Thinking of doing more with your data? Do your customised AI- driven data monetisation lab with us!

Unlocking revenue with embedded analytics

We are pioneering the realisation of data monetisation journeys for thriving companies. Together, we amplify your customer experience through analytics solutions directly embedded into your products and services.

Explore the journey
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“Claudy’s Analytics Journey-as-a-Service not only helped us to form a strong analytics roadmap for our company, but also to implement solutions with real impact on our customers’ and partners’ experience in less than 6 months.”

Gael Poffet, CTO at Swibeco

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Your potential

Why embedding analytics into your product?

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20% upsell opportunity
Companies that can provide self-service analytics capabilities to their customers and partners, generally see an upselling potential of up to 20%
80% cost reduction
By replacing manual data reporting with highly-automated embedded analytics, companies can reduce their reporting cost by up to 80%
Competitive advantage
By leveraging the uniqueness of their data models, companies can provide differentiating insights to customers to gain a competitive edge.
Discover your potential
Our services

Analytics Journey-as-a-Service

We exist to serve you as the experienced guide on your data monetisation journey. Our Journey-as-a-Service offering is built from purposefully designed service packages to help you create and achieve your big data monetisation vision and overcome any obstacle on the journey with certainty.

Join us at any stage in the journey.

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Data Monetisation Lab

Time: 2 weeks

Could there be untapped value in the data youʼre sitting on? Find out with us! With our proven, AI and data-driven process, we provide you with up to 3 fully validated data monetisation business cases to form your outcome-driven data vision in 2 weeks.

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NEW!

Data Cloud Kickstarter

Time: 3-4 weeks

Ready to climb the next mountain? Stop wasting resources on manual data handling and start your data monetisation journey with the Snowflake Data Cloud. We partner with you to build your first custom analytics solution on Snowflake to begin understanding your customers and partners better than ever before.

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Intelligence Suite Builder

Time: 2-4 months

Integrating and making sense of all your data can seem like the longest and steepest hike on this journey. But itʼs essential to building a solid foundation for your big data monetisation vision. Keep cool, weʼve got your back! This package is filled with service components that help you to get your internal intelligence suite built in no time.

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Embedded Evaluator

Time: 3-4 weeks

Ready to see the return on analytics? Let’s tackle the last mile on this journey together. We help you selecting the right technology and build your first embedded analytics product to test out with your customers.

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Embedded Realiser

Time: 1-2 months

Your customers love it and want more? We partner up with you to bring your complete embedded analytics product to life. Every mountain you’ve climbed so far, will be worth the view you get from this one. Together, we turn analytics into a profit center for your company.

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Our ecosystem

Partners and technologies

We work with carefully selected partners and cloud-agnostic technology vendors to help you succeed on the analytics journey.

The partners that we trust

We partner with Snowflake to build the backbone of the world’s future data and AI applications together.

Our local, Swiss consulting partner for any legal matters in technology and data.

The technologies that we support

The leading cloud data platform provider, providing unlimited scalability for any data analytics and AI needs.

Awarded Snowflake’s Data Integration Partner of the Year. The best tool to build data models faster and automate data governance strategy execution.

The simplest, open-source application framework. Natively available in Snowflake. Best for prototyping and testing of data apps.

Microsoft’s Business Intelligence platform. A price-competitive choice, especially if you’re an existing Microsoft customer.

Great Swiss challenger in the BI market. ‘Ask data’ at its core. Good for internal and embedded analytics.

Enablers for state-of-the-art embedded analytics apps. Maximum customisability at minimum cost.

FAQ

Frequently asked questions.

Looking for quick guidance on your analytics journey? Get some fast answers on the most frequently asked questions of our customers.

Embedded analytics is the integration of data analysis and reporting directly into your software or platform. It empowers users to access real-time insights and visualizations without leaving your application, enhancing user engagement and informed decision-making. Ultimately, it is the most powerful tool software companies have to proof their value to customers.

