We transform complex data into actionable business insights. Our expertise spans the entire data lifecycle, from extraction to analysis. We design and implement robust data architectures that drive strategic decision-making and measurable business outcomes.
In non-IT companies, the role of IT should align closely with business objectives. Consider balancing in-house expertise with external solutions. Developing internal IT capabilities can offer control and customization, but may require significant investment in talent and infrastructure. Outsourcing or purchasing off-the-shelf solutions can provide quick implementation and access to specialized skills, but may limit flexibility. The optimal approach often involves a strategic mix, leveraging internal knowledge of business processes while tapping into external technological innovations. Evaluate your current IT team's strengths and identify areas where external partnerships could enhance your data management capabilities.
A high-performance data platform is a high-performance business platform. Drawing on deep and broad experience, we have successfully addressed every technical and business challenge such platforms can pose.
The absence of sound data management processes can cause delays and create obstacles to developing new business items. In desperation, teams may feel compelled to resort to their own, unsanctioned IT workarounds.
Imagine a board meeting in which the Chief Commercial Officer and Chief Financial Officer disagree about your company’s gross margins. Isolated data repositories can cause costly scenarios like this and make reconciliation nearly impossible.
If people doubt the quality of your data, they won’t use the data warehouse. If they can’t see how the data is processed or where it originates, they’ll be even less likely to trust it.
If you’ve assigned an inexperienced team to work on your data project, don’t count on success. If you feel you must take on complex data warehousing yourself because you lack the resources to do otherwise, you risk paying dearly in the end.
Work with IT consultants who go the extra mile to understand your business. By doing so, you’ll help to assure smooth and timely implementation.
New developments are cropping up in the data market all the time. How can you be sure they’re the right fit for you?
Want to understand how a data platform integrates complex data sources and transforms critical information to provide actionable insights and decisively benefit your business? Start here.
Enterprise Data Warehouse (EDW)
Extract and Load processes move data from operational systems to analytical platforms. Combining different data sources into a single, unified platform makes it easier to see your organization’s big picture. From there, you can make use of data management and improve decision-making.
Managing data pipelines often involves manual effort, leading to high development costs and time delays.
Changes in technical requirements create the need for continuous updates to connectors, diverting the data team from core analysis tasks.
Adapting to a variety of source systems can be cumbersome, often requiring individual setups that can make your system less extensible and less user friendly.
Equip teams with automation tools that streamline the creation of data insights and reduce the need to manage connectors manually.
Centralize schema and requirement changes to avoid ad hoc connector adjustments, saving time and focusing on analytical value.
Over 400 easy-to-access connectors exist to enable rapid and adaptable data integration. Use them to ensure quick, hassle-free setup and immediate data availability.
Reduce the costs associated with data processing and storage. Gain quicker access to insights to make timelier business decisions. Ensure your platform can scale the growth of your business. Ensure high-quality and accurate data for informed business decisions.
Prevent disruptions in data flow that can halt critical business operations. Ensure compatibility with evolving source systems and new software versions to avoid operational disruptions. Reduce exposure to data breaches and compliance issues. Ensure fast, reliable data transfers to optimize decision-making.
Maintain a unified view of data across the organization to support holistic analysis and more informed decision-making. Integrate new data sources quickly to support innovation and keeps your business competitive. Lower integration costs and reduce complexity for smoother operations and faster project completion. Share insights and coordinate efforts to drive more effective business strategies and improved outcomes.
Managing data pipelines often involves manual effort, leading to high development costs and time delays.
Equip teams with automation tools that streamline the creation of data insights and reduce the need to manage connectors manually.
Reduce the costs associated with data processing and storage. Gain quicker access to insights to make timelier business decisions. Ensure your platform can scale the growth of your business. Ensure high-quality and accurate data for informed business decisions.
Changes in technical requirements create the need for continuous updates to connectors, diverting the data team from core analysis tasks.
Centralize schema and requirement changes to avoid ad hoc connector adjustments, saving time and focusing on analytical value.
Prevent disruptions in data flow that can halt critical business operations. Ensure compatibility with evolving source systems and new software versions to avoid operational disruptions. Reduce exposure to data breaches and compliance issues. Ensure fast, reliable data transfers to optimize decision-making.
Adapting to a variety of source systems can be cumbersome, often requiring individual setups that can make your system less extensible and less user friendly.
Over 400 easy-to-access connectors exist to enable rapid and adaptable data integration. Use them to ensure quick, hassle-free setup and immediate data availability.
