In addition, almost all modern organizations have a data-driven strategy and want to make more use of dashboards, analytics, and machine learning. We do see that most organizations are still struggling with the quality and availability of data. 

A recognizable problem is that unlocking data does not succeed or is too slow. We, as AMIS Conclusion, are regularly asked to speed this up. A modern data platform is an important accelerator for the implementation of data-driven working. It is also the place where data engineering is necessary.

Data engineering is the collection and use of data for large-scale data processing for reporting, analytics, and machine learning. You can think of it as the robust plumbing of the data world that ensures that the data is always in the right place, of the right quality, safely and reliably in the right place. Just like you can rely on hot water from the kitchen tap at any time of the day. 

AMIS Conclusion has more than 30 years of experience in data engineering in data-intensive organizations. We realize and manage the data platforms and ensure that our customers' data users (e.g. in the field of BI, analytics and machine learning) can focus on applying domain knowledge to extract real value from data for their organization. 

We make the difference for our customers by combining our years of experience with data access through all kinds of integrations and links with the application of our software engineering principles. Examples include working under source control, continuous quality validation, automation of repeatable tasks, working according to best practices and peer review, and the use of standards. In addition, we set up customer-specific data workspaces, in which the data is made available to data analysts and machine learning experts in a reliable and accessible manner. 

With our data engineering services, we deliver the following added value: 

• Having access to a generic foundation that serves as the basis for the diversity of reports, analyses and machine learning models.
• Data is reliable because we provide insight into origin, meaning, quality, availability and topicality, so that users can fully focus on creating business value.
• Source and user systems are connected modularly, so that each component can scale independently as needed.
• The data accumulation and processing process is proven and repeatable, so that it is clear how the conclusions are reached.
• Have access to an environment in which experiments can be carried out. 

Our data engineering activities deliver measurable results for our customers, for example in terms of cost savings, sustainability, compliance, reduced manual labor and new business models. 

• At a healthcare organization, we have achieved a 20% reduction in the workload of medical examiners by better detecting high-risk cases based on data. This has also improved patient turnaround time by 30%.
• For a company in the feed chain, we have been able to increase the output of the data analytics team by 150% by automating much of the data engineering work.
• Thanks to our help, an international trading company is able to share large-scale datasets with current data (daily figures) of reliable quality with the entire organization, at no extra cost to the IT department.
• For a transport company, we have created insight into the current status of the goods and equipment, which has made the planning 15% more accurate.
• For a trading company, we created a data catalog accessible to more than 300 data professionals and 150 low-code data applications. This has led to a 30% growth in new and innovative data solutions. 

With our many years of experience in software engineering and enterprise data processing, we are uniquely positioned to carry out data engineering projects effectively and successfully. Our best practices and application of reference architecture ensure a future-proof and sustainable result. In doing so, we make use of our accelerators that provide a solid and manageable foundation that fits into an environment that is scalable and subject to active lifecycle management. We take an iterative approach with a DevOps team, combining short-term results with a clear vision for the future. We also secure the necessary knowledge for the realisation of such a platform with links in order to arrive at data services (Data-as-a-Service).