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Data Science & Advanced Analytics

Creating added value for your company with artificial intelligence

The right AI strategy makes the difference

Artificial intelligence (AI) and data science are the trend topics of the last few years: for example, 84 percent of all decision-makers from IT and management state that the use of AI is a decisive factor for competition. In times of increasing cost and competitive pressure, it is becoming more and more important that companies not only make the right decisions, but that these decisions are also made quickly and proactively. With the right AI strategy, you can not only optimise your most important processes with the help of AI-supported applications, but also gain a better understanding of your data, on the basis of which you can then make the right decisions for the long-term success of your company.

In practice, however, the necessary expertise to develop the right AI strategy and identify suitable use cases is often lacking. Our experts have therefore developed concrete approaches that offer you and your company the right support and individual solutions depending on your needs.

In addition, we offer you the opportunity to expand the data science competencies in your company through individual AI training courses tailored to your company. For this purpose, adesso provides you with an overview of typical use cases in your industry, necessary skills for the development of AI applications and an insight into current technologies. To ensure that your employees are well prepared afterwards, the training can also be carried out directly with your company data. You decide which programming language is suitable for you and your employees.

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Ideation

Together with you, we analyse the use cases that are suitable for your company in an interaction room workshop and determine the necessary framework conditions. During the workshop, not only is a thorough understanding of the business processes built up, but a holistic overview of the existing IT structure, the quality of your data and the information content of the data is also created. Based on this, our experts design a custom-fit implementation plan including the appropriate architecture and technology.

Prototyping

Once a concrete use case has been identified, we support you in carrying out the proof-of-concept and in developing a minimum viable product (MVP). Prototyping is primarily about the rapid implementation of use cases. The goal is to evaluate the functionality and feasibility of the developed concept as quickly as possible. To achieve this, a suitable amount of data is used for the prototypical development of a machine learning model.

If you decide to use our adesso-Result-as-a-Service offer in this context, we can also implement your use case completely for you. In this case, you only provide the data and background information, and our experts take over the implementation up to the delivery of the finished AI application. If you wish, we can also create a custom-fit concept for integrating the AI application into your business processes.

In this context, we would also be happy to inform you about the Datathon. The Datathon is a five-day workshop in which we develop initial prototypes together with you, answer your questions about our approach and concretise hypotheses in order to derive meaningful recommendations for action.

Operationalisation

How is the final machine learning model deployed? Is real-time prediction required or is batch processing sufficient? How can I ensure that the quality of a model remains high in the future? How can I ensure the use of up-to-date data? Do I need to integrate a feedback loop? In the course of operationalising AI applications, many questions and challenges come up for companies. Using modern MLOps methods, we can provide you with targeted support in solving these challenges and integrate your prototypically developed use case into your data platform - from proof-of-concept directly to data-driven product, so to speak.

Our data science service portfolio

AI Assesments & Strategy

Advanced Analytics & Models

Tools & Infrastructure

Our data science experts help you to develop a successful AI strategy with the help of maturity analyses and technology consulting, and to identify and evaluate profitable use cases. We expand the competences of your employees through workshops - for example on the topics of data literacy and MLOps.

adesso develops analytics models for you, implements valuable use cases and makes your employees fit for the future with training courses on current technologies in the areas of machine learning, neural networks or programming.

We are your contact partner for all aspects of the operation of AI products, so that previously developed models can deliver the promised added value for your company in practice. An important factor here is your own infrastructure. With innovation scouting, technology consulting and/or a vendor selection, we offer you the necessary spectrum of consulting services to use or optimise existing infrastructures. Our offer also extends to technology-focused product development (for example within SAP S4/HANA or SAS Viya) and high-performance special solutions from our partners.

Our partners and data science technologies

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Can your company’s problem be solved through AI? What are the possible approaches and what data is available? Which technologies and concepts can be used? How does a prototype become a finished product? We have developed a concept, divided into three phases, for implementing data science and AI projects in a structured and goal-oriented manner.

1. From the Interaction Room to use case concept

Before the two-day workshop is held, the first step is to identify the existing IT structures, data structures, data quality and information content of the data. IR:analytics combines BI/DWH expertise with your specialist areas and ensures optimal communication and a lively exchange right from the start. During the workshop, the following points will be clarified:

Understanding
Identifying
Ensuring feasibility and viability
Prioritisation

Based on the results of the workshop, a basic concept will be developed to determine which architecture, technology, data and methodology are best suited for preparing the data and implementing the identified use cases.

2. From concept to prototype with real data

The second step includes the prototyping, with the objective of testing use cases as quickly as possible. This is where the agile, prototypical implementation of the elaborated results begins. Based on prioritised backlogs, a step-by-step implementation is carried out in sprints.

Operationalisation is about making the use case productive and achieving added value, measured against previously defined KPIs.

In order to ensure the structuring of tasks and the reproducibility and objectivity of the implementation, adesso combines an agile approach with the CRISP-DM process in data science projects. The process usually goes through several iterations. More data is available after each run, which allows a more targeted procedure for the next run. At the end of the prototyping, the following results are available:

a description of a scalable architecture for data science and AI
a prototypical implementation of the use case
documentation that is transferable to other use cases
a viewpoint with specific recommendations for next steps

3. From PoC to finished product

Operationalisation involves the go-live of the use case. The third step begins with the development of a scaling plan suitable for big data that allows us to work with you to prioritise the roll-out in terms of the component functions, customers and markets. The roll-out developed from the pilot will be carried out step by step. The number of users will be continually increased to generate more data to optimise the prototype. The optimisation focuses on technical (such as status or uptime) and functional monitoring (such as forecast quality over time). In this way, the pilot is gradually developed into a robust data science product. Extensive testing during the development phase turns the developed pilot into a marketable data science use case.

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