Gain real value from your knowledge
What is DataLab Big Data Analytics?
The challenges of Big Data´s three Vs (Volume, Variety, and Velocity) can be met with well-planned Big Data architecture. However, storing and allocating Big Data is only the first step in the Big Data value chain. For this data to generate value for your business (the fourth V: Value), it needs to be analysed using business intelligence or analytical procedures such as machine learning and transformed into business-relevant information. DataLab Big Data Analytics allows you to do this.
Utilise our analytical expertise to ensure success for your business
What functionalities does DataLab Big Data Analytics offer?
DataLab helps you to optimize your business by using advanced analytical techniques to extract actionable customer knowledge from Big Data.
The CRM environment retains a variety of Big Data analytics applications. Our extensive CRM experience and analytical expertise in Big Data Analytics allows us to provide you with support from the design phase to the successful implementation of your Big Data use cases, in areas such as:
- Predicting cancellations or inactivity
- Determining product or channel affinities
- Identifying the best direct marketing measure (next best offer)
- Developing a product recommendation system
Start today: Ensure SUCCESS by utilizing KNOWLEDGE derived from DATA!
For whom is DataLab Big Data Analytics suitable?
DataLab Big Data Analytics is suitable for your business if you requiere
- support in implementing your analytical Big Data use case.
- added value for your company based on your big data architecture.
- analytical models run smoothly and in a quality-assured manner during CRM operations.
“Analyzing Big Data requires methodological and technological expertise. We provide exactly this expertise in our “Big Data Analytics” consulting projects to ensure you get the most out of your data. We base our approach on the “start-simple-scale-fast” principle that allows for a quick implementation of initial measures based on the generated insights. This increases the likelihood of management buy-in for scaling the use cases or the implementation of case studies.”Jan Hendrik Seidel