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Data analysis is the process of making better-informed decisions by generating useful conclusions from the data. It involves the usage of data analysis tools- software and applications- by skilled people who can interpret the results. Currently, there exist a multitude of data analysis software in the market. However, only the experts of this field know which tools to use for the desired goals. In this guide, we will present leading tools wisely selected by professional analysts and mostly used in the industry.
Instead of merely presenting a list of data analysis software, we grouped them into categories based on their working style and benefits.
Business Intelligence tools are among the frequently used data analysis applications. Such software enables analysts to dive into the details, monitor, and generate useful findings from the data set. Besides, BI software applications deliver effective functions like self-service, advanced SQL modes, or predictive analysis. In this way, these tools do not require extensive IT skills and ensure that people with different levels of know-how can utilize them. A list of data analysis software for Business Intelligence includes SAP BusinessObjects, Dundas BI, Geckoboard, Sisense, Oracle BI, Tableau, Domo, etc.
A variety of statistical tools help analysts to manipulate, explore, and come to insightful conclusions. Such data analysis software makes the tasks of data analysts easier and faster. They bring functions that enable data scientists to perform hypothesis testing or compile descriptive statistics. Each data analysis program in this category has offerings, speed, or requires different levels of the learning process. One of the big data analysis software in this category is SPSS(Statistical Package for the Social Sciences). It not only performs parametric or non-parametric analysis but also enables users to carry out advanced processing by creating scripts. Another frequently used statistical tool is R-studio. This application was once used mostly by academicians, but currently, many large companies like Google or Facebook utilize it. Other members of this type of data analysis programs include MatLab, SAS, GraphPad Prism, or Minitab.
Python is one of the big data analysis software. Compared to other tools, it is easier to use with its simple syntax. Whether a user wants to perform data cleaning, modeling, or constructing algorithms, Python provides high-quality results. The compatibility of this programming language is also worth mentioning. The code is written with Python mostly runs in the other operating systems without requiring drastic changes or new code writing. For example, a code can be run both on Windows and macOS without errors. Big corporations like Netflix or Reddit utilize this application.
A data model is how the data parts, such as description, semantics, or consistency constraints, are organized. The modeling process is required to define how to store the information in the database. This data analysis software is also a visual representation of data and helps to apply business rules or other policies on the data. Oracle SQL Developer, Archi, Erwin, PowerDesigner, ER/Studio are some examples of data modeling tools. They increase productivity and decrease errors in the data analysis process and provide ready solutions for business needs. For example, the Erwin tool can work both with structured and unstructured data. Whether you want a logical or conceptual model, Erwin can help you accomplish the tasks.
ETL tools help data analysts to adjust the data from another source to their own system. During this process, the data is copied but represented in a different way. Data transformation involves cleaning or changing the structure, while loading is about making the data available in the destination system. Hevo is one of the big data analysis software belonging to this category. Its features, like easy implementation and automatic mapping, make it famous in the industry. Besides, it provides alerts to keep the analyst informed. Another data analysis program in this list is Skyvia. This ETL tool requires little technical knowledge and is able to import data without duplicates. If you deal with common integration issues, Skyvia offers predefined templates. Other members in this list of data analysis software are Talend, Xplenty, DBConvert, or Sprinkle.
Spreadsheets are one of the common, simple, and traditional data analysis software. They are popular in the industry due to their long history. Besides, spreadsheet applications are easy to learn. Even if you do not possess a high level of technical knowledge, you can use such applications professionally. However, on the negative side, their capacity can be lower and the functions implemented are limited. A simple example of such data analysis programs can be Microsoft Excel. Users mostly utilize pivot tables and different functions for small analysis processes. Calculating some numbers, representing the results in visually understandable ways, creating charts, tables, or graphs are possible with this tool.
Our list of data analysis software categories would not be complete without data visualization tools. Such tools help analysts to present their findings to other entities which usually lack technical knowledge. The most basic examples of this category can be Excel and PowerPoint. However, these tools are not preferable for advanced analysis because they cannot meet the requirements. For huge and fast-changing data sets, Tableau can be effective, while Highcharts makes it possible to view and run the visualization across different browsers. There also exist other data analysis software for data visualization such as Sisense, Plotly, Datawrapper, FusionCharts, etc.
We have presented different categories of data analysis software and mentioned some tools belonging to these categories. All these tools have their own benefits and disadvantages. While choosing the most suitable one for your business operations, it is better to get more information about each possible option. Besides, it is recommended not to choose one without understanding the needs of the business. A top software that works for one organization does not mean it will work for others at the same productivity level. As businesses have diverse needs, their expectations from the data analysis programs should vary.
The easiest and fastest way of ensuring quality data analysis can be through outsourcing companies. Such companies have experience in the field, and they know which tools to utilize for achieving clients’ goals. Instead of finding analysts and then choosing tools, you can rely on the service providers. In this way, businesses will save time, energy, and cost of establishing in-house data analysis teams or acquiring data analysis software. If you decide to get help from expert service providers, you can evaluate your options and choose the most suitable one by using the Gygzy platform.