The unifying data science platform
Our Applied Data Analytics Laboratory (AdaLab) can support everything from simple digital transformation tools and dashboards to advanced AI applications. But most importantly, it supports the corporate community and empowers your teams of domain experts to turn their expertise into data and analytics solutions for their peers to help them make expert guided decisions.
AdaLab is a secure, always-on, work-from-anywhere community-centric data science platform for data analytics and augmented decision support. It operates, integrates and improves a set of the best, modern open-source tools within data and analytics.
Finding, accessing, interoperating and reusing (FAIR) data and analytics is essential to getting return on investment of digital transformations.
AdaLab’s Application Gallery makes it easy for all users to find, share, track and collaborate on FAIR analytics. All users can create and publish their own data products and applications and make them available to their peers in minutes. Built-in reporting and quality assurance workflows ensures that usage of all available applications can be optimized and that quality and relevance are high.
In larger organizations, access to data and applications are limited to sub-groups due to per-seat licenses and geographic constraints.
To ensure optimal usage of data and analytics for the whole organization AdaLab is licensed on a flat license and runs scalable in your own cloud accounts. This ensures that all have equal access to use and collaborate on creating analytical solutions which deliver data driven decisions to all domains of your organization.
If it is too technically demanding to create and maintain data and analytics products the organization will not be able to fulfill its potential. The few analytics creators available become a bottleneck to a successful digital transformation.
AdaLab comes packed with the most popular and useful data science tools and applications. The tools are hand-picked and combined with custom components that make it easy to build from examples and create new web applications for non-IT professionals. This ensures that your domain experts can act as citizen data scientists and embed their knowledge about your business into new tools for their colleagues to help them make expert guided decisions and form a community driven analytics culture.
When analytics becomes too centralized and specialized it gets detached from the rest of the business. It becomes a sparse resource subject to planning and prioritization. This delays and distorts progress and agility of data utilization.
With the community-centric analytics approach that AdaLab supports, agility and innovation comes naturally when colleagues collaborate on creating new analytics solutions for their peers in short iterations. When this community is supported by embedded IT-professionals, they learn the business from the business experts and these in turn become better at analytics application development. The result is a self-serving agile analytics community that drives data usage and innovation.
Data and platform engineers are scarce resources, often becoming bottlenecks when tasked with duties that could be more efficiently outsourced, such as building software that can readily be purchased. Additionally, the complexity of navigating multiple systems and data sources poses a significant challenge for most users, hindering their ability to access the right data in a timely manner. This dual constraint not only limits your engineering team’s productivity but also stifles the potential for broader organizational data literacy and innovation.
Use your data engineering resources on what makes your company unique and not on building a generic platform which it is much more cost effective to buy. Instead, your resources should go into building reusable integrations into a common layer where your custom data and services can be interoperated and combined to create new and unique business value. AdaLab comes pre-configured and prepared for most integrations which makes it easy to plug-n-play most data sources. If not our expert data engineers can help you develop custom integrations in a codebase that you own and can reuse.
Your challenge: When applications are not integrated they form silos for both data and users making collaboration hard. Additionally, problems with performance and reliability are often seen and potential synergies between the tools are not realized.
Our solution: Combining the scalability of cloud computing resources with the flexibility of container orchestration from Kubernetes, AdaLab can scale to any workload and any number of users. On this foundation we have integrated the best data and analytics packages and tools to create a secure and optimized analytics platform. A high level of integration ensures that the strengths of each tool can be utilized within the others making the whole greater than the sum of its parts.
Discover and share Jupyter notebooks, dashboards, apps, and resources as cards, making tools and resources easily findable and accessible.
Direct access to the tools that you use every day is never more than two clicks away.
AdaLab brings together Analytical Consumers, Citizen Data Scientists, and Analytics Developers for effective collaboration and knowledge sharing. They can quickly communicate, experiment and collaborate through each card.
Provide training, tutorials, and documentation through Card Groups, organizing related content for easy learning and use. AdaLab’s QA Workflow maintains content quality by enabling review, testing, and validation of shared resources.
Jupyter notebooks offer numerous extensions, enhancing their capabilities for data exploration, analysis, and visualization. These extensions include interactive widgets, data visualization libraries, and version control tools.
A no-code data exploration and visualization BI-tool offering interactive dashboards and visualizations without writing any code. Superset allows business users and subject matter experts to access and analyze data, easily define and share datasets.
Seamless integration with databases via Superset and the AdaLib library, making datasets accessible for multiple applications in Python and R. This provides a “Single Source of Truth”, allowing for federated querying of datasets by name anywhere on the platform.
Containerized kernels for Jupyter notebooks enhance reproducibility, isolate dependencies, and optimize resource usage. These advantages lead to more reliable results, improved collaboration, and reduced maintenance efforts.