ML & AI Readiness
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.
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.
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.
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.
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.
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.
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.
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.
What services can be combined withML & AI Readiness?
Case studies on the subjectML & AI Readiness
You can find suitable examples of our work on this topic in the following case studies:
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