Data Science Design Sprint

Your starting signal for the digital future: 6 days towards data products

Reduced costs, efficient processes or new service modules: Do you see areas of application in your company for the use of AI, machine learning etc.? Then get started on your data product now – quickly, efficiently and goal-oriented with our data science design sprint.

Together with our experienced data scientists and consultants, you will complete the data-driven deep dive from the challenge to the successful prototype of your data product.

Knowledge transfer, concept, solution: Data Science Design Sprint Overview

Day 0: Status quo and planning

On day 0 we create the basis for a successful data science design sprint. What is your target sprint result? Which data sources are available for you? Which competencies and technologies are already available? In the run-up to your data science design sprint, we determine your status quo.

 

Objective, participants, time and place: We use information we have gathered to plan your data science design sprint.

Day 1: Learn and understand

The data science design sprint creates solutions and knowledge. What is data science? What possibilities does artificial intelligence really offer? What basic conditions do successful data products need?

 

On the first day, our data consultants will provide you with the knowledge you need in order to understand data science and bring it into your company in a sustainable way. In-depth insights into case studies from business practice provide you with inspiration and create a clear understanding of the potential of data science.

 

Day 1: Learn and understand

The data science design sprint creates solutions and knowledge. What is data science? What possibilities does artificial intelligence really offer? What basic conditions do successful data products need?

 

On the first day, our data consultants will provide you with the knowledge you need in order to understand data science and bring it into your company in a sustainable way. In-depth insights into case studies from business practice provide you with inspiration and create a clear understanding of the potential of data science.

 

Day 2: Identify and prioritize

In which business areas do you see potential for optimization? What is the data situation? Together with you, we identify the application areas in which data science can generate added value in your company.

 

Which use case offers the greatest business value? How complex is the implementation? At the end of day 2 you will have your use case.

Day 3: Specify and plan

The use case is ready. On day 3 the concrete solution follows. What is the relevant data? What is the availability of this data? Who are the relevant stakeholders? What technical infrastructure is needed?

 

This is where the requirements for the prototype are defined, the framework conditions are analyzed and the further project steps towards the sprint goal are determined.

Day 3: Specify and plan

The use case is ready. On day 3 the concrete solution follows. What is the relevant data? What is the availability of this data? Who are the relevant stakeholders? What technical infrastructure is needed?

 

This is where the requirements for the prototype are defined, the framework conditions are analyzed and the further project steps towards the sprint goal are determined.

Day 4: Design and realize

It goes in practice: We develop the prototype of your data product. Methodology, toolset, data connection: From day 4, your solution becomes more tangible day by day.

 

In addition to visible progress, valuable insights into existing optimization potentials for data and infrastructure are created.

Day 5: Train and validate

In the training phase the algorithm starts to learn and the results become resilient. Are the analysis results obtained informative? How can the analysis model be further optimized? Is there any information that is still missing? Validation makes the analytical potential of the analysis model clear.

 

Parallel to this, work on the development of the user interface is in progress.

Day 5: Train and validate

In the training phase the algorithm starts to learn and the results become resilient. Are the analysis results obtained informative? How can the analysis model be further optimized? Is there any information that is still missing? Validation makes the analytical potential of the analysis model clear.

 

Parallel to this, work on the development of the user interface is in progress.

Day 6: Experience and set productive

The analysis model and user interface are available and enable the simulation of real customer journeys. After only 6 days you can discover the prototype of your data product. We call it the moment of excitement – your vision becomes tangible.

 

How does it go on? At the end of the data science design sprint, the roadmap is defined until your data product goes live.

Key facts


Participants


Commitment, creativity and innovative power are created when decision-making competence meets interdisciplinary skills. Management, business users, IT administration: The optimal group of participants considers business and technical topics equally.

Results


After the sprint you will receive a documented prototype consisting of an analysis model and a user interface. In addition, you will receive all learning materials as well as the roadmap for the further procedure.

And you personally? You will be immersed in the topics of data science and AI in a very short time and learn key competencies for your future in a creative exchange.

Type of execution


At a distance in your business premises, creative meeting rooms or via remote: When carrying out the data science design sprint, we flexibly adapt your wishes and requirements.

Start your Data Science Design Sprint.
Your contact person:
Oliver Bracht

Contact
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