Advisory & Design
We always follow our objective, “Think Big, Start Small”, meaning that we first identify key value opportunities with quick results to make sure that you can reap the first benefits of the data project as soon as possible.
This goes hand in hand with our interactive agile development methodology that builds confidence among stakeholders and users as they get familiar with new solutions and tools at a very early stage and enjoy quick wins.
Our end-to-end solution starts with the initial assessment, where we evaluate your current technology stack and determine a clear data strategy, roadmap and modern data architecture that helps you achieve your business goals in the most efficient and cost-effective way.
Rather than setting up internal servers and databases, we prefer to choose the cloud platform as it brings multiple benefits over on-premise architecture:
ease of set up
Why wait for the hardware to be purchased, software to be installed and networks to be configured, if you can have an instance up and running in just a few minutes.
No need to pay up-front for what you might no need.
SCALABILITY AND flexibility
Services can be scaled up and down on-the-fly, without downtime or disruption.
high reliability and ease of accessibility
Cloud services come with very high reliability and are easy to access from different devices and locations.
Services are maintained solely by the cloud service provider.
End-to-End Solution Delivery
Data Warehousing and Data Lake Expertise
With a pragmatic step-by-step plan, proven data integration methodology and data warehouse automation platform, we can help you to significantly reduce delivery time, costs, and risks and achieve greater efficiency and consistency.
We have over 15 years of experience in building data warehouses on different technologies such as Snowflake, IBM Integrated Analytics Systems (Netezza), Oracle Exadata and Microsoft SQL and have a large base of prominent clients who trust our expertise.
Our consultants possess diverse modelling skills, i.e. Kimball, Super-Sub Type, Anchor and Data Vault and have extensive industry-specific knowledge regarding insurance, banking, telecommunications and retail.
Therefore, we can help you to select the most suitable methodology and model the data warehouse according to your analytical requirements.
We faced different technical and industry-specific challenges over and over since the very beginning and have encapsulated everything we’ve learnt along the way into DataMerlin.
DataMerlin introduces a modern approach to solving complex data integration challenges. It is a platform composed of proven methodology, built-in technical expertise, best practices and tools that automate the ETL / ELT process as much as possible.
By combining the power of automation with a metadata-driven approach, you get exceptional results that are 5- times faster than traditional methods.
We are able to either seamlessly integrate data from disparate internal and external sources, cleanse it, transform it and load it to your data warehouse for a unified view to support business goals and initiatives or simply ingest it into the Snowflake data lake for instant access and data science initiatives.
Analytics and Artificial Intelligence Expertise
We have a proven track record of over 150 successfully completed projects where we helped organisations to bring analytics to their everyday operations and achieve results like higher marketing ROI and revenue, better customer satisfaction, quicker response to market, lower acquisition cost and higher efficiency.
Most common projects:
Mentoring and Training
We believe in sharing information and spreading knowledge. For this reason, we offer mentoring, training and consulting services for different analytics topics.
To organisation that would like to concentrate on their core business and completely outsource their data and analytics environment, we offer a fully managed analytics solution.
5 Reasons to Use Managed Analytics Service
New technologies constantly emerge and consequently organisations are experiencing a lack of expertise and knowledge, because it is too costly to employ data experts with all the variety of required skills and too time consuming to train them in the latest methodologies and solutions.
Data is growing at an exponential rate and data sources are becoming more diverse and complex, therefore consolidating and integrating data from all internal and external systems has never been more difficult.
Traditional ETL / ELT development and implementation takes months, which is far too long to meet business requirements and to be ahead of the competition.
Legacy data systems don’t support unstructured data and thus cannot leverage the advantages of big data analytics, nor do they support real-time business operations.
Existing data warehouse environments are near maximum capacity and cannot be scaled up any more or only at a very high cost.