the need for descriptive insights versus the desire to use predictive or prescriptive analytics forms the basis for your choice in analytics solutions.

For basic reporting, a tool like Google Analytics may suffice. It provides insights into who visits your website and which pages are popular, without the need for advanced integrations or analyses. However, if you want to delve deeper into the matter and, for example, combine click behavior with purchases, Google Analytics falls short. A data warehouse then becomes essential to bring together data from various sources for comprehensive analyses.

And for organizations wanting to go a step further, such as offering personalized recommendations to customers, machine learning models come into play. These models offer advanced insights and predictions but also require more from your technological infrastructure and data analysis skills.

What truly adds value to your organization?

By starting with the end goal – what adds value to your organization – you can align the choice of platforms with the real needs. Determining the right priorities is crucial in this process. By reasoning from a value-adding perspective, you determine from there which actions are necessary and which insights and data you need to collect and analyze.

Three types of solutions

The required solution depends on the specific needs, budget, and existing systems of your organization. Here, we distinguish three main types:

1. Direct to source

For simple needs, such as analyzing online click behavior, reports from the source may be sufficient. This is often the case for smaller organizations (SMEs) for whom complex and expensive platforms are unnecessary. Advantages include minimal effort for setup and use. However, the disadvantage is that insights are isolated, and integration with other data sources is lacking.

2. Data warehouse

When there is a need to integrate insights from multiple sources, a data warehouse solution is suitable. This scenario is relevant, for example, for organizations that want to analyze e-commerce data in addition to click behavior. The advantage is that insights from various sources can be combined, providing a holistic view. The downside is that integration can be more complex, requiring more technical skills.

3. Data platform

For organizations that, in addition to combining sources, also want to act based on this data, for example by making personalized recommendations with machine learning models, an integrated data platform is the best solution. The advantage is the ability to realize both traditional and advanced data-driven applications. The downside, however, is that advanced skills are needed for managing and using the platform. This is more expensive and time-consuming.

The analytics value chain

Ultimately, it’s about finding a solution that fits your desires, budget, and existing systems. Mark Dumay (Partner at Squadra Analytics) has compiled a step-by-step plan to effectively set up your analytics value chain. By following this structured approach, you can ensure that each step in the process contributes to the ultimate goal: generating value from your data.

1. Choose your application

Start by determining the desired applications. Do you want targeted reports (Business Intelligence), predictive models (Machine Learning), an integrated marketplace for your data, or a combination thereof?

2. Identify the required elements

Once you have chosen the application(s), determine which elements are needed to achieve your goals. These are the tools and systems required. Think of software for data editing, analysis tools, and the infrastructure to facilitate these.

3. Set up your data chain

With the application and necessary elements in mind, you set up the data chain. This starts with the sources where your data comes from. This can include ‘Events’ (occurrences that generate real-time data), ‘Change Data Capture’ (changes from source applications), or ‘Data Batches’ (full or incremental data extractions).

4. Standardize your data

The next step is to standardize the data. This means converting raw data into a structured form that is easy to use for your purposes. In the value chain, this is often represented as the movement from ‘Bronze’ to ‘Silver’ – the process of refining data to a usable state.

5. Manage data and insights

After standardizing the data, it must be converted into managed datasets that are ready for use, or from ‘Silver’ to ‘Gold’. These are data optimized for analysis and decision-making, ready to be applied in practice.

6. Implement your insights

The final step is to use your insights for the chosen applications. For reporting and Business Intelligence (BI), you can create dashboards and reports. With Machine Learning, you can perform advanced analyses for predicting trends or behavior. And if you opt for a marketplace, you can make data available to external partners or customers.

The image below shows the steps of the value chain in a visual schema, giving you a clear picture of how to go from data to value. It is a roadmap that helps you navigate the complex world of data and analytics and supports you in making strategic decisions for your organization.

In each phase of the value chain, there are opportunities to advance your business. By following this structure, you ensure that each step contributes to the larger goal: creating value from your data. With this approach, you can ensure that you invest in what is truly important for your organization and not get lost in the multitude of technologies and possibilities that the world of analytics has to offer.

Considerations in choosing a platform

In choosing the right analytics platform for your organization, besides the factors already discussed, there are several other important aspects to consider. These aspects can significantly impact the effectiveness and success of your data and analytics initiatives. These include the organizational structure, sourcing strategy, platform architecture, technology preference, and the infrastructure on which the platform runs.

Organizational model

The choice of an analytics platform must fit your company’s organizational model. In a centralized model, a specific data and analytics team manages all data requests, ensuring a uniform approach but may limit flexibility. A collaborative model combines central guidance with local responsibilities, efficiently managing standards and reusable assets while allowing innovation at the department level. In a decentralized model, individual business units make their own choices, leading to flexibility but also possible fragmentation and inconsistency.


The way you obtain your technical resources can also influence your choice. With staff augmentation, you hire specialists on a flexible basis to complement your own team, while a dedicated team model provides a team that works exclusively on your projects. Outsourcing the entire management and development of projects requires strong communication planning and follow-up but can offer scale advantages and expertise that might be lacking internally.

Platform architecture

The choice of platform architecture depends on your data strategy and needs. Often, the setup of an analytics platform consists of multiple layers, such as the previously mentioned division of bronze, silver, and gold. A hybrid model and data vault modeling prescribe a specific filling of these layers. Data mesh is a relatively recent development, approaching data and analytics from a decentralized, domain-specific angle. These platforms are capable of communicating with each other based on standards and conventions.

Technology preference

Whether you choose best-of-breed, best-of-suite, or open-source solutions depends on specific requirements and preferences. Best-of-breed solutions offer specialized functionality, while best-of-suite provides an integrated experience across a broad range of needs. Open-source technologies offer flexibility and community support but may require more effort to integrate and manage.


Finally, you must decide where your analytics platform runs: on-premises, in the cloud, or a hybrid solution. On-premises solutions offer full control and security but lack the scalability and flexibility of the cloud. Hybrid cloud solutions offer the best of both worlds, while public cloud options provide unmatched scalability and speed of innovation.

By considering all these factors, you can make an informed choice that suits the unique needs and objectives of your organization, ensuring that your data and analytics platform forms a solid foundation for future growth and innovation.


In this guide, we have discussed how to renew your analytics so that your business continues to grow and compete. We have seen that choosing the right platform starts with knowing what you want to achieve. From simple insights to advanced analyses with machine learning, the most important thing is to choose a solution that suits what your organization really needs.

We also looked at the three main types of solutions: direct-to-source, data warehouses, and data platforms, and how you can step by step extract value from your data with the analytics value chain. In addition, factors such as your organizational model, how you assemble your team, the architecture of your platform, your technology preferences, and your infrastructure are important for the success of your analytics initiatives.

By following this guide, you ensure that you lay a strong foundation for your data and analytics platform. This way, you are prepared for the future and can fully leverage the power of data for the success of your organization.


Do you have questions about modernizing your analytics platform or want to learn more about how to promote a data-driven culture within your organization? We at Squadra are here to help. Get-in-touch for a no-obligation conversation about your specific needs and how we can support you in your transformation process.