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Secure Competitive Advantages with Data Science Consulting from valantic

From the initial inspiration to the successful implementation.

Right now, many companies see the use of data science only in a scientific context. However, data science projects are indispensable for all companies in order to secure clear competitive advantages on an innovative market and use data effectively. Companies can optimize business processes, increase the quality of their products and services, and reduce costs and increase customer satisfaction by using data science to detect patterns and for forecasting. The benefits are obvious, yet many companies are lacking a suitable entry point for making a successful start with data science projects.

Since often there is no clear guideline for making such projects tangible, data science is frequently not yet used in operational practice. That’s why our data science consultants are prepared to help you develop and implement your data science strategy: from identifying relevant use cases to selecting suitable tools to the productive implementation of machine learning (ML) models and use of artificial intelligence (AI).

Table of contents

Table of contents

Together with customers, valantic develops individual use cases that we accompany all the way, from design to implementation. With data science projects, companies can reduce costs, improve the quality of their products and services, and therefore increase their profits for the long term.

In addition to its data science expertise, valantic has completed numerous successful business intelligence (BI) and data warehouse (DWH) projects. Many companies have already profited from valantic’s extensive consulting experience.

Why valantic?

  1. Comprehensive advising on the development of your data science strategy

    Our data science experts are prepared to help you identify relevant use cases, select suitable tools, and implement machine learning (ML) models and artificial intelligence (AI) in your productive environment.

  2. Data science project experience

    Through many successful business intelligence and data warehouse projects, valantic has acquired deep knowledge. It is therefore the ideal partner for data science projects since data provision and preparation form the basis for successful data science analyses.

  3. Agile project management

    valantic uses agile procedural models such as scrum and Kanban. Our data science projects are implemented using short iteration cycles and feedback loops in order to minimize risks and errors in the development process. This increases transparency and the speed of change, which makes data science projects more cost-effective for our customers.

  4. Vendor-agnostic

    Our long-term data science partnerships enable us to access many platforms and tools that we use for companies’ benefit. There is a custom-tailored solution and advising for every data science project. Profit from our partnerships with IBM, SAP, Cognigy, Microsoft, AWS, UI Path, and Tableau. The use of open-source platforms and tools also allows us to create flexible, individualized data science solutions for each of our customers.

Time Series Forecasting

For a company’s strategic control and planning, reliable prediction of future sales and other KPIs is crucial. Data is analyzed over time to achieve this. In addition to classic statistical models such as the ARIMA model, in the meantime complex deep learning models (neuronal networks) are being used more frequently. The prediction of KPIs has enormous potential for companies since the insights gained can serve as sound bases for decision-making.

Fraud Detection

Many companies suffer from fraudulent activities, some of which increase costs immensely. During fraud detection, transactions are examined in order to develop a model that detects patterns that are very likely to result in fraud. This model is applied to new data and real-time data in order to detect and prevent fraudulent transactions. Preventable costs can be avoided this way.

Customer Segmentation

Today, it’s ever more important for companies to understand and address customers’ individual needs. With customer segmentation, relevant customer groups are identified and divided into sensible segments. With knowledge about the similarities and differences between these customer groups, companies can develop targeted marketing and CRM strategies. Segmentation also offers a good basis for conducting additional data science analyses, such as churn management and sentiment analyses.

Churn Management

With the churn of customers, companies lose sources of income and potential for upselling, cross-selling, and further recommendation. With a churn management analysis, the customer churn can be determined based on the customer’s past data. In addition, customer churn serves as an indicator for addressing customers who are threatening to leave. This can prevent regular customers from jumping to a competitor and increase customer satisfaction.

Recommendation Systems

A recommendation system should suggest to users the most relevant items for them from a large quantity of objects (documents, pictures, videos, or products). By recording user behavior, relevant suggestions are made by a machine learning algorithm. Recommendation systems can assist a company’s customers and employees. By recommending suitable products, the customer experience is improved for the long term in an online shop, for example. Furthermore, a recommendation system can help employees with their daily work and produce slimmed-down processes and lower costs by finding relevant documents more quickly.

E-mail and text analysis (Natural Language Processing)

Every day, companies generate immense quantities of text documents. In the process, things get lost easily. Furthermore, incorrect entries in unstructured text data frequently go unnoticed. By using text analytics and NLP, companies can examine text documents efficiently, analyze e-mails and other customer communication, fill out forms automatically, and find relevant information in large quantities of data. Thanks to technology, costs are reduced, and employees can focus on their core tasks.