Maintain a unified view of data across the organization to support holistic analysis and more informed decision-making. Integrate new data sources quickly to support innovation and keeps your business competitive. Lower integration costs and reduce complexity for smoother operations and faster project completion. Share insights and coordinate efforts to drive more effective business strategies and improved outcomes.
Data transformation is about reshaping, merging, and organizing data so it's flexible, follows good practices, and is protected from being lost. Ultimately, this results in data sets that are easy to understand and use.
It’s tempting to perform initial data modeling that uses short cuts or non-scalable techniques. It’s also short sighted, resulting in systems that too often become fragile when they scale. Even minor updates become time-consuming.
Traditional methods of setting up analytics are done by hand and aren't standardized, which results in late delivery and brittle development processes.
Business users often spot data errors before the data teams do, showing a gap in data quality checks and inefficient error detection.
Invest in experienced data professionals who understand how to build robust, scalable data platforms. Their expertise ensures fit-modeling techniques that are sustainable and adaptable as well as effective.
Switch to automated and pattern-based processes that work in parallel for more consistent and reliable analytics. This make implementations stronger and reduces the need for manual work.
Encourage collaboration between business domain owners and data teams to define clear roles for maintaining data quality. Use proactive technologies to spot and fix data issues early.
With a robust and scalable platform, your business operates on a stable foundation. You can be assured of more accurate and timely insights for better decision-making.
More consistent and reliable business insights ultimately mean greater efficiency and enhanced decision-making for your business.
Allow your business users to confidently take charge of the benefits of data management.
It’s tempting to perform initial data modeling that uses short cuts or non-scalable techniques. It’s also short sighted, resulting in systems that too often become fragile when they scale. Even minor updates become time-consuming.
Invest in experienced data professionals who understand how to build robust, scalable data platforms. Their expertise ensures fit-modeling techniques that are sustainable and adaptable as well as effective.
With a robust and scalable platform, your business operates on a stable foundation. You can be assured of more accurate and timely insights for better decision-making.
Traditional methods of setting up analytics are done by hand and aren't standardized, which results in late delivery and brittle development processes.
Switch to automated and pattern-based processes that work in parallel for more consistent and reliable analytics. This make implementations stronger and reduces the need for manual work.
More consistent and reliable business insights ultimately mean greater efficiency and enhanced decision-making for your business.
Business users often spot data errors before the data teams do, showing a gap in data quality checks and inefficient error detection.
Encourage collaboration between business domain owners and data teams to define clear roles for maintaining data quality. Use proactive technologies to spot and fix data issues early.
Allow your business users to confidently take charge of the benefits of data management.
Publishing transforms data into streamlined, actionable insights that aid in decision-making and optimize business operations. It turns complex data into clear, insightful reports that stakeholders can act upon.
Lengthy development cycles and infrequent updates can slow the development process, causing a bottleneck that keeps the end product from reaching your users and reduces your data platform’s value.
Data visualization is a good thing. But too much data visualization can be slow to load and hard to use, and too rigid data visualization can fail to deliver the desired functionality. Finding the right balance is key.
User onboarding drives user adoption. The ease of using business intelligence tools can vary a lot, which can pose challenges for newcomers. How productive user onboarding of these tools are often depends on how much online help and community support is available to them.
Create a structured setup with clear requirements and consistent naming conventions to speed up implementation. Involve users in live prototyping to make sure reporting solutions meet their needs
Use dedicated data sets for specific use cases. Limit how much of your EDW you need to load into the data set. In addition to limiting the breadth of the data set, cut down on its length: Don’t load the decades’ worth of data if only the past few years’ worth is sufficient. These moves can significantly decrease dashboard load times and encourage use.
Select business intelligence tools that are intuitive to use and support automated features that further flatten the learning curve. Look for tools backed by a strong community and easy-to-access online resources.
Improve satisfaction and delivery time.
Deliver a better experience that enables users to focus and engage.
Streamline user adoption and enable more effective use of your business intelligence tools by all levels of user.
Lengthy development cycles and infrequent updates can slow the development process, causing a bottleneck that keeps the end product from reaching your users and reduces your data platform’s value.
Create a structured setup with clear requirements and consistent naming conventions to speed up implementation. Involve users in live prototyping to make sure reporting solutions meet their needs
Improve satisfaction and delivery time.
Data visualization is a good thing. But too much data visualization can be slow to load and hard to use, and too rigid data visualization can fail to deliver the desired functionality. Finding the right balance is key.
