Your Data Engineering partner

Dedimo wanted to explore how AI could help automatically transcribe therapy sessions between client and therapist and generate reports.

Transavia operates in a highly competitive travel market where customers expect personalised and consistent communication across every touchpoint. Whether on the website, in the app or via email, each interaction needs to reflect customer behaviour and preferences.

Heerema Marine Contractors operates the world’s largest crane vessels, equipped with many sensors that together generate millions of measurements every day. This sensor data is critical for safer operations, lower emissions, better engineering and well-founded investment decisions.

Volero.nl is a young and fast-growing company that sells rugs through a webshop and a physical store. The company is primarily active in the Netherlands but is growing rapidly across Europe, including Belgium, Germany and Poland. To support this growth, it is essential for Volero to work in a data-driven way.

At energy company Ennatuurlijk, the belief grew that intuition was no longer enough to set the course. The energy market was changing rapidly, the organization was growing, and the amount of information was increasing every day. IT Manager Eric Vanderfeesten went looking for a data partner who could not only provide strategic advice on data-driven work, but could also strengthen his data team operationally. In this interview, he shares his vision, experience and results from working with Digital Power.

An innovative start-up in the baby care sector aimed to work in a data-driven way in order to gain valuable insights and enable strategic growth. They engaged our support to help realise this ambition.

Ennatuurlijk supplies sustainable heat and cold via heat networks to consumers and businesses. The internal Data & Analytics team is tasked with making the organisation data-driven. In doing so, they ran into a challenge: the many requests for data products within the organisation were difficult to manage and the impact remained limited. The management team therefore asked us to help them develop a data strategy, create a future-proof data landscape and drive a data-driven mindset within the organisation.

In September 2023, Transavia asked us to evaluate their Customer Data Platform (CDP): did it still align with their marketing objectives, and was it future-proof considering the stricter regulations around third-party cookies?

After we built a data warehouse for Meerlanden, their data scientist began working with the data. We proposed setting up a Machine Learning Operations (MLOps) framework together, allowing them to integrate their models directly into the existing environment. This enabled them to make predictions that improved the efficiency of Meerlanden’s services.

Talent Garden provides masterclasses and training programs to students, engaging with them through various online and offline touchpoints. Online interactions include completed contact forms and information requests, while offline touchpoints involve meetings and calls with Talent Garden’s sales team. Throughout the customer journey, from initial contact to final enrollment, Talent Garden collects extensive data*. With a wealth of raw data at their disposal, they sought to improve their enrollment strategy and the effectiveness of their sales team. To achieve this, they asked us to develop a data model that could better predict the likelihood of a new contact eventually becoming a student.

A global asset manager, specialising in Quant and Sustainable Investing, offers a range of investment strategies, including equities and bonds. To strengthen their competitive position and proactively respond to changing client needs and market developments, the sales and marketing department aimed to adopt a more data-driven approach.

A digital agency develops and manages various websites and analyses their performance using Google Analytics, sharing the results with clients via dashboards. However, the transition from Universal Analytics to GA4 presented challenges because the data structure in GA4 is different, causing the existing dashboards to stop functioning. The agency asked us to help devise a scalable and future-proof solution that would work for all of their clients.

Rapidly growing scale-up EnergyZero needed to expand and establish a strong data team due to their extreme growth. The primary data need was to support and conduct the financial analysis for an upcoming audit. Additionally, they wanted to automate work processes and improve data exchange with B2B partners.

Financial institutions need to process large amounts of documentation. For this particular institution, an internal team facilitates this by, for example, creating summaries using text analysis and natural language processing (NLP). They make these available to the various business units. To conduct audits more efficiently, they wanted to develop a question-and-answer model to get the right information to them faster. When ChatGPT was launched, they asked us to create a proof of concept.

The Kadaster manages complex (geo)data, including all real estate in the Netherlands. All data is stored and processed using an on-premise data warehouse in Postgres. They rely on an IT partner for maintaining this warehouse. The Kadaster aims to save costs and work more efficiently by migrating to a Databricks environment. They asked us to assist in implementing this data lakehouse in the Microsoft Azure Cloud.

