Data and AI

Creating value from data

   big datadata analyticsdata engineeringdata sciencemachine learning

Data-driven companies are more successful on their way through digital transformation. The right data, effectively recorded and processed can for example help to predict energy demand in industrial plants. It can also help to adapt the behavior of IT applications to real user experiences or generate groundbreaking data products for new business models. In short: data creates benefits and reduces costs.

Why data projects fail

 

Pilot und MVP Gateaway

The pilot trap

Pilot phases for data products often run too long and focus too much on concept level. However, data projects live from trying, verifying, falsifying and adapting theses. Here it is always important to work closely on the data, to quickly find a feasible solution or to stop the pilot phase in a controlled manner if it is not feasible (fail fast).

The engineering hurdle

The even greater challenge emerges as soon as a successfully piloted data product is targeted to go into production as MVP. Overnight, the data-focused pilot team finds itself in a full-blown software engineering project. This project must now be integrated with modern DevOps approaches, bringing new functionality and embedding new data streams into grown process and application landscapes.

With us you build data products the right way

Pilot und MVP Gateaway

Pilot gateway

By driving sound cycles according to CRISP-DM, we evaluate the feasibility of a data product closely and in minimum possible time. At an early stage, we manage the expectations of product benefits and give you necessary foundation to decide for a go or no-go.

MVP gateway

We design and control development and deployment of a data-driven MVP primarily as a data-driven software engineering project. We rely on our many years of expertise as a proven DevOps partner for small and large custom-made IT systems and solutions.

Data Engineering

We integrate and transform data

It all starts with the right data. About 80 percent of the effort for a viable data product lies in extracting, processing, merging, and transforming the data. You do not want to invest this effort manually every time but strive for an appropriate degree of automation by deploying reproducible and robust data pipelines.

Data extraction, transformation, and loading

Classic ETL for data warehouses or ELT for data lakes, structured, semi-/unstructured or stream-based data, in batch or in real time.

Data modeling and storage

From raw data storage, single-node databases, and big data to analytics-ready data representation in classic data schemas or agile data vault models.

Cyber security, data protection, and ethics

Attack protection, CIA, GDPR, procedural and technical sound integration of data sources, data ownership, data usage, and data sovereignty in productive data pipelines.

 

Where we work already

Data Analytics

We analyze and visualize data

The data relevant for an application context must be provided, analyzed and visualized. We distinguish between two variants: reporting and exploratory analysis. Here, too, there are many recurring tasks that should be robustly automated so that your users can concentrate on interpreting reproducible results.

Data provision

Avoid vendor lock-ins through flexible and modular interfaces that provide company-wide and domain-specific master and transaction data.

Reporting

Integration of analysis tools from open source components, cloud services, or in-house developments in order to continuously update KPIs, reports and dashboards.

Exploratory analysis

Provision and configuration of analysis playgrounds with a high degree of freedom to discover and save new relationships and metrics in self-service.

 

Where we work already

Data Science

We recognize patterns in data and make predictions

Data analytics helps us to better understand causal chains of the past and present. With data science we recognize recurring patterns and can make predictions. With the right preprocessing to adequately smooth out the application domain's data volatility, we keep predictions robust, reliable, and traceable. With MLOps, we integrate data science workflows seamlessly and automatically into your enterprise IT.

Hypotheses and baseline

Design thinking workshops for multi-dimensional problem analysis and derivation of hypotheses. Joint selection and definition of suitable decision-making criteria.

MLOps life cycle

Selection of suitable ML platforms and AI services. Automation of data science workflows with versioned models, data, and environments. Reproducible DevOps integration into your company IT.

Quality, concept/data drift, and explainability

Objective test procedures to validate your ML models, scientifically proven compensation concepts, and whitebox-driven traceability of predictions.

 

Where we work already

Leading companies build their data products with us

Would you like to find out more? We're here to help