Should you run LLMs locally?

The question is no longer how you technically unlock data, but how you organise your company to create structural value from it. During the DWH & BI Summit in March 2026, data leaders, architects and governance experts came together to discuss data mesh, data governance, data products and data modelling.

Machine learning (ML) is often treated as a modelling exercise. Pick an algorithm, train it, evaluate the metrics, deploy. In reality, the algorithm is one of the least important decisions you’ll make.

For professionals in data, analytics, martech and customer experience, Digital Velocity is one of the events where developments become tangible. It brings together practitioners, partners and industry leaders to show how they approach AI, real-time data and customer experience in practice.

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.

Artificial Intelligence is transforming how you design, build, and maintain digital solutions. From code generation to data pipeline automation, AI has become a trusted companion in technical workflows.

When the regular Analytics Translator of a service organisation went on maternity leave, the team sought our help to ensure ongoing AI projects ran smoothly. At the same time, the organisation wanted a fresh, external perspective: how was the role being filled, and where could improvements be made?

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.

Analysing hundreds or thousands of open answers from surveys, interviews or reviews is time-consuming. To better understand those answers, we group them into themes (for example: ease of use, service, delivery or reliability). At Digital Power, we use Large Language Models (LLMs) to quickly categorise high volumes of open answers. Our team built our own secure, transparent tool that lets us see exactly what happens under the hood. But is AI already advanced enough to replace our human researchers?

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.

If you’ve decided to migrate your data warehouse to a European environment, taking a structured approach is key. This blog focuses on the steps needed for a smooth and successful transition.

This document is intended to provide guidance during the migration of legacy data warehouses or databases to modern Lakehouse solutions such as Databricks and Snowflake. It describes the different steps that are needed for a structured migration process. Migrations are often complex processes that require careful planning and execution to ensure a smooth transition.

Due to geopolitical developments and growing concerns about data sovereignty, more and more businesses are looking to reduce their dependence on US cloud providers.

In an era marked by international tensions, rising concerns about privacy, security, and technological sovereignty, you may find yourself re-evaluating your reliance on US technology solutions. The drive towards digital independence isn't just a political goal, but may also be a pragmatic business necessity.

Artificial Intelligence (AI) is changing the way organisations operate, from personalised customer experiences to automated or assisted decision making, AI helps your organisation leverage your data. However, navigating through this fast-evolving field can feel overwhelming, with terms like AI, Generative AI (GenAI) & Machine Learning (ML) often causing confusion.

With the ongoing developments in the data and AI industry, the hype around AI agents has no signs of slowing down. Jensen Huang, Nvidia CEO, is a strong proponent of AI agents, envisioning a multi-trillion-dollar opportunity, where agents can perform tasks with a high degree of autonomy and revolutionize how people work and how businesses operate. So, in this article we would like to discuss what exactly AI agents are, what are their main components, how they interact together and the basics on how to build one.

In this two-part article series, we explore the importance of data governance and offer a practical guide for implementation within your organisation. In the first article, we explained why you can’t afford to delay. Now, we break down how to get started, step by step.

In an era where data lies at the heart of business operations and innovation, postponing data governance is unwise. This article highlights why it’s crucial to take action now to gain control over your data, minimise risks, and achieve a competitive advantage.

Many organisations are feeling the pressure: AI suddenly seems to be high on the agenda everywhere. New tools are following each other at lightning speed, colleagues are experimenting with ChatGPT, customers are asking smarter questions, and in the media, the word “AI” is unmissable. It feels like a train has taken off, and no one wants to be left behind.

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.

Are you increasingly working in the cloud? Then it’s time to think about governance. With clear agreements and controls, you stay in control of your cloud environment – from costs and security to compliance. That way, you avoid security issues, unnecessary spending or falling short of compliance requirements.

So you have a data warehouse and developed models proven to generate valuable inferences, but the business impact is not there yet? In this webinar we will show how the right framework can activate these models within minutes to continuously deliver up-to-date predictions. This is the central objective of MLOps.

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?

Whether you work for a major fashion brand, supermarket, or in the travel industry, leveraging your customer data to personalise customer experiences is crucial to your success. But it's not easy to achieve. Like most B2C companies, airlines are swimming in customer data from dozens of different places, struggling with data quality, privacy compliance, and real-time personalisation.

More and more companies are running into the limitations of traditional Customer Data Platforms (CDPs): they lack flexibility, struggle to import and export data, and find it difficult to comply with strict privacy regulations.

To truly work in a customer-centric way, a flexible and powerful tech stack is essential. Customers expect relevant, personalised interactions at the right time and through the right channel. With the right technologies, you ensure every customer feels understood while optimising your marketing efforts.