Merlin is the largest digital music licensing partner for independent labels, distributors, and other rightsholders. Merlin’s members represent 15% of the global recorded music market. The company has deals in place with Apple, Facebook, Spotify, YouTube, and 40 other innovative digital platforms around the world for its’ member’s recordings. The Merlin team tracks payments and usage reports from digital partners while ensuring that their members are paid and reported to accurately, efficiently, and consistently.

An essential part of Otis's business operations is the maintenance of their elevators. To time this effectively and proactively inform customers about the status of their elevator, Otis wanted to implement continuous monitoring. They saw great potential in predictive maintenance and remote maintenance.

Agrico is a cooperative of potato growers. They cultivate potatoes for various purposes such as consumption and planting future crops. These potatoes are exported worldwide through various subsidiaries. All logistical and operational data is stored in their ERP system, Microsoft Dynamics 365. Due to the complexity of this system with its many features, the data is not suitable for direct use in reporting. Agrico asked us to help make their ERP data understandable and develop clear reports.

Our consultants log the assignments they take on for our clients in our ERP system AFAS. In our CRM system HubSpot, we can see all the information relevant before signing a collaboration agreement. When we close a deal, all the information from HubSpot automatically transfers to AFAS. So, HubSpot is mainly used for the process before entering a collaboration, while AFAS is used for the subsequent phase. To tighten our people's planning and improve our financial forecasts, we decided to set up a data warehouse to integrate data from both data sources.

One of the largest online shops in the Netherlands wanted to develop a standardised way of data processing within one of its data teams. All data was stored in the scalable cloud data warehouse Google BigQuery. Large amounts of data were available within this platform regarding orders, products, marketing, returns, customer cases and partners.

The National Road Traffic Data Portal (NDW) is a valuable resource for municipalities, provinces, and the national government to gain insight into traffic flows and improve infrastructure efficiency.

Valk Exclusief is a chain of 4-star+ hotels with 43 hotels in the Netherlands. The hotel chain wants to offer guests a personal experience, both in the hotel and online.

TM Forum, an alliance of over 850 global companies, engaged our company as a data partner to identify and solve data-related challenges.

Stichting Donateursbelangen aims to strengthen trust between donors and charities. They believe that that trust is based on collecting money honestly, openly, transparently and respectfully. At the same time effectively using the raised donation funds to make an impact. To further this goal, Stichting Donateursbelangen wants to share information about charities with donors through their own search engine.

Dedimo is a collaboration of five mental healthcare initiatives. In order to continuously enhance the quality of their care, they organize internal processes more efficiently. Therefore, they use perceptions from the data that is internally available. Previously, they acquired the data themselves from different source systems with ad hoc scripts. They requested our help to make this process more robust, efficient and to further professionalise it. They asked us to facilitate the central storage of their data, located in a cloud data warehouse. The goal was to set up the data infrastructure within this environment, since they were already used to working with Google Cloud Platform (GCP).

At Royal HaskoningDHV, the number of requests from customers with Data Engineering issues continue to climb. The new department they have set up for this, is growing. So they asked us to temporarily offer their Data Engineering team more capacity. One of the issues we offered help with involved the Aa en Maas Water Authority.

The Designathon Works foundation organises Design Hackathons (Designathons) for children aged 8 to 12. The target? Teaching children from all over the world skills to become a 'changemaker'. They are challenged to design solutions for a better world, for example to combat climate change. From the Datahub, we helped Designathon Works fine-tune the impact measurements free of charge. We also made a first move towards automating data collection, analysis and visualisation.

FysioHolland is an umbrella organisation for physiotherapists in the Netherlands. A central service team relieves therapists of additional work, so that they can mainly focus on providing the best care. In addition to organic growth, FysioHolland is connecting new practices to the organisation. Each of these has its own systems, work processes and treatment codes. This has made FysioHolland's data management large and complex.

The Neuron provides a programmatic bidding platform to plan, buy and manage digital Out-Of-Home ads in real-time. They asked us to predict the number of expected impressions for digital advertising on billboards in a scalable and efficient way.

RNW Media is an NGO that focuses on countries where there is limited freedom of expression. The organisation tries to make an impact through online channels such as social media and websites. To measure that impact, RNW Media drew up a Theory of Change (a kind of KPI framework for NGOs).

