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Review of Top Analytical Processing Tools for Business Intelligence Operations

Business intelligence is growing tremendously lately. Predictive analysis is being used even by the smaller enterprises, and it is expected to grow to more than ten billion in the next couple of years.

Many software giants are releasing their analytical processing tools and enriching the same with many features to meet the increasing demand.

There is the introduction of complex algorithms, artificial intelligence, machine learning, and deep learning techniques to improve decision-making efficiency. These tools making business forecasting and decision making much easier and effective.

Review of Top Analytical Processing Tools for Business Intelligence Operations

Let us explore some of the top business analytics tools available in the market.

OLAP – Online Analytical Processing

OLPA is a complex computing approach that can answer many analytical queries at a faster pace and can also make decision-making and forecasting much easier. OLAP is a subsidiary of intelligence or BI. It holds relational database management, data mining, and data reporting much effectively.

In simpler terms, OLAP encompasses relational data management, data mining, and reporting. OLAP tool will let the users analyze the multivariant data from different perspectives.

Most of the OLAP tools are built on three fundamental analytical operations:

  • Consolidation: These are also called roll-up operations, which perform data aggregation, which can be computed in various dimensions. For example, the retail stores are trying to forecast the trends in the retail market.
  • Drill down: It is a contrasting approach to the above, through which the users can scroll through the data in reverse order of consolidation. As in the above example, users can view the individual products’ retail patterns using a drill-down approach.
  • Slicing and dicing: In this approach, the users may take out a slice (a sample or set of data) known as OLAP cube and use slicing it from various perspectives to see the data trends.

The configured databases using OLAP tend to use a multi-dimensional model, which will let users computer various complex ad-hoc analytical queries easily and quickly with lesser processing time. 

Also Read: Bureaucracy In Small Business – 6 Most Common Issues And How To Avoid Them

Choosing ideal OLAP tools

There are a lot of OLAP tools available in the market, which will enable effective data analytics. However, there are some key analytical features that you need to look for in an excellent like flexibility, user-friendliness, leveraging parallelism, performance, metadata layer, security features, etc.

So, you should consider these features while analyzing various OLAP software. For expert advice in this regard, you can approach consultants like RemoteDBA. Further, let us explore some top OLAP tools with these features.

1) Xplenty

Xplenty is a comprehensive data OLAP toolkit that helps to build the data pipelines. It also offers many features to process, integrate, and prepare data for BI. It also features low-code, code-intensive, and no-code development and querying capabilities.

The No-Code and Low-Code options will let to create the ETL pipelines easily, and the API components of Xplenty will offer you flexibility for advanced customization.

Xplenty is an elastic and scalable platform that can easily handle monitoring, deployments, security, scheduling, and maintenance. It also features many intuitive graphic interface tools to help you implement ELT, ETL, or replication.

It also offers multiple solutions for sales, marketing, customer support, and also for developmental needs. Users also can get comprehensive customer support through chat, email, phone, and online meetings.

2) IBM Cognos

IBM Cognos features a web-based and integrated analytical processing toolkit. It features a unique set of tools to perform data processing, analysis, and reporting, along with features to monitor various data-related metrics.

There are also many in-built components on Cognos that will help you meet various enterprises’ information requirements.

All these components have a basis for Windows: IBM Cognos Framework Manager, IBM Cognos Transformer, cube designer, map manager, IBM Cognos connection, etc.

The IBM Cognos Report Studio can create the reports that are shared using different knowledge processing applications. It also gives the flexibility to process different reports, including lists, charts, maps, and repeated functions.

3) MicroStrategy

MicroStrategy is a licensed OLAP tool from a provider offering various business intelligence and mobile software applications globally. MicroStrategy Analytics offers companies to analyze the huge volume of data and effectively offer business-centered insights across the enterprise securely.

MicroStrategy is capable of delivering dashboards and reports to the users, and the analysis and reports can be shared through mobile devices and email.

It is a highly secured and scalable application featuring good governance for higher-end BI applications. You can get various versions of MicroStrategy as on-premises as well as a host-based service cloud. 

4) Palo OLAP Server

Palo is a multidimensional OLAP server that features many BI applications and can help with process controlling and budgeting. This tool is from a company named Jedox AG, which offers many BI solutions. Palo features spreadsheet software as its UI.

It allows various users to share the centralized DB, which also acts as a single truth source. It features high flexibility to handle various complex data models and lets the users have a better insight into the statistics.

Palo works on real-time data and also can consolidate data using multidimensional queries. One major feature of Palo is that it can store the run-time data in its memory to offer quicker and easier data access to all users.

Also Read: Insurance Digital Transformation: Trends And Best Practices

5) Apache Kylin

Apache Kylin is an open-source data analytics engine with a multi-dimensional approach. It offers an SQL interface and MOLAP, which is synchrony with Hadoop for supporting larger data sets.

It can also support quick query processing in different steps. Using Kylin, you can identify the star schema and build the cube from given data tables. It can also run queries and get results through APIs. Overall, Kylin is built to reduce the time taken for query processing and ensure quicker processing of data rows’ trillions.

Suppose you are looking for more OLAP tools for your enterprise business intelligence applications.

In that case, you can explore Mondrian, icCube, OBIEE, Clear Analytics, DBxtra, Pentaho BI, JsHypercube, Jedox, Phpmyolap, HOLOS, SAP AG, Bizzscore, NECTO, Jmagallanes, HUBSPOT, etc., which all help to design predictive analytics strategies based on the existing data and systems to be used by organizations in various functions supply chain management, CRM, marketing, human resources or ERP, etc.

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