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.

Since the launch of ChatGPT, an increasing number of organisations have been exploring the question: "How can we apply AI within our organisation?" At this hotel chain as well, employees have already been using AI applications on their own initiative and recognise the potential to scale their use further. They sought pragmatic AI applications tailored to their domain and an approach focused on creating business value. The hotel chain engaged with multiple partners and ultimately chose to work with us. Our pragmatic approach was the decisive factor in their decision.

As the usage of data in organisations becomes ubiquitous, the need to keep control over your data is becoming increasingly important. Gaining control over your data is achieved through effective data governance. However, many people struggle to figure out what data governance encompasses exactly and how to start implementing this at their organisation. This article aims to give you an overview of the crucial components of data governance and how to introduce them at your organisation.

In the world of machine learning, working with large datasets and complex models can quickly become time-consuming and resource-intensive. To speed up this process, parallelisation becomes crucial. This technique involves breaking down tasks into smaller subtasks that can be processed simultaneously across multiple CPU cores or distributed machines within a cluster. By spreading out the workload, you can achieve faster and more efficient data processing on a large scale.

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.

The internet provides a massive amount of interesting social media posts, likes and shares. A wealth of information, especially for organisations wanting to make more impact online. But where do you start? What data will you collect, how will you analyze it and how can you convert insights into concrete action points? To answer these questions, it is important to start with the mission and goals of the organization.

In the ever-changing world of Python, managing packages, environments and versions efficiently is important. Traditional tools like pip and conda have served us well, but as projects become more complex, so do our requirements. This guide looks at modern alternatives - Poetry, PDM, Hatch and Rye - each of which offers unique capabilities to streamline Python project management.

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.

Years ago, you couldn't develop an application or process without knowledge of complex programming languages like Javascript, PHP, and Python. You needed a programmer or Data Engineer. Today, there is a shortage of technical experts, while more and more low-code solutions are appearing on the market. These tools allow you to get started without in-depth technical knowledge. Whether this is the right solution for you depends on various factors. Make the right decision with the help of this article.

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.

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.

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.

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.

Artificial Intelligence seems capable of everything, and sometimes even better and faster than what we can do ourselves. Analysing qualitative data is a time-consuming task, and as researchers, we are curious if it can be done faster and easier. Does AI offer a solution for this? Our researchers investigated.

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.

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.

In the past, all data from companies was stored locally in an on-premise environment. More and more companies are migrating their data infrastructure to the cloud. Cloud computing utilises servers managed and maintained by cloud service providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. In this article, you will read the answers to the questions you may have when considering a migration to the cloud.

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.

In this webinar, we discuss the maturity model that we apply to quantify the maturity of different dimensions of data governance. Additionally, we provide concrete steps and implementation tips to start providing added value through data management.

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.

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.

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.

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?

FrieslandCampina Global facilitates local teams in Europe, Asia, and Africa. They want to gain a better understanding of the market and provide the teams with new insights. The goals are to strengthen their competitive position and to identify new opportunities for expansion.

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.

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.

For developing modern data applications, a Data Engineer is essential. But what does it actually mean to be a Data Engineer and what exactly do you do? Our colleague Oskar, Data Engineer at Digital Power, explains.

In this video, you will find out what a job as a Data Engineer looks like! What does a working week look like, which clients do our Data Engineers work for and what makes working so much fun? Dennis likes to tell you more about it!

Everywhere at events and online, stories are told about what 'data science' is all about. Definitions are anything but consistent. They go from 'getting something of value out of data' to 'it's basically the same as statistics'. And a Data Scientist is 'a data analyst who lives in Silicon Valley' or 'a socially skilled IT person who does something with data'. But what is it really?

In this video, you'll discover what a job as a Data Analyst looks like! What does a working week look like, which clients do our Data Analysts work for and what makes the job so fun? Dennis is happy to tell you more about it!

In this video, you will find out what a job as a Data Engineer looks like! What does a working week look like, which clients do our Data Engineers work for and what makes working so much fun? Oskar likes to tell you more about it!

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.

A few years ago, the job title didn't even exist: Data Engineer. Nowadays, there is a high demand for Data Engineers. Almost every organisation consciously collects data, and the realisation that this must be done in a structured way is growing. If the data you collect is not well organised and correct, you cannot use it as input for making good decisions. Data Engineers build infrastructures that process data. Therefore, they are indispensable to organisations that want to collect and apply their data in a structured way.

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.