Publisher Malmberg collects a lot of offline and online data. More and more educational institutions are using online licenses in addition to (or instead of) printed teaching materials. To properly make use of this, Malmberg uses monthly reports. The in-house data team compiles these as input for specific departments. Malmberg asked us to strengthen this team and make the internal processes around data more efficient.

In the world of data and technology, Data Engineers and Machine Learning Engineers are crucial players. Both roles are essential for designing, building, and maintaining modern data infrastructures and advanced machine learning (ML) applications. In this blog, we focus specifically on the roles and responsibilities of a Data Engineer and Machine Learning Engineer.

With the increasing availability of cloud services that enable companies to leverage Large Language Models, it becomes relatively easy to setup your own GPT-model. However, one important question needs to be answered before you start building: what are the benefits for my organisation?

The AI Document Explorer (AIDE) is a cloud solution developed by Digital Power that utilises OpenAI's GPT model. It can be deployed to quickly gain insights into company documents. AIDE securely indexes your files, enabling you to ask questions about your own documents. Not only does it provide you with the answers you are looking for, but it also references the locations where these answers are found.

Based on our know-how, the purpose of this blog is to transmit our knowledge and experience to the community by describing guidelines for implementing a data platform in an organisation. We understand that the specific needs of every organisation are different, that they will have an impact on the technologies used and that a single architecture satisfying all of them makes no sense. So, in this blog we will keep it as general as we can.

There are many different forms of data storage. In practice, a (relational) database, a data warehouse, and a data lake are the most commonly used and often confused with each other. In this article, you will read about what they entail and how to use them.

Bringing machine learning models to production has proven to be a complex task in practice. MLOps assists organisations that want to develop and maintain models themselves in ensuring the quality and continuity. Read this article and get answers to the most frequently asked questions on this topic.

This article explains the essence of Kubernetes Event Driven Autoscaling (KEDA). Subsequently, we configure a local development environment enabling the demonstration of KEDA using Docker and Minikube. Following this, we expound upon the scenario that will be implemented to showcase KEDA, and we guide through each step of this scenario. By the end of the article, you will have a clear understanding of what KEDA entails and how they can personally implement an architecture with KEDA.

This article provides a comparison between these two managed services Cloud Composer & MWAA. This will help you understand the similarities, differences, and factors to consider when choosing them. Note that there are other good options when it comes to hosting a managed airflow implementation, such as the one offered by Microsoft Azure. The two being compared in this article are chosen due to my hands-on experience using both managed services and their respective ecosystems.

Data projects often need to deliver results quickly. The field is relatively new, and to gain support, it must first prove its value. As a result, many organisations build data solutions without giving much thought to their robustness, often overlooking data quality. What are the risks if your data quality is not in order, and how can you improve it? Find the answers to the key questions about data quality in this article.

Since the field of big data emerged, many elements of the modern data stack became the data engineers' responsibility. What are these elements, and how should you build your data team?

In the article, we start by discussing Serverless Functions. Then we demonstrate how to use Terraform files to simplify the process of deploying a target infrastructure, how to create a Function App in Azure, the use GitHub workflows to manage continuous integration and deployment, and how to use branching strategies to selectively deploy code changes to specific instances of Function Apps.

The world of data is continuously shifting and so are its corresponding jobs and responsibilities within data teams. With this, an up-and-coming role appeared on the horizon: the Analytics Engineer.

Infrastructure as Code has proven itself as a reliable technique for setting up platforms in the cloud. However, it does require an additional investment of time from the developers involved. In which cases does the extra effort pay off? Find out in this article.

It is now clear to most companies: data-driven decisions by Data Science add concrete value to business operations. Whether your goal is to build better marketing campaigns, perform preventive maintenance on your machines or fight fraud more effectively, there are applications for Data Science in every industry.

Basically, Data Engineers work on data pipelines. These are data processes that can retrieve data from a certain place and write it in somewhere. In this article you can read more about how data pipelines work and discover why they are so important for a solid data infrastructure.

Working in a data-driven way helps you make better decisions. The better your data quality, the more you can rely on it. A good data architecture is a basic ingredient for data-driven working. In this article, we explain what a data architecture is and what a Data Architect does.