Get to insights faster
Before realising business value with analytics, companies have to build a platform and fill it with data. This investment is necessary to consistently generate insights at scale. We use our accelerator and years of experience to quickly build the analytics platform and implement data pipelines, reducing the cost and risk of the initial investment. That way, our clients can get to value 10x faster and focus on what’s important for business: business intelligence, data science, and data driven decisions with machine learning.
Reduce cost and increase scalability
Traditional on-premise EDW software, such as Teradata, Netezza, or mainframe-based DB2, is getting prohibitively expensive and can’t efficiently scale to the new analytics use cases. By migrating data pipelines and reporting to the cloud, you can reduce total cost of ownership, take advantage of onboarding new data sources, implement real time streaming data pipelines, and dynamically scale to increase efficiency of data scientists.
Very few companies should be in the business of managing on-premise data lakes. High cost of maintenance, stability issues, coupled with lack of scalability and limited technology stack options for DataOps and MLOps, they slow down data analysts. Migrating on-premise data processing to the cloud based solution reduces total cost of ownership, increases data quality and accessibility, and re-focuses company resources on building differentiating value.
Increase data quality and accessibility
Basic data lake is no longer sufficient to implement effective analytics at scale. Too many companies fill in in the lakes only to realize that the data is difficult to use. To unlock the value of data, upgrade your data lake with data governance, data quality, catalog and lineage, access layer, implement stream processing, and deploy an AI platform.
Become data driven organization
Getting value from data is hard without needed skills, culture, process, and tools. Similar to how DevOps streamlines application delivery processes, DataOps and MLOps can increase the quality of data pipelines and help data scientists consistently and repeatedly turn data into insights.
Get from raw data to business impact faster
|Capability||Data lake||Messaging||EDW||Access layer||Orchestration||Data catalog||Data quality||Application platform||AI platform|
Analytical data platform industries
We helped Fortune-1000 companies unlock the full potential of data.
Data is the main corporate asset for many technology and media companies. It could come from customers or IoT devices, but it should be efficiently and securely captured, managed, and made available for consumption. High performance, scalability, and quality are paramount in these cases. Find out more in a case study how we helped the #1 media company in the world to design and develop analytics at scale.
Read more about analytical data platforms
Accelerate the journey to modern analytics
We provide flexible engagement options to design and build analytical data platforms and AI use cases at scale. Clients take advantage of our accelerators to increase their speed to insights and reduce the risk. Contact us today to start with a workshop, discovery, or PoC.
We offer free half-day workshops with our top experts in big data and analytics to discuss your stream processing strategy, challenges, optimization opportunities, and industry best practices.
If you have already identified a specific use case for big data or fast data, we usually can start with a 4–8-week proof-of-concept project to deliver tangible results for your company.
If you are in the stage of requirements analysis and data strategy development, we can start with a 2–3-week discovery phase to perform gap analysis, design your solution, and build an implementation roadmap.