Sentiment Analysis

Sentiment analysis is a method from natural language processing (NLP). The analysis is used to determine whether customers have a positive or negative attitude toward a particular company’s products or services. With sentiment analysis, companies understand their customers better and they can react quickly and with purpose to customer requests or criticism. The results of the sentiment analysis can be displayed on dashboards and made available to the relevant departments. Sentiment analysis supports decision-making based on customer insights.

Supply chain management and production

The increasing digitalization of production and supply chains is already generating huge quantities of data. Thanks to the intelligent linking and enrichment of the data with data science methods, there are many use cases that generate true added value with respect to transparency, optimization, and automation. With connected plant, we work with our customers to implement high-quality data products in the supply chain management and production sectors.

Learn more about appropriate use cases in the production and supply chain sectors.

Of course the use of data science extends far beyond these use cases. Other possible use cases are, for example: Predictive maintenance & quality assurance in production.

Procedural models for data science projects

The key to a successful data science project is the effective cooperation of the entire team. Over time, therefore, various procedural models have been developed, which serve as guidelines and offer helpful structure. Companies that orient themselves according to procedural models can better plan data science projects and thus minimize project risks. As an experienced partner, valantic helps its customer with project planning and uses the following models:

Data provision and data preparation

Data Engineering

Companies frequently underestimate the relevance of data engineering for data science projects. However, data provision and preparation play a decisive role for every data science use case that will be deployed productively at a company. Data engineers focus on data collection, cleansing, preparation, and validation. Data engineering guarantees high data quality and quick data availability. Both are basic requirements for validated and sensible analyses and solutions.

This diagram shows the Data Science hierarchy of needs in the form of a pyramid.

State-of-the-art data platform

A state-of-the-art data platform is a collection of tools and technologies that enables the company to become a data-driven organization. Frequently cloud solutions are indispensable for this path since they make the IT infrastructure scalable and elastic and enable quick implementation of use cases and the quick growth of companies.

The pillars of a state-of-the-art data platform are the data warehouse and the data lake. They serve as central repositories of structured and unstructured data (Big Data) and prepare the raw data via data transformation for business intelligence and data science applications. The data migration is the point where the construction of a data platform begins. Here, data from many different data sources is loaded into the data warehouse or data lake. The data transformation enables the cleansing and combination of the data. Tools in the data catalog and governance sector enable the addition of metadata and formulation of governance functions for creating, classifying, and managing a catalog for data stocks.

Given the increasing number of tools, the big challenge is to manage data protection controls and access control across the entire stack (access governance). Today’s business intelligence and analytics tools offer users dashboards and self-service reports. Companies that go beyond business intelligence and focus on predictive analyses and machine learning use special data science tools for this.

This diagram shows the data platform of Data Science.

There are many technologies that have to be integrated sensibly into the company’s data platform. Great expertise and project experience in data engineering and governance are required for this. The data platforms that valantic implements with its customers distinguish themselves through custom-tailored tool selection, agile data management, and a flexible, quick process model for data provision and preparation.

Development and provision of machine learning models

Types of machine learning

Machine learning describes the application of learning algorithms with the aim of making predictions or discovering unknown patterns and laws in data. Unlike classical algorithms, these learning algorithms are not programmed, but are trained and continuously improved with data. This allows machine learning algorithms to be used flexibly in a variety of application areas. Depending on the use case, different machine learning models can be set up and used. Machine learning is divided into three types: supervised learning, unsupervised learning, and enforcement learning.

This diagram shows a machine learning model.

Supervised learning

In supervised learning, the data stock consists of a collection of previously tagged data. The data consists of features and labels. The goal of a monitored learning algorithm is to use the data to develop a model that receives a feature or a set of features and can predict the labels for new data records. Monitored learning is used in prediction and prognosis (regression), fraud detection (classification), risk assessment of investments, and calculation of the probability of failure of machines (predictive maintenance).

Unsupervised learning

In unsupervised learning, the algorithm uses a data stock that consists solely of features. In contrast to supervised learning, the data has no labels and therefore there are no target variables. The goal of unsupervised learning is to identify hidden patterns and similarities in the data. These patterns and similarities can be used to group data (segmentation) and to reduce the complexity of a data set (dimension reduction). By using these methods, data can be visualized better and interpreted more easily by users. In addition, the results of the algorithms can be used as features for further data science analyses. Classic use cases for unsupervised learning are customer segmentation, referral systems, and targeted marketing.