How do you find out what you want, and what do you look for in job vacancies? Will you opt for a large company, a small company, a consultancy or something else? These are some of the questions that our graduation intern Stijn had to deal with. He had a discussion with his colleagues to get answers to these questions. The result? The Data Choice Cast! In this podcast, Stijn asks all his pressing questions and receives tips that help him (and hopefully you, too) to make the right choice in choosing a job in the data world.

On February 10, 2022, the Digital Power Datahub and the Partos Digital Lab together kicked off the Data Awareness series with the Intro to Data Awareness. This series of 6 training courses develops the Datahub especially for the members of Partos; non-profits in the development cooperation industry. The aim of the series is to make development cooperation specialists data-wise, so that they can make and measure more impact.

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.

You are almost done with your studies and looking for an employer that offers you the opportunity to learn everything about the field of data. Or you are no longer challenged in your current position and would like to become more technical. In both cases, you do not want to follow unpaid courses, but you would like to get started as soon as possible for real customers, with a serious salary. Does this sound familiar? Then these data traineeships are really something for you.

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.

The outbreak of the corona pandemic in early 2020 has turned the world upside down. In addition to countless infections, hospitalisations and deaths, we also saw an outbreak of violence in many countries. Citizens took to the streets, sometimes violently, to protest against the measures taken, but domestic violence also increased in many places and fear and frustration played a role in racism.

At the 'MKB Data Science Top 50', 50 agencies competed for the title 'the fastest-growing SME data science agency in our country'. Even before the event, we heard that we were in the top 3! During the Den Bosch Data Week, Marieke got to pitch our organisation.

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.

In June 2020, Sander became part of our team. Although he started in the middle of corona time, he soon noticed that he was greatly stimulated to make contact with his new colleagues. This largely came naturally as part of our onboarding program: "This matched perfectly to my needs: I started calling many colleagues myself to get acquainted! "Read how Sander designs his own training as a data engineer."

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.

Vesteda was curious if social listening – monitoring and analysing social media discussions about a brand, competitors, products or hashtags/keywords – could add value to the organisation. To this end, we started a project that consisted of two parts: exploring possibilities for social listening in the Corporate Communication department and applying social listening in an ongoing Data Science project.

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.

Tuesday, 3 March 2020, was known as Super Tuesday, the day on which several American states vote simultaneously for the Democratic presidential candidate. We use this day as a case for the application of Social Network Analysis. This example is about elections, but you can also apply the same method to a commercial case where you replace the names of the candidates with, for example, different brand names.

The Corona measures are a much discussed topic on Twitter. The crisis team not only fights against Corona's effects on public health, but also tries to maintain legitimacy for the decision to keep certain measures in place among the public. With this practical case we explain how you can make public opinion on Twitter transparent with Social Network Analysis.

If your organisation is active on social media and you want to optimise the online strategy, you need to know what is happening online around you and the impact of your activities. Social Network Analysis can help you with that. We explain what it is, how it works and the purposes it serves.

RNW Media builds online communities in countries with limited freedoms. In these communities, young people can read and discuss sexual and reproductive health and rights (SRHR) and civil rights. In addition, RNW Media is working on advocacy – putting the interests of young people on the map with governments.

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).

More and more organisations are getting started with data science. A logical consequence of this is clearly a growing number of related vacancies. But how do you set up a useful job description for a data scientist – and mostly: how do you actually pick the right one? We're giving you some hints on what to do, and what not.

The introduction of the new privacy law (GDPR) in 2018 has ensured that many organisations put privacy high on the agenda. In this article you can read about the 5 ethical risks of working digitally and using data. We also share a concrete solution: the Responsible Data Framework.

Residential investor Vesteda is working on a new website. If an available rental home has a garden, the location of the garden must be listed on the webpage of that home. This information was not yet available in the database. We were instructed to determine the location of the garden based on the coordinates of the homes.

Organisations wanting to get started with data quickly ask for Data Science solutions. Data Science is often seen as the holy grail of data-driven working. But what does a successful Data Science project actually look like in practice? And how can it serve your organisation? In this series of articles, we take you through all the elements you need to achieve success for your organisation with Data Science.

Fair Wear is a non-profit organisation that aims to improve the working conditions of employees in garment factories. The NGO has collected a lot of documentation about its activities in recent years, for example in the form of reports from a complaint line for factory employees, reports of audits that check whether factories comply with the guidelines, and reports of training for factory employees. This information is stored as typed text, usually in Word or PDF format.

An increasing number of people are traveling by public transportation. Several stations in The Netherlands are being rebuilt or renovated to keep up with the growing number of train passengers. For the rebuilding and layout plans, information was needed on station traffic. NS Stations also wanted to improve transfer safety in collaboration with ProRail.

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.