To nurture a data-driven culture:

  1. Lead by example, prioritize data in decision-making.
  2. Provide access to data and analytics tools.
  3. Foster data literacy through training and workshops.
  4. Encourage data sharing and collaboration.
  5. Recognize and reward data-driven achievements.
  6. Continuously communicate the value of data-driven insights.
  1. Cloud Data Warehouse: You can start with Amazon Redshift, Google BigQuery, or Snowflake for scalable data storage and compute for data processing.
  2. ELT Tools: You can use ELT tools like Apache NiFi, Talend, or Apache Spark for simple data integration.
  3. Data Streaming: Implement Apache Kafka or Apache Flink for real-time data ingestion.
  4. Data Visualization and Analytics: Leverage Tableau, Power BI, or Looker for insights and Python with Pandas, NumPy, or machine learning for advanced analytics.
  5. Embedded Analytics: Consider ThoughtSpot, GoodData or Streamlit for embedded analytics apps.

This stack provides a strong foundation for data-driven decision-making and growth.

To choose the right technology for your data analytics tech stack:

  1. Define your goals and requirements.
  2. Assess your data needs, scalability, and budget.
  3. Consider compatibility, performance, and security.
  4. Prioritize user-friendliness and support.
  5. Conduct a proof of concept.
  6. Research vendor reputation and future-proofing.

Involving stakeholders and careful evaluation will guide you to the best-fit technology for your analytics needs.

To ensure a high ROI on data analytics:

  1. Define clear business objectives.
  2. Focus on actionable insights that drive decisions.
  3. Continuously measure and evaluate ROI.
  4. Optimize analytics processes for efficiency.
  5. Invest in training and upskilling for your team.
  6. Stay agile and adapt to changing business needs.
  7. EMBED valuable analytics apps into your product to monetise your data with your customers and partners.

To visualize and share data effectively:

  1. Use a BI tool to create interactive dashboards.
  2. Create clear, audience-specific visuals.
  3. Ensure data accuracy and privacy to build trust in data.
  4. Leverage collaboration tools and seek feedback for improvement.
  5. Share via email, direct sharing in the warehouse, or embeds in your applications.
  6. Tell compelling data stories to make insights actionable.

These practices enhance data-driven decision-making and collaboration with your stakeholders, partners, and customers.

The level of programming involved in embedded analytics varies. It can require basic configuration and integration skills, such as using APIs or SDKs provided by analytics platforms. In more advanced cases, customization and coding expertise in languages like JavaScript, Python, or SQL may be necessary to tailor analytics components to your software application’s unique requirements.

If you don’t have a dedicated data analytics team, you can still leverage analytics effectively. Consider outsourcing analytics tasks to specialized firms or using user-friendly analytics tools that require minimal coding. Additionally, cloud-based analytics platforms often offer built-in features to simplify data analysis for non-technical users.

For a cost-effective data analytics stack on a limited budget, consider using open-source tools like PostgreSQL or MySQL for data storage, Apache Spark for data processing, Streamlit for data apps or tools like Metabase or Redash for visualization. Cloud providers like AWS, Google Cloud, or Azure also offer budget-friendly options with pay-as-you-go pricing.

To ensure scalability of your analytics tech stack:

  1. Use cloud services for elastic scaling.
  2. Opt for scalable data warehouses like Snowflake.
  3. Implement containerized applications with Kubernetes.
  4. Monitor performance and scale resources as needed.

If analytics queries strain your production databases too much:

  1. Consider query optimization techniques.
  2. If no success, implement a data warehouse for analytics.
  3. Use ETL processes to offload data overnight.
  4. Consider data historization options to keep track of changes in the production database

Offshore outsourcing is a great way to get affordable resources for the implementation of specific data analytics projects. However, typically these resources are lacking the strategic and cultural transformation capabilities to get your organisation from manual data processing to actually adopting modern embedded analytics solutions. Claudy offers you time and expertise from subject matter experts with deep industry know-how. We don’t provide time for money. We provide value for money.

To adhere to evolving data privacy regulations:

  1. Stay informed about new regulations.
  2. Conduct regular data audits and risk assessments.
  3. Implement strong data encryption and access controls.
  4. Appoint a Data Protection Officer (DPO) if required.
  5. Document data processing activities for compliance.
  6. Keep a data breach response plan ready.
  7. Train employees on data privacy practices.
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