Excel is a software product that needs no introduction. However, despite its popularity, it has several limitations that create difficulties in solving complex analytical problems. In this article, we will...
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Solving Typical Excel Problems Using Megaladata
What’s New in Megaladata 7.2
We have added a built-in Job Scheduler and a new OpenID authentication method employing Access Token. The list of data trees-related components was enhanced with the Calculator (Tree) component. Several...
Low-Code Analytics at Top Speed
The Megaladata platform employs methods that ensure high-speed processing, efficient memory use, and easy scaling. Let's take a deeper look at what enables the platform's high performance and how...
Megaladata: What's Under the Hood?
The article describes the technological basis of the low code Megaladata platform — its components, architecture, frontend and backend, functionality, and performance. We intend to answer the most...
Cohort Analysis: Why Is It Valuable?
Every entrepreneur striving for effective business management comes to ask themself a question: What is the income brought by one client?
Coding in Megaladata: Python vs. JavaScript
This article covers the technical aspects of using programming languages in Megaladata. We compare the two languages, JavaScript and Python, analyze their limitations, and provide guidance on how to use...
Shewhart Charts as a Tool to Control Business Processes
Every business experiences failures in business processes from time to time. Finding and eliminating the causes takes not only time but also money. It is crucial to perform real-time monitoring of the...
Advanced Analytics Platforms and BI Systems — What's the Difference?
Advanced analytics platforms and Business Intelligence systems employ different approaches when dealing with data. Although data analysis takes place in both cases, there are significant differences between...
Release notes 7.1.4
Fixed: critical error in Calculator, Megaladata Integrator issues, visual problems in the workflow area. Package installation optimized.
Missing Data Imputation
Missing data is a very common issue in real-world data processing. The reasons may vary — data entry errors, information hiding, fraud, and so on. In this article, we discuss cases where incorrect handling...
Release notes 7.1.2
In the Server editions, it is now possible to connect to SAP HANA through ODBC. Fixed: Megaladata Integrator, Calculator, and some data source connections. Platform performance increased, logging...
Data Leakage in Machine Learning
In machine learning, data leakage refers to a situation where one or more input features used during the model's learning process become unavailable when the model is applied in practice. Data leakage...
Release notes 7.1.1
Fixes made to visualizers, PostgreSQL connection, and some components. Megaladata Desktop Edition performance improved.
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 implemented fixes for the application, including the Export group components, database connections, association rules, and other related 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...
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.
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. ...
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 provide a 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 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 defined solely by repeated purchases, or does it also include an emotional connection, such as when a customer recommends your company to others?