BI, Business intelligence, Data analisys

Business Intelligence (BI) solution for data analysis and distribution, and reporting.

Turns data into information essential to managers and executives for faster, easier and more accurate decision making!

IN-Memory Database System - fast and intuitive

An In-Memory Database System is a database management system that stores data entirely in main memory. This contrasts to traditional (on-disk) database systems, which are designed for data storage on persistent media. Because working with data in memory is much faster than writing to and reading from a file system, IMDSs can perform applications’ data management functions an order of magnitude faster. Because their design is typically simpler than that of on-disk databases, IMDSs can also impose significantly lower memory and CPU requirements.

The basic functions

  • Extracting data from a data source to an in-memory star schema
  • Cleansing of inconsistent data
  • Data clustering and data classification
  • Data analysis

How is data extracted from an ERP system or other sources?

Data is usually located in a transactional relational database of your ERP system, in another dislocated database or even in various spreadsheets. The system automatically extracts data from all data sources according to configured refresh intervals. It is not necessary to request a report and wait for it to be delivered. There is no need to use the so-called OLAP cubes that hold only a portion of the data. All required data is extracted and held in one place, which is achieved using a specialized data-mapping module. Once configured, it can persistently perform data extraction and loading according to set rules.

What is Data Cleansing?

Data Cleansing is the process of unifying the same business entities to a single identifier. Transactional data in your ERP system may contain errors that do not hinder regular business operations. However, during data analysis those errors can bring an analyst to a completely incorrect conclusion which can lead to wrong business decisions. In branched organizations, data (e.g. the names of items or suppliers) is entered from various places and through various processes. It often happens that two different persons can’t find the corresponding unique identifier and open a new one (e.g. on the basis of suppliers’ invoices). This does not hinder payment and the system will still work, but once we ask a question such as “How much did we spend on this item or with this supplier?” we cannot get the correct answer without performing data cleansing. MaxyT contains a specialized data cleansing module that can provide a unified point of view at the data and eliminate these problems during analysis and decision making.

Why do we perform data enrichment through Data Classification?

Spend Management Classification (SMC) is the process of grouping spend data in groups that allow managers to control and manage spend. All spend is classified according to the “chart of accounts”. Any single spend data from the chart of accounts is mapped to a single spend category in the SMK. MaxyT allows using more than one predefined classifications (CPV, UNSPSC) or a custom classification. A user can quickly define a series of business rules and thus create a new classification, which is a new way of monitoring and viewing spend data. New data that enters the system is automatically classified according to predefined rules. All classifications can be used during data analysis.

What are the benefits of analysis in MaxyT?

Analysis in MaxyT provides spend visibility and points to potential savings. All available cleansed and classified spend data is included in the analysis process. Data can be analyzed according to all dimensions (Time, Materials, Suppliers, Orders, Invoices, Chart of Accounts, etc.) and classifications (CPV, UNSPSC, custom classifications, etc.). The data that is being analyzed can be displayed using tables or graphs. The analytical module in MaxyT is especially designed to meet the needs of the end user. Because of its intuitiveness and the speed at which it processes and visualizes data you can start analyzing spend in your organization only after a few hours of training. The data on which the analysis is performed is automatically refreshed, cleansed and classified which allows making quality business decisions.