Use dedicated data sets for specific use cases. Limit how much of your EDW you need to load into the data set. In addition to limiting the breadth of the data set, cut down on its length: Don’t load the decades’ worth of data if only the past few years’ worth is sufficient. These moves can significantly decrease dashboard load times and encourage use.
Deliver a better experience that enables users to focus and engage.
User onboarding drives user adoption. The ease of using business intelligence tools can vary a lot, which can pose challenges for newcomers. How productive user onboarding of these tools are often depends on how much online help and community support is available to them.
Select business intelligence tools that are intuitive to use and support automated features that further flatten the learning curve. Look for tools backed by a strong community and easy-to-access online resources.
Streamline user adoption and enable more effective use of your business intelligence tools by all levels of user.
Despite a substantial investment, the bank’s data warehouse infrastructure failed to meet benchmarks for system resilience, agility, availability, and data quality. This left the Risk Department and Middle Office with a tough choice: scrap the project altogether or pour additional money into it in hopes of satisfying the original requirements.
We offered the client a solid reengineering solution with the assurance it would hit performance targets, transforming their data platform over two years with a more robust approach. We handled the implementation directly and guided the engineering team throughout the process.
Since the successful refactoring, EDW user adoption has taken off bank wide.
The EDW has consistently outperformed expectations, multiplying the volume of releases to meet business requirements sixfold and significantly shortening lead times for new feature requests.
At the same time, users now enjoy a unified view of the bank’s P&L, market risk, credit risk, liquidity ratio, leverage ratio, economic capital, and underlying trading activities on a daily and intraday basis.
A bank's finance department started a project to create a unified reporting system. Different departments and projects used various platforms that were connected but didn't have a way to see the big picture, manage everything together, or forecast budgets. This made it hard for the finance team to do its job with confidence, which included complex tasks such as managing unquantifiable risks. The sheer volume of ongoing business activity only intensified the challenge.
We introduced a finance data management platform that not only helped the finance department to move forward but also gave the entire organization the means to streamline other data projects. In addition, we provided guidance and hands-on examples that allowed the finance team to apply best practices to their project aligned with the existing EDW.
For the first time, the internal finance team enjoyed detailed, reliable oversight into the links between budgets and procurement information, on one hand, and specific projects, day-to-day activities and organization-wide areas of operational support on the other. This enabled them to reconcile ledger records, invoices, purchase orders and more.
With a clear understanding of how costs are allocated and budgets are used, the client felt more confident, especially when reporting to management and answering stakeholders' questions.
Our client faced a significant issue with their current data platform for generating monthly closing reports. Internal audit required them to produce better quality, easier-to-audit data under tight deadline constraints. The client requested solutions to streamline the platform, making it more manageable and adaptable to their evolving reporting needs.
We quickly provided a feasible solution to meet audit requirements despite limited time and resources. Beyond solving for immediate needs, our team showed more broadly how to implement business items and reconcile output with the current data platform. Ultimately, we improved communication between IT and business for better collaboration.
The first part of our solution created a strong and reusable foundation that meets audit requirements and supports business goals while keeping the system stable. Our next step is to maximize automation, which will reduce risks and make processes more efficient.
The client wanted to create a risk management data warehouse to combine risk measures with trading profit and loss, making reporting easier. This project aimed to lay the groundwork for a company-wide data warehouse that would also benefit other departments. After several failed data projects, the data team was looking for guidance on how to build a strong data warehouse foundation.
We proposed to deliver a minimum viable product (MVP) within a month and then train the data team for the tasks ahead. We offered strategic and practical advice on starting a data warehouse from the ground up. Additionally, we introduced the team to best practices in data modeling and development during hands-on workshops.
The project successfully completed its initial delivery ahead of schedule. By providing the MVP as a prototype data warehouse, we demonstrated the feasibility of a successful data platform setup within their own infrastructure. This quick execution included implementing and documenting the solution, establishing a new development workflow, enhancing team skills through workshops, setting up a robust CI/CD system for seamless integration and delivery, creating a blueprint for expanding the data warehouse across the organization, and integrating data contracts to ensure data quality.
The risk department's compliance team was hampered by static reporting methods that depended on manual data transfers, leading to an inefficient and bloated repository of reports with overlapping information.
We transformed reporting by creating dynamic dashboards that align with business priorities. Using advanced tools and working closely with the team, we used an iterative process to develop solutions rapidly to meet specific needs.
Switching to dynamic reporting greatly improved efficiency and the quality of reports. The new system's quick response and automatic alerts have increased stakeholder engagement and made the organization more transparent. Tighter integration of IT and business has created a shared understanding that will help with faster development in future projects.
