Make data available for analytics and provide easy access to create your own reports and gain further insights. Clean and harmonize data and put it into context with other data from different applications.
Endless data in a wide variety of data qualities presents decision makers with tricky data challenges. Thus a clear data management strategy is required to enable businesses to collect, process and create business value with the large amounts of data available today. One targeted solution for dealing with great amounts of data of varying qualities is data extraction.
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With Data Extraction you can easily access your data, create your own reports and place information into context. In companies, up to 80% of all data is unstructured and it costs employees a lot of time to analyze it. Thanks to artificial intelligence, these analyses can be performed automatically and accurately. Data extraction retrieves data from various sources. In order to be able to process it further, the data is migrated to a data repository (e.g. a data warehouse or data lake) where it can be analyzed automatically.
The concept of data extraction and data exchange is complex and cost-intensive, the further processing of the data is typically not much simpler. We have experience in data extraction from SAP, Salesforce, Microsoft, Third-Party Apps, REST APIs and many more. This allows us to load the right data in the right format, with the right tool, to reduce costs and drive efficiency.
Today many employees still have to manually extract data from IT applications on a recurring basis and lose important time to create new business value. Using automated processes, repetitive work can be automated and quality raised.
Integrating data from one application into another application based on defined triggers is easy today and enables stronger automation along business processes. Copying information from one application to another is no longer important.
Data extraction reduces manual and transmission errors. By automating these processes, automated controlling and enhanced data validation, there are fewer errors and higher accuracy in reporting.
Due to automation there is less need for manual processes as data flows automatically, approvals are tracked within the system and end-users have one portal to access the information.
The use of strategic insights derived from extracted data enables businesses to pursue new business opportunities or to extend their core business.
Auto-extraction generates real-time data using automated workflows to respond directly to market changes. Fewer bottlenecks and delays become possible. Data Extraction also leads to faster data entry with fewer errors – saving time in everyday work.
Unstructured data holds great value to those who can extract and structure it – meaningful information hidden in various sources comes to light and allows for data-driven decisions.
Manual processes in data extraction are repetitive, demotivating and tiring tasks for employees. Automated data extraction creates room for focusing on meaningful tasks and improves productivity.
Algorithmic natural language processing as a subcategory of Artificial Intelligence and one of the main use cases of Deep Learning.
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A scientific approach to extracting and examining data from multiple data sources to evaluate how the data relates to each other. Accurate targeted data interpretation adds value through informed decision-making.
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Expertise in single repositories for all enterprise data used for reporting, visualization, advanced analysis, and machine learning.
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Setting you up with a central database optimized for analysis purposes.
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Profound changes in organizations to meet changing conditions. Decisive success factor in competition.
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Simulation of natural intelligence in machines programmed to learn with experience and interpret contexts. Able to interact and cooperate with other digital systems.
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