OUR SERVICE
Your data science projects are in good hands with us.
Introduction
Do you need help in deciding, planning, embedding or applying your algorithms?
No problem! Take advantage of our services that are perfectly tailored to you! We support you with our expertise where you need it to be successful.
Strategy consulting
The goal: What are the greatest benefits of using data science in your organization? And how do you get there?
“Data science, artificial intelligence or machine learning: sounds nice, but what’s the point? Where do we need to start and why didn’t our pilot project work?”
If you are asking yourself these or similar questions, you are not alone! Many of our customers are at this point. This is exactly where our strategy consulting comes in: together with our clients, we analyze their current business goals, their organization and their (IT) processes and data. We then develop a data science strategy based on this analysis. This gives our customers clarity on where they should use data science, what it can do for them and how they should proceed in order to actually make the project a success. And the great thing is: the whole thing doesn’t take as long as it may sound now. We develop most strategies in just a few weeks.
Speaking in detail…
It all starts with the objective – in which area data science and automation will bring the greatest benefit, taking into account the company’s strategic goals. What weak points and pain points are there for the business, what operational trade-offs need to be considered? We work this out together with our customers, who contribute with their expertise from their own business. At this point, it is not yet a question of identifying the solutions, but rather the issues and priorities.
To ensure that the right solution is developed and used, we find it important to involve management, specialist departments and future users right from the start. We want to deliver a minimal, productively usable solution as soon as possible, which will be iteratively developed and adapted to changing requirements. The operational conditions must be taken into account and risks must be calculated, for example the accuracy that must be achieved before a model can be used in production. Depending on requirements and data-driven maturity, a recommendation system where the decision lies with a human, an AI that makes decisions and learns from them in real time, or a hybrid solution may be suitable.
Available data and the ability to generate or procure new data is an important feasibility criterion and another cornerstone for use case selection. But the use case discussion can also identify the need for digitization and inspire the integration of additional data sources. The secure and sensible recording of data enables automation and ensures audit compliance. Being data-driven also means being able to understand the information on which decisions were made.
The overall solution can consist of several components that operate the process together and can be brought into production step by step if necessary. A modular structure makes it possible to start with simple algorithms and a few influencing factors in order to replace them over time with more complex models and a higher degree of automation. Starting with the simplest possible algorithms has several advantages: low effort and susceptibility to errors, interpretability and the function as a baseline for comparison when trying out more complex algorithms.
Project management
Promoting interdisciplinary collaboration while keeping an eye on the market, the relevant stakeholders and the objectives? That works!
Nowadays, achieving goals works best in interdisciplinary teams. In projects, the various disciplines work together for a limited time on a limited goal. Successful project management is necessary to ensure that this can be done effectively and efficiently and that external impulses, such as market changes, can be absorbed at the same time.
In our projects, we often find that (pure) data science projects or projects with a high proportion of data science “tick” differently. This is mainly due to the fact that the discipline of data science cannot be precisely estimated and planned at all times due to its creative research work. However, it is precisely this creative research work that makes the difference and should therefore not be restricted at the wrong time.
There should also be basic data science skills and a good understanding of the work of data scientists in project management.
Our project managers fulfill this requirement: they generally have a basic mathematical education and additional training in project management. In their day-to-day work, they perform a variety of tasks such as planning sprints or projects, specifying requirements, leading team meetings or translating between mathematics and domain languages.
- Classic vs. agile
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The debate about traditional and agile project approaches is already outdated. In the vast majority of situations, we recommend an agile or at least hybrid approach. However, the misconception that an agile approach is synonymous with a lack of planning persists. Our project managers have already worked on classic and agile projects and can combine the best of both approaches thanks to this experience.
By the way, agile project management is not the same as Scrum. In addition to the well-known Scrum framework, there are various other process models that could be more suitable for your project.
- Project management goals
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The aim of project management is to organize and control the project and all those involved in terms of scope, budget and time. Depending on whether the project is carried out in a classic, agile or hybrid way, dimensions are fixed or still variable. The advantage for you is obvious: you get a well-founded organization of your project and thus the best possible professional result. You always have an overview of where the project currently stands and what challenges still need to be overcome.
The debate about traditional and agile project approaches is already outdated. In the vast majority of situations, we recommend an agile or at least hybrid approach. However, the misconception that an agile approach is synonymous with a lack of planning persists. Our project managers have already worked on classic and agile projects and can combine the best of both approaches thanks to this experience.
By the way, agile project management is not the same as Scrum. In addition to the well-known Scrum framework, there are various other process models that could be more suitable for your project.
The aim of project management is to organize and control the project and all those involved in terms of scope, budget and time. Depending on whether the project is carried out in a classic, agile or hybrid way, dimensions are fixed or still variable. The advantage for you is obvious: you get a well-founded organization of your project and thus the best possible professional result. You always have an overview of where the project currently stands and what challenges still need to be overcome.
Services in detail
Product Owner
Product Owner
Agile Coach & Scrum Master
Agile Coach & Scrum Master
Project management
Project management
Consulting
Consulting
Change management
Algorithm and organizational development in perfect symbiosis
The full potential of algorithmization can only be exploited if you continue to develop your organization at the same time. If algorithms are ineffectively embedded in the organization, this can even be counterproductive in the worst case.
Employees work past the algorithms, they look for loopholes to trick the algorithms and there is a lack of trust in the algorithms. The magic word in this case is change management: the necessary environment for the newly developed algorithms must be created by consciously managing changes in the organization. The change can affect various areas of the organization: From the organizational structure and processes to competencies and corporate culture.
The challenge of digital transformation consists of two aspects: On the one hand, to use the latest technologies and, on the other hand, to develop the organization to use these new technologies efficiently. This requires a basic understanding of algorithms, how they work and their influences and effects, as well as knowledge of change management methods. A digital transformation can only succeed and algorithms can only work successfully in the company if the organization can be designed sustainably.
Objectives of change management
- Creating an environment in which algorithms can work in the best possible way (fully automated, semi-automated or supportive).
- Involvement and support of employees during the change process.
- Shaping the corporate culture and working together.
- Establishment of a learning organization.
- Designing the interaction between people and technology.

Services in detail
Transformation management
Transformation management
Change management
Change management
Organizational development
Organizational development
Consulting
Consulting
Application operation
The demands on operations are huge these days: 24/7 in real time, without downtimes, bugs or other problems
Ideally, the clients don’t notice anything about the operation and the users are satisfied. In order to fulfill this requirement, a lot has to be done in application operation. Monitoring and reactive intervention are no longer enough these days. Instead, issues must be tackled proactively and improved on an ongoing basis.
Services in detail
Maintenance
The main task of operations is maintenance, i.e. the necessary functional and technical adjustments to the IT system to ensure smooth operation. We use various methods to design preventive maintenance and thus increase the satisfaction of your users.
DevOps
Nowadays, applications are constantly being developed further. We use DevOps methods to consider further development and operation together. This enables us to increase the quality and speed of your software development.
Structure
After a project, the question of how to operate the new application often arises. We will not leave you alone here and will support you in setting up the new structures and handing over the project to the company.
Support
If you need support with application operation, we are also there for you in the long term. For example, in a support model in which you can draw on our expertise for difficult or major issues.
Complete takeover
If you do not have your own company organization, we can organize the operation for you.