Data Science Consulting

We offer advice in all areas of data science with the aim of empowering our customers to use this complex technology. We have bring to the table many years of experience with different data science technologies and tested these in various areas of application.

Our Services

With our consultation services, we support you in your specific uses cases. Our experienced experts can assist you in developing data-driven solutions for your company. Here, we differentiate between projects in which develop a tailor-made solution for you, and projects in which data science expertise is to be built in your organisation. In either case, our consultants are  happy to help: we provide solutions ready for implementation as well as carry out knowledge transfer projects, in which we train your employees.

Custom Business Solutions

We design customized solutions for your data science projects.

We design customized solutions for your data science projects.

Custom Buiness Solutions

After the use cases have been identified, we implement your ideas in constant contact with end users, domain experts, and decision-makers. This way we ensure successful integration of the data science solution into your business processes.
We take care of all the necessary steps for you: from data preparation and modelling and evaluation right through to deployment and maintenance. Depending on the problem at hand, greater benefit might be achieved with an application if it can access a larger data pool – which is why we pay particular attention to the future scalability and maintainability of infrastructure and software when designing your comprehensive solution.

In order to be able to create elegant solutions even in the case of special challenges and prevent breakage of the tool chain, our development team supports us with project-specific developments.

Knowledge Transfer Projects

Our advisors are experienced trainers and can help you build in-house expertise in the area of data science.

Our advisors are experienced trainers and can help you build in-house expertise in the area of data science.

Knowledge Transfer Projects

Our consultants are experienced in training and can support you in building in-house knowledge. Supervised by us, you will build up the necessary competence for your experts to independently design data science solutions in the future.
The knowledge transfer projects can be embedded in our program for establishing data science in companies: They build on our training courses and deepen this knowledge using a practical example in the context of your company. By successfully implementing your first projects, you will gain experience from real challenges and your employees will be ideally prepared for problems of a more complex nature.
The consultant learns about your company's internal processes during the knowledge transfer project and will be there to provide you with specific assistance and further suggestions via the Data Science Expert Support.

Proven Strategies

Get an overview of our proven strategies. We will support you in your data science project with best practices and established standards.

Collaborative CRISP-DM

We carry out our projects with the agile manner necesssary for the field of data science and largely follow the generally customary CRISP-DM Standard (Cross Industry Standard Process for Data Mining). However, since CRISP-DM only looks at the role of the data scientist and the success of a project significantly depends on the integration of the results into company processes and corporate culture, we have extended the standard to Collaborative CRISP-DM. It maps the entire process of introducing data science to organisations, includes end users as well as domain experts better and thus ensures data science solutions are integrated in existing business processes.

Click on the individual phases to get a more detailed explanation of the terminology. The area within the blue circle shows the regular CRISP-DM, the phases outside of it are our extensions.

image/svg+xmlUse CaseIdentification DataProviding InfrastructureIdentification BusinessUnderstanding DataUnderstanding DataPreparation Modeling Deployment Evaluation Gain Acceptance MonitorAcceptance

Collaborative CRISP-DM: Our Extension of the proven standard for data science projects.

Further Questions?

Feel free to contact us!

Get an overview of our training opportunities.

Our experts support you even after the project has ended.

Are you wondering what possible use cases could work for your company?

Project Management – Use Case Identification

Identification and evaluation of use cases play a key factor in the successful introduction of data science to your business. The first challenge here is to spread the Predictive Mindset among the employees in order to enable them to see the new possibilities that the implementation of data science will bring. The next step is to select the most promising projects, i.e. those that have the best cost-benefit assessment, simple infrastructure, and are most likely to succeed.

When establishing data science in your company, several use cases should be considered, as it is difficult to draw conclusions and make decisions about the technology’s applicability from just one example. Using several, it is possible to learn from failures and experience first-hand successes as positive reinforcement, generating a learning curve.

 

Project Management – Data Providing

Once a use case has been determined, the collaborative process between data scientist and professional experts begins. They will select data sets that seem most useful in finding a solution. This generates an overall picture showing what possibly relevant information is available. The data sets are assessed in regards to presumed relevance, accessibility and complexity of analysis. It is the management’s task to ensure that other departments can and do help by providing semantics and access to the data.

 

Project Management – Infrastructure Identification

Depending on the size and nature of the problem and the identified data, the infrastructure is chosen to support an efficient analysis while minimizing the necessary infrastructural effort. This includes the infrastructure for storing the data as well as for processing it.

For complex problems where the quality of the analysis is unpredictable, it is possible to divide the analysis into several phases, each using different infrastructures. The phases are divided in a way that will produce the best possible results on least cost and effort, and that enables easy data migration to the next, more complex infrastructure.

 

Data Science Loop – Business Understanding

During this initial phase of the data science part of the project, the data scientist focuses on understanding the project objectives and requirements from a business perspective. What is the economic challenge and what should the solution take into account? What is required to be able to integrate the solution into business processes?

From these questions, the data scientist forms a first outline on how to reach the project’s goals. During this phase, the essential quality criteria for a successful project outcome as well as the deployment prospects of the project are determined in coordination with representatives of the management.

Data Science Loop – Data Preparation

After understanding the data, the data scientist goes through the processing steps necessary to generate suitable situational profiles from the raw data that can be used to train predictive models. The data preparation is adapted according to the applied model, meaning it is carried out repeatedly. It comprises data and feature selection as well as transformation, aggregation and editing data.

 

Data Science Loop – Modeling

In this phase, various modeling techniques are applied and tested and their parameters are calibrated to optimal values. Some techniques have specific requirements on the form of data, therefore, stepping back to the data preparation phase is often necessary.

 

Data Science Loop – Evaluation

At this stage in the project, a model that appears to have high quality has been found. Before proceeding to the final deployment of the model, it is important to more thoroughly evaluate the model and its construction and to test models on independent data not used during training, in order to ensure it will achieve the expected quality and is able to indeed solve the initial business problems.

 

Data Science Loop – Deployment

The deployment of a project will integrate the project results into the business operations. Without a deployment, the project will not generate any use and must be deemed a failure. Deployment can be as simple as a regular scoring, web service deployment or a more complex big data deployment. This also covers possible end user interfaces for controlling the underlying data science algorithms.

 

Social Integration – Gain Acceptance

A solution is helpful only when put into practice. As employing data science technologies requires the fundamental change in thinking described with the Predictive Mindset, they are often rejected. In this phase, end users are enabled through training to actively participate in the project, bring forth their own ideas, and learn to trust the technologies, in short establishing the Predictive Mindset. Most importantly, it covers the practical integration into the end users’ work processes. In order to avoid rejection it is essential to leave the end user in control.

 

Social Integration – Monitor Acceptance

The deployment does not end the data science project. On the one hand, the predictions can always be improved in many aspects, as is shown in the data science loop, on the other hand, the end users gain more and more experience with the technology, enabling them to refine the original Uses Cases. Often this leads to adjusting the specifications for the integration into work processes as well as for the quality criteria for the predictions. Therefore, the end users will still need a data scientist’s guidance and support while the data scientist needs the domain expertise to provide new use cases.

 

Data Science Loop – Data Understanding

In this phase, the data scientist gets acquainted with the data provided and, if necessary, uncover data quality problems. At the same time, the data scientist can test his understanding of the business’ problems, assessing them with the received data. This interpretation of the data can often lead to an additional improvement in the understanding of the processes involved, on part of the data scientist and often of other stakeholders involved.