Blog

Theory of constraints: how to expand bottlenecks in data analysis
When implementing analytical projects, it is not too efficient to try to improve the system as a whole. Let’s take a look at the theory of constraints to see how low code development helps reduce the...
Release notes 7.1.0
In the new version, we made fixes to the application, the Export group components, database Connections, Association rules, and other components.
The Apriori algorithm for association rule learning
Apriori is one of the most popular algorithms for determining association rules. Employing the property of anti-monotonicity, Apriory is able to process large volumes of data within a reasonable amount of...
What's new in Megaladata 7.1
The First Major Update of the 7th Megaladata Version While working on the release, considerable attention was paid to the requests that came from our users. The main changes affected safety and usability...
Data quality criteria
Data quality is a generalized concept that describes the degree to which information is suitable for analysis. There are a number of criteria used to assess the correctness, completeness, accuracy and...
Scalable CLOPE algorithm for clustering categorical data
Splitting of categorical and transactional data arrays into groups with similar attributes is the most important task of data mining. In most cases, traditional clustering algorithms are not effective when...
Smart sales analysis for wiser business management
The combined use of ABC and XYZ analysis will help you to better navigate the product range, optimize logistics and warehouse stocks, segment customers and partners, and properly set up interactions. ...
Implementing a decision support system: A case of one financial organization
How to migrate to a modern analytical platform and replace the database without stopping the work of the decision making pipeline. An actual case of a leader of the microfinance market.
Release notes 7.0.4
Several regression errors were corrected, including in Connection to databases. Errors were corrected in Megaladata Integrator, ARIMAX and other components.
Clustering algorithms in data mining
This article is an attempt to systematize the latest achievements in the development of data clustering techniques. The purpose of this article is not to providea detailed description of all clustering...
Release notes 7.0.3
Errors were corrected in several Connections to data sources, visualizers, Calculator, Coarse classes, Date and time components and also in the components included into Import, Data tree, Programming and...
Genetic algorithms as a mathematical apparatus
Genetic algorithms aim to solve optimization and modeling tasks by iterating, variation and combining target parameters with the help of mechanisms similar to biological evolution.
Release notes 7.0.2
There are many fixes in visualizers, import and export components in the new version. The errors occurred in Megaladata 7.0 were corrected. Support of multiline values import and parsing of dates in ISO...
Customer segmentation by loyalty or RFM analysis
Every entrepreneur in the process of business development is faced with the question: how to make his client more loyal and not let him go to a competitor.
Release notes 7.0.1
Errors in Megaladata Integrator, import from file sources, Replace node and operation of other handlers were corrected. Changes were introduced into BatchLauncher utilities operation. Performance of...
How to clean data before uploading it to a storage
When you create a data warehouse is not enough attention is paid to cleaning the incoming information into it. Apparently, it is believed that the larger the storage, the better. This is a surefire way to...
Detection and correction of one-dimensional outliers in data
Many analysts face the need to process outliers when it comes to data. Thankfully, Megaladata has a built-in component for quickly determining extreme values with the ability to automatically delete or...
Release notes 7.0.0
Many corrections were made in the components connected with databases and web services operation. Operation of some components was improved.
Building customers' loyalty
What is customer loyalty? Is it repeated purchases? Or an emotional connection that is manifested, for example, in the fact that the client recommends your company to their friends?