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Business Intelligence
Automated Analytical Reports

Business Intelligence – example applications

Business reports and analysis

  • analytical reports
  • process monitoring
  • corporate reports
  • reports built ad-hoc
  • management panels
  • data exports to external formats: PPTX, PDF, Excel, etc.

Data mining and dredging

The data mining process relies on data selection, compilation and analysis.

Some examples of the opportunities from data mining for the company:

 

  • How can we optimise the production costs?
  • What is the product availability status over time?
  • What is demand planning on the basis of WFA/Attainment indicators?
  • What is the expected return on planned investments next year?
  • Which clients are most promising and which may move to the competition?
  • How can a company loyalty programme be strengthened?
  • Can potential fraud be detected?

Data warehouses

This kind of engine allows ultra-fast searches and analyses all your content efficiently, meaning that ownership results in numerous benefits:

  • consistency in information – all data stored in the warehouse has one common form
  • elimination of errors – before the data is loaded into the warehouse, all possible inconsistencies are identified and levelled out – the reporting and analysis process is significantly improved

Big Data and stream processing

  • We primarily associate Big Data as datasets with a significant amount of data, speed of processing and diversity in the data.

  • When it comes to Big Data, we typically process large amounts of unstructured data. For some companies, this can be several dozen terabytes of data (e.g. corporate/corporate department level). For others, this may involve hundreds of petabytes (e.g. group entity level).

  • Speed means receiving and processing data from sources at high frequency and using it for presentation in an appropriate structure. Some smart devices with internet access operate in real or near-real time, and require real-time assessment and action. For example, IoT allows a company to monitor shipments in real time and incorporate data on route conditions into decisions that can save a lot of time and money.

  • Diversity means handling many types of data. With the development of Big Data, new and unstructured types of data have begun to be collected. Unstructured and semi-structured data types, such as text, audio and video, require additional pre-processing to bring out their business relevance.
  • Stream processing is a big data technology that focuses on the real-time processing of continuous streams of variable data – providing immediate results from their analysis. 
  • Benefits include immediate detection of conditions and anomalies in a very short time, which is useful for tasks such as early fraud detection.
  • Streaming data processing systems in Big Data are effective solutions when we have operating scenarios that require: minimal latency, built-in features to handle imperfect data, SQL queries on data streams to build extended operators, guaranteed ability to generate predictable and repeatable results, stored and streamed data integration abilities, fault tolerance features, guaranteed data security and availability, real-time response ability with minimal load for high-volume data streams and the ability to automatically scale applications between multiple processors and nodes.
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