DataSense’s solutions rely on a reference architecture with automation, agility, traceability and auditability at its heart. It all starts with determining the right data strategy.
When you build a house, defining an architecture is the basis of a balanced and strong foundation. The same applies to building a data warehouse. Data Architecture is a set of rules, standards and models that determine and define what type of data are collected and how they are used within an organisation stored, managed and integrated.
The goal of data modelling is to make sure the data warehouse delivers its promised return on investment. It is the component of data architecture that supports the mission of the data warehouse, to provide business intelligence and enable analysts to form tomorrow’s profit making strategies.
Our open and standardised modelling technique allows the data integration process to be automated. This automation ensures speed, cost reduction and higher data quality by reducing manual errors. This allows for more focus on the actual analysis and more advanced solutions that can provide a company with the insights they need.
We use a hybrid reference architecture to build future-proof solutions. Your business requirements change or new systems are put in place or deleted? No worries – our multi layered solution is flexible and scalable by design.
Rather than losing time in linking and structuring data manually, we integrate all data sources inside and outside of your organization in a data hub by automating the model generation.
We have developed an agile methodology with a “multiple speed approach”, allowing us to divide a Data Hub in two main parts. The first part is the integration of all data source systems, while the second part is used for the consumption of the data, i.e. analytics and applications. This enables you to meet your reporting needs, but also to establish a bidirectional communication between the sources.
Our Data Engineers build pipelines that transform raw data into ready-to-use formats for further analysis. Through flow management, a single change can be kept from having drastic repercussions throughout the pipeline.
By exploring your data and enriching it, we can propose relevant AI cases and solutions to your questions. You may even harness AI for predictive business intelligence, allocating your resources where they make sense and maximising return on investment.
Systems can learn from data, identify patterns and make decisions with minimal human intervention.
We empower the business value of our clients by building and training machine learning models that allow you to conduct predictive and prescriptive analytics.
Performance testing is the practice of determining how a particular set-up will perform under a particular workload. The main goal is to improve speed, scalability, stability and reliability.
An automated testing approach is much more time-efficient and cost-effective while allowing for a timely detection of poor quality of the data warehouse. The Robot Framework also facilitates automatically repeated tests and ensures that data delivery defects are avoided in future output. As a result, companies can rely on trustworthy reports for their decision-making processes.