Reinforcement learning

In reinforcement learning, the machine (agent) is in a virtual or real environment. The agent can perform actions in this environment and is evaluated by a reward system and a cost function. In so doing, the agent learns a policy that maximizes its benefit. The machine therefore uses feedback from interaction with its environment to optimize future actions and prevent errors. Unlike supervised and unsupervised learning, the algorithm does not require sample data, but only an environment and an assessment function to optimize its actions. Reinforcement learning is applied in those areas where decision-making is sequential. Classic applications include computer games, robotics, resource management, and logistics.

Deep learning

Deep learning is a subset of machine learning that uses artificial neural networks. Deep neural networks exist when the neural network has more than one intermediate layer. Neural networks are particularly suitable for complex applications and/or the field of unstructured data (Big Data). Deep learning is already being used in a variety of AI applications (for example in voice assistance, face recognition or autonomous driving), which leads to revolutionary and disruptive changes.

This diagram illustrates a Deep Learning model

Machine learning in companies’ productive environments

There are some challenges when introducing a data science use case into a productive environment. The implementation of the machine learning models (ML code) represents only a fraction of the work effort involved in a successful data science project. While machine learning models are often based on open-source tools and tools, the classic IT architecture often works with proprietary software — for good reason. This situation requires a close meshing of ML code and IT operations (MLOps). Factors to be considered are not only the provision of the machine learning models, but also a continuous quality check. MLOps ensures effective use of the analysis results, ensuring consistent quality of the results.

This data science diagram shows a machine learning model in enterprise production environments.

Artificial intelligence (AI)

Artificial intelligence (AI) is already a part of our daily lives. In the morning, our voice assistant wakes us up. On the way to work, due to the current traffic situation, a navigation device suggests a detour from the regular route. Throughout the day, the smartphone helps us compose messages and texts. In the evening, we rely on the recommendations of a virtual agent to find suitable films and TV series. AI accompanies us without our even noticing it.

This chart contrasts strong and weak AI.

AI is about developing learning algorithms that mimic human behavior. There is a distinction between strong AI and weak AI. Strong AI refers to the development of a system that has the same intellectual abilities as a human being and is thus able to solve any kind of general problems. Weak AI describes a system that has been developed and trained with the help of data specifically for a particular use case. While strong AI is still a dream of the future, weak AI is already being used at many companies. The most common applications include:

  • Predictions and pattern recognition
  • Image and face detection
  • Natural language processing (NLP) & voice assistants (chatbots)
  • Robotic process automation (RPA)
  • Robotics and autonomous systems

When using artificial intelligence, it is important to think in the long term. Artificial intelligence can not only achieve cost savings and profits in the short term, but also enable completely new business models in the long term. In order to achieve this long-term and sustainable added value in the best possible way, AI projects should be embedded in a company-wide strategy. valantic assists companies with the strategic planning of AI projects, the design of AI applications, and the implementation of AI systems in productive operation. Read our service page to find out how you can use artificial intelligence profitably at your company.

Tools and platforms

A suitable data science platform should not only enable the construction and use of machine learning models, but also the preparation and visualization of the data. Meanwhile, vendors are competing to offer these capabilities. Cloud-based products (Software as a Service) in particular are experiencing rapid growth. In addition to classic software vendors such as IBM, Microsoft, SAP, and SAS, younger companies such as Databricks, Dataiku, and DataRobot are also trying to establish their innovative products on the market. While younger companies are relying on lean and easy-to-use data science platforms, large software vendors offer highly customizable solutions that are often required to address complex business needs.

Many of these cloud-based products are fast and easy to deploy, and they frequently offer a platform where data scientists, analysts, and business users can exchange ideas. With predefined processes, even specialist users can execute complex algorithms and interpret and share the results. With the help of AI functionalities, many tools can create company-specific analyses with the click of a mouse. The tools also strengthen the interpretation of the underlying data through their visualization capabilities. This, in turn, reduces initial costs and facilitates entry into more complex data science projects.

In addition to the established software providers and the younger companies, programming languages such as Python and R are a valid means of editing and visualizing data and creating and using a company’s own machine learning models. There are predefined open-source libraries for this purpose. Due to their flexibility, the programming languages Python and R are usually the first choice for data scientists. To bridge the gap between analysts, business users, and data scientists, these two programming languages are now integrated into data science platforms and tools. This enables better collaboration and thus increases the productivity of the entire team.

This image shows all data science providers.

Through our partnerships with established software providers and the use of open-source libraries and tools, we are able to develop a solution that fits every customer. Work with valantic to benefit from our expertise and use the right data science tools and platforms.

Make an appointment now!

Daniel Völker, Head of Data Science

Daniel Völker

Head of Data Science