ML & AI Readiness

For quick decisions and exciting use cases

Better forecasts, optimized decision-making, personalization and the basis for artificial intelligence don't sound like a fairy tale, but the intelligent use of data. And that ensures long-term competitiveness, higher sales and optimization of spending.

Our process

Strategic Alignment & Awareness

Conducting management workshops to define and align the vision and opportunities of ML & AI in the company
Exploring challenges to be solved with ML & AI and then differentiating them from other fields such as advanced analytics, LLM, etc.
Identification and specification of specific ML & AI use cases, their data sources and contacts required for implementation

Maturity assessment & use case evaluation

Carrying out a data audit based on the identified use cases to check the quality of relevant data
Analysis of the existing technology landscape on implementation options for ML & AI use cases
Outline of a possible data landscape that facilitates implementation of such use cases
Review of the existing skill and mindset for implementing ML & AI use cases

Recommendations & action plan

Preparation of a prioritized and detailed use case list for ML & AI
Designing a multi-stage ML & AI implementation roadmap
If necessary, support in the implementation, validation and development of any pilot use cases
If applicable, recommendations for action to achieve a suitable ML & AI maturity level (e.g. data cleansing and standardization initiatives, training, hiring support, etc.)

Why do companies need machine learning and advanced analytics?

Improved forecasts

By using advanced analytics and machine learning, companies can make more accurate predictions regarding customer churn, employee turnover, fraud, and future market conditions. This not only supports eCommerce or publishing, but also in the area of finances and marketing.

Optimized decision making

Through diagnostic, predictive and prescriptive analytics, companies can analyse events retrospectively, predict future events and provide data-based recommendations for action. These are important steps on the way to becoming a data-driven company.

Personalization and recommendations

Incredibly important not only in eCommerce: Advanced Analytics enables companies to identify patterns in the data and create customized offers for customers, which leads to improved customer satisfaction and effectiveness of marketing initiatives.

Increasing sales and competitiveness

The use of analytics based on their own data and thus on historical events ensures that companies can offer their customers the right offer at the right time — and this ensures higher sales and differentiates them from competitors.

This is how we go about establishing machine learning and advanced analytics

From the first audit to building competencies in the company to the development of use cases for the use of artificial intelligence — we stand by our customers as a strong partner.

Phase 1

Status check — the data audit

As the first step of cooperation in the area of ML and advanced analytics, we evaluate the current state of the company in terms of data infrastructure, quality and usage. This includes the assessment of existing data sources, data processing systems, analysis tools and the existing know-how of employees. An audit helps to identify strengths, weaknesses and development potential.

Phase 2

Strategy development and goal setting

Based on the results of the audit, we develop a strategy, which is based on the goals and needs of the company. These sets specific goals for the use of machine learning and advanced analytics. This includes resource planning and a plan for integration into existing business processes.

Phase 3

Training and Skills Development

For the effective use of advanced analytics and machine learning, it is essential that employees are trained to use these technologies. For this purpose, our team includes ML and data science experts who support stakeholders from the data departments with technical skills and an understanding of artificial intelligence.

Phase 4

Deployment and integration

The strategy and knowledge are there: The planned analysis tools and machine learning models can now be implemented. It is important to us to find individual solutions and not to implement models that will not be followed up later.

Phase 5

Establishing use cases for artificial intelligence

Use cases for artificial intelligence can now be evaluated based on the implemented machine learning models. The basis for this is a clean database. Solutions can include automation and personalization, but also the development of new business models.

Grafik des Frameworks mit dem Data Institute arbeitet.

Advanced analytics in our framework

We always work with the framework organization, culture and architecture.

Because in our opinion, these 3 areas are the most important factors for successfully anchoring data in the company in the long term.

Advanced analytics and machine learning as the basis of artificial intelligence must also be built on this framework. The organization describes who, culture describes HOW and architecture describes WHAT.

Without this basis, no long-term data strategy can be built.

How does our framework work?

The Data Institute — the strong partner when using machine learning

We want companies to have an impact quickly and be successful working with data. At the same time, we have an overall view of the company and want to implement long-term strategies that enable employees to work independently with data — and then also with artificial intelligence.

Learn more about us

What is machine learning and advanced analytics anyway?

Machine learning (ML) and advanced analytics are closely linked concepts in the area of data analysis and artificial intelligence.

Machine learning is a form of artificial intelligence that enables computer systems to learn from experience and improve themselves without having to be explicitly programmed. This is done by algorithms that analyze data and use it to identify patterns and trends. Through this process, systems are increasingly able to discover hidden insights, historical relationships and trends and identify new opportunities in various areas such as customer preferences, supply chain optimization, or energy generation. Machine learning is already being used in many applications, for example in recommending products, e.g. in eCommerce, in retail, in marketing and in self-driving vehicles.

Advanced analytics, on the other hand, is a comprehensive term that includes various techniques of data analysis, such as machine learning, predictive modeling, and neural networks. These techniques are used to go beyond traditional business intelligence and gain more complex insights into data. Advanced analytics can be used in various areas, including diagnostics (e.g. statistical A/B testing of websites), predictive analytics (e.g. object recognition using smartphone apps) and prescriptive analytics (e.g. dynamic pricing). The aim is to make predictions about future events, figures or behaviours based on historical data and to recommend appropriate measures.

The combination of these competencies creates exciting application opportunities for companies that ensure long-term competitiveness.

Who needs machine learning and advanced analytics?

The use of artificial intelligence, machine learning and advanced analytics is interesting for almost all industries and opens up exciting new business models. ML is almost the order of the day in the financial sector, just as it is in insurance, which makes it possible to better assess risks, in production, predictive maintenance is important to predict the maintenance of machines, and intelligent supply chains can also be set up in retail through advanced analytics.

In marketing competencies are also indispensable when it comes to efficiently targeting users and optimizing customer contact.

And even NGOs can benefit from this by being able to better manage emergencies and make better use of resources.

Who is involved in building machine learning and advanced analytics?

The development of ML models and the use of advanced analytics requires strong competencies from data scientists. However, the data strategy must also fit this. The topics must therefore be supported and sponsored by management.

Compliance teams and marketing employees who should incorporate the new competencies into their strategies are just as important.

Artificial intelligence is a decision of the entire company, which is why we always focus on the culture and enablement of individual employees when establishing it.