With Data Intelligence CURE offers you proficient support in analysing your data, using the latest methods from the fields of data science, machine learning, and predictive analytics. In doing so, we combine practice-oriented know-how with cutting-edge research and thus offer you customised outside-the-box solutions: From data provision (i.e. internal and external data including social media) through data preparation and modelling all the way to visualisation and integration into your daily business.

Together, let’s use Data Intelligence to enable data-driven innovation and intelligent processes.

Data Engineering

With our data engineering support, we take care of the data relevant to your project. From the selection of data sources, the setup and filling of a database to continuous database maintenance, so that statistical and mathematical models can be applied.

In the process, we access already existing data systems, data structures and – if necessary – develop customised and automated (or partially automated) solutions in Python, R, Java, or by using similar languages for smooth data integration.

Web & social media data are frequent data sources which we collect via monitoring, but also internal data from your website (via e.g., Google Analytics or Adobe Analytics), intranet or data from public sources such as the Robert Koch Institute and the German Foreign Office. Interfaces that we can “tap into” play just as important a role as your CSV files with thousands of lines, which our data engineers can easily put into the right form.

How can we help you?

Data Analytics & Data Science

Descriptive & Predictive Analytics

Using intelligent data evaluation to accurately describe the current situation or forecast future developments while acting accordingly in anticipation – that is a highly relevant competitive edge right there. Use it for yourself!

Simply describe your ideas. Our experienced experts will then check existing data for you, obtain further data if necessary, develop target-oriented models, check their suitability, and then implement all this in such a way that it optimally supports your decision-making processes.

Modelling

We lift your data treasure! We develop individual qualitative and quantitative models tailored to the specific problem, considering both the present market standard and current research to achieve the best results for you.

What is your question?

Machine Learning

Self-training certainly is the holy grail of data analysis. For each case, we use the most appropriate method to gain the most from your data:

  • Unsupervised Learning: Learning without knowing the target in advance. A typical application is clustering, such as customer segmentation. Best-practice methods: k-means, neural networks, Hidden Markov, Gaussian Mixture
  • Supervised Learning: Learning with the target known in advance. Typical applications are regression (such as predicting customer cancellations) or classification (such as sentiment analysis). Best-practice methods are: Regression (LM, GLM, logistic), decision tree methods (random forests, XGBoost), support vector machine, neural networks/deep learning.
  • Reinforcement Learning: Self-training through rewards. Typical applications can be found, e.g., in the field of computer games intelligence. Best-practice methods are: Monte Carlo methods and temporal difference learning (such as Deep-Q learning).

What do you want to learn from your data?

Data Science

Every Data Science project starts with you. Because for us, it is always about providing you and your company with the competitive edge. To do this, we need to know exactly what your status and goals are. Only then can we collect suitable data, prepare it, and develop individual qualitative and quantitative models tailored specifically to the problem and implement them in professional code (Python, R, Spark, …). Mind you, we do not consider the project finished until the results are also appropriately (interactively) visualised, communicated, and automated.

Which data would you like to know better?

CURE Intelligence Data Science Process

“Data is the new oil” is only true if internal and external data is properly collected, processed, evaluated, and visualised. That’s what Data Intelligence is all about – let us get you ahead of the game!

Market Research

Recognising market trends and customer needs in time, as well as movements among competitors, early knowledge about the acceptance of new developments or the effectiveness of advertising media can significantly determine the future of a business. We support you with customised interactive dashboards, predictive models and plenty of know-how.

Which market developments would you like to know about?

KPIs

Key performance indicators (KPIs) that can be used to determine the performance of activities in your company are a plus – especially if they are integrated into your decision-making and communication processes in a meaningful, reliable, and targeted way.

The trick is to identify and evaluate the right KPIs. “Right” means that the KPIs are defined to exactly match your goals, and that their calculation is based on valid data.

Data Visualisation

Data should ideally be of the “speaking” kind. A useful report is compact and provides the most important insights for you quickly and easily. Here, we are masters of our trade. Whether it’s an online dashboard, PowerPoint, email or integrated into your IT infrastructure – whatever your preference, we are here to build you a functional and visually appealing report.

In doing so, we access existing tools such as Tableau, Microsoft Power BI, or Google Data Studio, or we build a tool tailored to your every need. 

We develop appealing web-based solutions such as dashboards (white-labelling option included), or newsrooms for integration into your systems or intranet, and automatically fill them with (real-time) data. Whether you need a simple and compact dashboard presentation as a basis, or an advanced version with insights, recommended actions, and complex data models – let’s get started with Data Intelligence!

Example:
Aggregated Online Marketing Reporting

  • Determination of requirements and examination of the data landscape (internally and externally)
  • Creation of a database in which all data sources are aggregated
  • Development of reporting including qualitative and quantitative target values, benchmarks, KPIs, evaluations for all sales and PR channels
  • Process definition for continuous optimisation

Example:
Development of a Competitor Benchmarking

  • Status quo and requirement analyses
  • Data acquisition and processing to ten competitors
  • Selection and development of target-oriented KPIs
  • Conception of a functional, visually appealing reporting
  • Implementation of continuous monitoring and reporting measures including a management summary

Example:
Campaign Optimisation Through Demand Forecasting

  • Goal: Better control of search engine ads by early recognition of a regionally growing demand for flu remedies
  • Examination of various models and social media data regarding their suitability
  • Model testing using internal sales data
  • Development of a real-time reporting system for ad optimisation