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Data Analytics and Analytics Engineering

Realize the value of your data

Modernize your analytics capabilities and democratize data usage throughout your organization.

Your data needs to be accurate, accessible, and actionable

While the potential of data is widely recognized, many organizations struggle to use it efficiently. Some of the hurdles they encounter are limited availability for end-users, a lack of data literacy throughout the organization, and poor data quality. As a result, data does not reach the right people at the right time, limiting the company’s ability to generate business value

We can boost your analytical capabilities by leveraging cloud technologies, developing robust data products, and training your analytics teams. In particular, we will:

  • Increase the availability of data in your organization by facilitating its discovery, usage, and distribution. 
  • Implement data observability (discovery, lineage, monitoring, alerting, quality metrics) to restore trust in data. 
  • Bring software engineering best practices towards analytics such as testing, version control, and CI/CD. 
  • Introduce Self-Service Analytics for end-users to extract information, make decisions, and uncover opportunities directly.
  • Accelerate data democratization in your organization. 


Whitepaper: Data Democratization

Learn why organizations fail to turn data into business value 
"The biggest bottleneck used to be data availability. Now it’s the quality and usability of our data."
Sacha Roggeveen, Head of Data and Analytics,

What it takes to translate your strategy into business solutions

Your processes with regards to AI solutions are as important as building them and getting it into production. Ideation workshops, AI literacy trainings, product ownership and management of enterprise-scale projects it's all important. To do this you need the right analytical capabilities, workflows and be ready to scale. All this, with the right foundation in place: A modern data stack and data warehouse.

  • Develop business capabilities to discover and prioritize AI use cases
  • Maninging the AI products and leveling up AI-literacy
  • Developing the right analytics capabilities
  • Setting up the foundations with a modern data stack & data warehouse
Realize the value of your data today

Our Data Analytics & Analytics Engineering services

Data modeling and warehousing

  • Design, organize and document your data models. Improve data discoverability and database performance, while remaining flexible to business change. 
  • Choose and build the right data model for the right use case. 
  • Build a data warehouse or lakehouse that serves as the Single Source of Truth for all data applications. 
  • Scale up an existing data warehouse or migrate to the cloud. 
  • Automate Transformation Pipelines, meeting the Service Level Agreement and orchestrating the flow to get relevant data to end users when they need it. 

Data collection

  • Track user behavior. Optimize customer experiences through data insights. 
  • Integrate and centralize all data sources, ensuring a comprehensive collection of user data.  
  • Collect high quality behavioral data that powers your analytics and AI use cases. Update your models with new upcoming data.  
  • Adapt to the uniqueness of your business while ensuring compliance with new and upcoming privacy laws.  
  • Modify data collection by focusing on relevant models for your company.  

Modern Data Stack

  • Reduce the complexity of setting up and tuning a data platform by choosing tools that are easy to implement, maintain, and leverage.  
  • Lower technical barriers by using managed services. Use tools like Airbyte and Fivetran to enable seamless data integration from various sources, automate data pipeline setup and maintenance, and leverage pre-built connectors. 
  • Achieve scalability, flexibility, and high-performance analytics with cloud-based data warehouses like Databricks, Snowflake, and BigQuery. 
  • Deploy dbt to transform data, collaborate on data modeling, version control, and documentation. Automate testing, and scale data operations confidently. 

Data quality and observability

  • Implement monitoring solutions to track the health and performance of data pipelines, ensuring data flows smoothly and efficiently.  
  • Conduct comprehensive data profiling to assess the quality and completeness of your data, identifying issues such as missing values, outliers, and inconsistencies. 
  • Establish data governance policies to enforce data quality standards, define ownership, and ensure compliance with regulations. 
  • Track data lineage to understand transformations between sources and destinations. 


  • Speed up the building of data solutions. 
  • Upskill your professionals in DataOps. 
  • Connect your teams by implementing Agile best practices.  
  • Automate your data pipelines to increase speed and optimise performance.  

Data visualization and insights

  • Develop business intelligence solutions that consolidate KPIs and metrics, enabling stakeholders to monitor and track organizational performance. 
  • Design interactive dashboards to explore and analyze information effortlessly. 
  • Use analytics techniques to explore and analyze data, uncovering meaningful patterns, trends, and relationships. 
  • Assess your organization's needs to identify the most suitable BI tools, ensuring optimal functionality and scalability. 
  • Deploy a scalable and secure architecture for BI solutions, ensuring data privacy, cost efficiency, and high performance as your organization's data needs grow.
"Scaling up the benefits of data and analytics capabilities through the modern data stack also shifts new responsibilities to the platform team. Where previously data consumers with domain knowledge ensured data quality on demand through manual wrangling, today’s automated pipelines require savvy monitoring and new business processes for governance.
Kevin Duisters Commercial Analytics Manager, Interfood Group
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