Unlocking revenue with data monetisation
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.
“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
Why should you capitaliseon your data assets?
20% upsell opportunity
80% cost reduction
Competitive advantage
Data MonetisationJourney-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 on the journey.
Data Monetisation Lab
Time: 4 weeks
Could there be untapped value in the data youʼre sitting on? Find out with us! Building on an engaging, collaborative and AI-augmented process, we provide you with up to 3 fully validated business cases to form a strong data monetisation vision for your business in 4 weeks.
Data Cloud Kickstarter
Time: 3-4 weeks
Ready to climb the next mountain? Avoid wasting resources on manual data handling and kickstart your data monetisation journey with the Snowflake Data Cloud. We partner with you to build your first custom data analytics on Snowflake to prove the effectiveness of the platform in supporting your long-term vision.
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.
Embedded Evaluator
Time: 3-4 weeks
Ready to capitalise on your data assets? Let’s tackle the last mile on this journey together. We help you selecting the right technology and build your first embedded analytics solution to test out with your customers or partners.
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 the market. Every mountain you’ve climbed so far, will be worth the view you get from this one. Together, we turn your data department into a profit center for your company.
Partners and technologies
We work with carefully selected partners and cloud-agnostic technology vendors to help you succeed on the analytics journey.
We are a proud Snowflake Select Partner to build the backbone of the world’s data and AI applications together.
We partner with dbt to enable seamless data integration into the world’s leading data cloud.
Our partnership with Veezoo is all about scalable analytics, allowing end users to ask any question directly to the data.
We join forces with Embeddable to build the most powerful and valuable Analytics-as-a-Service solutions in the world.
Our expert consulting partner for any legal matters involved with data monetisation strategies.
Data Integration, Engineering & Modeling
Joining, cleaning and curating all your data
Internal Analytics
Data visualisation & sharing
Embedded Analytics
Analytics-as-a-Service
And more charting libraries
Supporting multi-cloud strategies
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:
- Lead by example, prioritize data in decision-making.
- Provide access to data and analytics tools.
- Foster data literacy through training and workshops.
- Encourage data sharing and collaboration.
- Recognize and reward data-driven achievements.
- Continuously communicate the value of data-driven insights.
- Cloud Data Warehouse: You can start with Amazon Redshift, Google BigQuery, or Snowflake for scalable data storage and compute for data processing.
- ELT Tools: You can use ELT tools like Apache NiFi, Talend, or Apache Spark for simple data integration.
- Data Streaming: Implement Apache Kafka or Apache Flink for real-time data ingestion.
- Data Visualization and Analytics: Leverage Tableau, Power BI, or Looker for insights and Python with Pandas, NumPy, or machine learning for advanced analytics.
- 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:
- Define your goals and requirements.
- Assess your data needs, scalability, and budget.
- Consider compatibility, performance, and security.
- Prioritize user-friendliness and support.
- Conduct a proof of concept.
- 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:
- Define clear business objectives.
- Focus on actionable insights that drive decisions.
- Continuously measure and evaluate ROI.
- Optimize analytics processes for efficiency.
- Invest in training and upskilling for your team.
- Stay agile and adapt to changing business needs.
- EMBED valuable analytics apps into your product to monetise your data with your customers and partners.
To visualize and share data effectively:
- Use a BI tool to create interactive dashboards.
- Create clear, audience-specific visuals.
- Ensure data accuracy and privacy to build trust in data.
- Leverage collaboration tools and seek feedback for improvement.
- Share via email, direct sharing in the warehouse, or embeds in your applications.
- 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:
- Use cloud services for elastic scaling.
- Opt for scalable data warehouses like Snowflake.
- Implement containerized applications with Kubernetes.
- Monitor performance and scale resources as needed.
If analytics queries strain your production databases too much:
- Consider query optimization techniques.
- If no success, implement a data warehouse for analytics.
- Use ETL processes to offload data overnight.
- 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:
- Stay informed about new regulations.
- Conduct regular data audits and risk assessments.
- Implement strong data encryption and access controls.
- Appoint a Data Protection Officer (DPO) if required.
- Document data processing activities for compliance.
- Keep a data breach response plan ready.
- Train employees on data privacy practices.