Build data products into the heart of your strategy

Our mission is to help accelerate your digital journey through executive data advisory and AI-As-A-Service

Data Product Strategy

The  strategy determines how an organization plans to benefit from data and AI, positions itself in the new competitive landscape and how it wants to get there. 

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Data Product Design

Data product ideas need to be identified and then designed and validated to ideally directly support the implementation of the strategy.

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Data Product Delivery

During data product delivery comes the time to turn great data product ideas into reality using an agile implementation approach.

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Data Ops and Scaling

Each data product, including the underlying machine learning models and infrastructure, needs to be closely monitored, supported and enhanced.

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Why should you waste time experimenting?

Our mission is to help accelerate your data and AI journey. Through a decade of experience we gathered in other organizations, we know exactly what works and how to approach things the best way.

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We support you in setting up the business strategy, organization and roadmap for data and AI

We support you in making the right technological and architectural choices

We support you in delivering data products faster and with better results including MLOps

We are not in the business of selling trends or fads, everything we do creates tangible and measurable value

We design sustainable solutions with a focus on compliance, security, data protection, ethics and quality 

We have done this many times in many organizations successfully before – you can trust us to show you the way

Our value proposition

Data Product Strategy

A strategy and vision for driving digital transformation through data and AI revises the current strategy and vision of the company and readjusts it to the new realities in the industry. It addresses the four distinct perspectives (strategy, product, capabilities and transformation).


How is your industry affected through data and AI? Does your current business model and corporate strategy need to be adapted? What should be key metrics?


Which capabilities and technologies are needed to deliver the vision and strategy? Is the current architecture and organization able to deliver it?


In which business areas and for which purposes should data products be designed and developed to best support the vision and strategy?


What are the business changes needed to implement the strategy and vision? How can the transformation of the company be managed? What are the steps on the way?

Data Product Design

The product strategy sets the focus for where data and AI products will contribute to the company’s success. Now, it is all about designing data products to implement the data product strategy.

Product features

The core of a data product is actually defined by the set of product features that the data product should contain to deliver the expected functionalities that lead to the desired business impact and user experience.  Limit the amount of product features to be built in the first iteration of the data product to ensure fast implementation and testing in a real life environment.

User experience

While it is important to keep the overall goal of the product in mind, the user should be in the center of the actual product design. Setting the personas and user journeys builds a solid foundation to start with the more detailed product design which lays out the user interface. For each created persona, there should be a user journey that describes each interaction the persona has with the product.

Business impact

Assessing the business impact and calculating the business case of the data product by contrasting the economic gains with the efforts to develop, deploy, operate and maintain the data product. Show how data product affect business outcomes measured by OKRs set by the data product strategy.

Legal and ethics

While laws and regulations provide quite strict boundaries, ethical aspects of data and AI are much less clear.  Each company needs to position themselves. Unethical behavior can in extreme cases be really bad for the business, as customers and stakeholders might turn away from the company and its products.

Solution architecture

Defining the requirements for data transformation and algorithm design, the data needed and data sources available and an initial draft of a solution architecture for the data product. This can involve getting first data extracts and start checking the data suitability for the data product to quickly evaluate the general feasibility of the data product.

Rapid validation

Quickly building a prototype that can be tested, in particular, the quality of the model and the user experience. It is better to build a prototype of the data product fast and possibly determine after the validation phase to cease investing into it rather than to spend a long time developing the MVP and discovering that it cannot be used after all.


Data Product Delivery

The actual data product development takes place in a series of agile sprints of the data product squad team with regular reviews with the stakeholders. All software code including code for infrastructure and deployment has to follow coding, documenting and testing standards to ensure maintainability, scalability, security, reliability, adaptiveness and user acceptance.


MVP Scope

The scope of the MVP needs to be further refined by creating epics, user stories, business and engineering requirements and acceptance criteria

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Technical Design

The architecture, data model, machine learning model, system interfaces, deployment and operations processes of the data product need to be defined

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A microservices architecture approach should be applied whenever possible by creating many software components rather than one big monolith

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Continuous Delivery and Integration

DevOps principles are adhered to, which ensure the smooth integration of Development and Operations, including the continuous integration and continuous delivery of software code

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Best practice

Data and ML Ops

The machine learning model needs to be supplied regularly or even constantly with fresh data, which is like oxygen for a data product. The data pipelines are the blood veins that transport the oxygen that need to be monitored and quality controlled 24/7 by building software scripts for data quality profiling with business rules and anomaly detection. The monitoring should provide warnings if data is not loaded correctly, data formats do not match the expectations, the content of data appears to be unusual. Machine learning models need to be re-trained with new data regularly to ensure that they capture systematic changes and incorporates new features or business logic adaptations (such as the rise of the electric vehicles in the automotive sector). Monitoring also needs to be applied to the entire software code and infrastructure to ensure a smooth operation and fast mitigation of problems.

Best practice

Scaling data products

Data products can often be scaled and rolled out to other business scopes to increase business value, which is done in the data product scaling phase. This would include the adaptation of data products to other parts of the business and expanding the functionality of data products to solve more business problems in the same business domain. Scaling a data product to a new scope usually requires an adaptation of the data product to the new scope. For example, data models and the business logic might be different in another production plant or market, and, hence, the model features, machine learning models and data preprocessing pipelines have to be changed accordingly to make the data product product usable in the new scope. In many cases, there are substantial synergies when scaling data products and, therefore, value can be generated faster, eg when features and target variables for machine learning models are the same.


Dr. Alexander Borek is an experienced data and AI leader, author and keynoter and supported many tech and established companies on their data journeys, in particular, with regards to strategy, roadmap, product portfolio and architecture. He brings more than a decade of experience in successfully establishing 15+ global data platforms and organizations and developing and scaling >50 impactful machine learning and analytics products.

In his previous role, he served as the Global Head of Data, Analytics & AI at Volkswagen Financial Services, which provides the financial products for the Automotive Brands of the Volkswagen Group. He ran an internationally operating unit consisting of ML experts, data scientists, cloud architects, front and backend developers, data engineers, product managers and UX designers to develop and operate high-end data products.

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Our books

Covering all data topics

Everything on data products

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Everything on data architecture and machine learning

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A risk approach to data governance and data quality

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The impact of ML and cognitive computing on marketing

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Automated pricing

Check it out

Central data platform


Predictive mobility


Smart logistics

Supporting a leading logistics tech company in their data transformation journey


Design and delivery of data products for pricing and network optimization


Setting up autonomous mission driven cross functional product teams with OKR steering


Implementation of cloud based data platform and data governance


Rolling out central analytics tool and dashboards to all functions and across markets

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