Technologies are the basis, methods create the solution. For our bespoke solutions, our Data Scientists master the world’s leading methods and technologies.
ETL stands for “Extract, Transform, Load”. Data Analytics starts with information merging – the aggregation of data from different data sources, which takes place through a chain of ETL processes. By merging of data from different sources new knowledge about dependencies and patterns can be generated.
Inconsistent, incomplete or incorrect data are among the most common problems in business day. Errors in data sets continue and multiply – up to top management reporting. Poor data quality is not only annoying at the operational level, but can also lead to wrong decisions. Data Quality Analytics includes all the necessary components to extract, clean up, reconcile, consolidate and reconfigure data from ERP systems and various source files, from social media or commodity exchanges. Intelligent algorithms across all master data and booking data also identify excessive or unauthorized payments to suppliers. In addition, duplicates can be identified in supplier, material and customer master data, and local data can be enriched with further information. Data Quality Analytics creates the basic prerequisites for optimized processes, processes and applications in your company: Consistent data, which is reliable for 100%.
Do you have to make quick and fair decisions? Then you need a permanently up-to-date database as a reliable decision-making basis. Realtime Analytics means that we can create a dashboard that visualizes information (such as raw material costs or transportation information) in near real-time. In addition you can be informed, e.g. via SMS or e-mail alerts, about important changes of values (e.g. price changes). Automated processes (for example, triggering an order when certain conditions are met) are also possible.
The exponentially growing data contain a huge amount of information for the identification of cost reduction possibilities. Using methods of exploratory and statistical data analysis, we analyze your data, such as vendors, purchase requests, purchase orders, postings, payments, quotations, contracts, parts lists, work plans, and market data. Automated price evaluations are also possible, e.g. of bills or offers in combination with raw material prices on the market using statistical methodology.
Thanks to sophisticated visualization capabilities of your data and the analysis results, we also make complex facts easily accessible to you and guide action recommendations from the analysis results.
With Predictive Analytics complex economic contexts can be predicted for fact-based decisions. PREDICTIVE ANALYTICS is often used for risk assessments in purchasing and supply chain management. Data mining methods and tools are an integral part of Predictive Analytics solutions to identify patterns in data stocks.
Virtually all operational business processes are recorded in the IT systems, primarily in the ERP system. In turn, a company’s IT systems generate daily data that often remains unused for a process analysis, but is the best basis for recording the actual processes.
Process Analytics is a method in which the actual processes are reconstructed with the aid of your data and visualized graphically in a flow chart – and this can be automated and real-time!
Machine learning describes the artificial generation of knowledge from experience. An artificial system learns from examples and can lump sum after the learning phase. That is, rules are recognized in the learning data. This allows machine learning to evaluate unknown data. The numerous application fields in supply chain management include automated scheduling or optimization in order timing.
There are similarities to Knowledge Discovery in Databases and Data Mining, where, however, it is primarily about finding new patterns and laws.