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Analytics

Predictive and Artificial Intelligence

ANALYTICS

Data and processes can be optimally combined in the BPM software (BPMS) with our modules for analytics. For example, we use Business Intelligence (BI), BigData, Predictive, Machine Data and Artificial Intelligence (AI) directly in BPMN process models for this purpose. As a special feature, MR.KNOW - BPM can also process unstructured data for intelligent process automation (iBPM) in live processes. You can therefore use predictions for risk assessments and utilisation control in order processes, goods management or also machine data, for example. Furthermore, you can make it possible for digital assistants and digital workers to recognize, talk and learn on their own using AI.

Preparing data.

Start with the data analysis

During data analysis, we support our customers in intelligent process automation (iBPM) with different tools such as IBM Warehouse, BI, Data Management as well as "R" Studio or Process Mining. The focus is always on working out objectives in procedural use. The focus is therefore on the process, which can then be supplemented with the necessary data. Of course, processes are continuously analysed in the BPM software (BPMS), evaluated with dashboards and reports, and continually optimized.

Using predictions.

We bring data and processes together

With the predictive processes of MR.KNOW, companies can use data and predictions for intelligent process automation (iBPM). We provide the next generation of technology solutions so that applications no longer have to be coded, but created automatically from BPMN 2.0 models. This means that data can be modelled directly and visually linked, e.g. in IBM SPSS and our BPM software (BPMS). You can make use of predictions e.g. for maintenance, goods management, control systems (ICS), human resource planning, machine utilization or even crime prevention.

Using Artificial Intelligence.

A learning system

Digital workers can be enhanced with artificial intelligence (AI) with MR.KNOW as part of the intelligent process automation (iBPM). This allows them not only to integrate data and process it in the procedures, but also learn from it. We also use AI software such as Alexa, Google Assistant, IBM Watson, etc. in voice, image and text recognition and can connect ontologies with process models in our BPM software (BPMS). Additional modules from start-ups and partners make intelligent applicant management or the digitalization of legal advice possible, for example. Your digital employees therefore become cognitive assistants and digital workers.

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  • Check-up for the use of data in processes
  • With predictive processes from MR.KNOW, companies can use data and predictions for automated processes.
  • We offer THE next generation of technology solutions so that applications no longer need to be programmed, but created automatically from models and controlled with data.
  • In doing so, we extend your existing BI or analytics tools or assist you with the data analysis.
  • As a special feature, MR.KNOW - BPM supports the processing of all types of data in running process, including dynamic data. Data can therefore be combined with processes, which react automatically.
  • Dashboards can be created, Excel evaluations can be replaced, processes can be tracked, complex data evaluations and plans can be created, and simulations and reporting can be generated.
  • MR.KNOW - BPM allows your data to be used in solutions such as automated control mechanisms in risk management, control of goods management / goods rotation in retail, utilisation and human resource planning or capacity planning for travel and tourism.
  • MR.KNOW - BPM can handle both structured and unstructured data; bi-directional processing is possible
  • Predefined reports and dashboards, e.g. for the Internal Control System (ICS) solution, knowledge management or complaint management
  • Processing of ontologies directly in BPMN process models
  • AI software as an extension for voice, text and image recognition
  • Combining and evaluation of different data sources
  • Evaluation and analysis of large quantities of data (BigData)
  • Business Intelligence connections (e.g. Qlik, IBM Cognos, SAS, jedox, tablaeau etc.)
  • Predictive tool linkage (e.g. IBM SPSS)
  • Machine data usage (e.g. HYDRA MES)
  • MS Excel integration, web interface, MOLAP, data warehouse, data integration and system connections
  • Classification, linking, splitting, segmentation, forecasting and management tool
  • Direct interaction and views, such as in P&L planning with details of the underlying figures, risks and process flows
  • Visual mapping of BigData sources and targets
  • Can be used with leading Hadoop distributions
  • Combination and use of data in processes
  • Relational databases: Oracle, MySQL, SQL Server, DB2, PostgreSQL, Progress OpenEdge
  • Big Data: HP Vertica, Amazon Redshift, Hadoop (Cloudera, Hortonworks, MapR), MongoDB, 1010Data, Infobright, Snowflake, Google BigQuery, SAP Hana
  • Files: XML, Excel, CSV, proprietary data sources
  • Any integration form: ETL, ELT, batch processing or real-time
  • Various analysis options (e.g. Monte Carlo simulation), dashboards and reports (PDF, HTML, Word, Excel)
  • AI extensions and processing of ontologies directly in BPMN process models

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