What is Data Analysis Software?

Data Analysis Software is a statistical analysis tool used to inspect, clean, transform and model data for deriving information. It is used to understand and analyse the data to produce facts that may be further utilised to reach quick and important decisions.

Data analysis can be quantitative or qualitative, based on your research methodology. For instance, interviews and conversations qualify as qualitative research, and surveys are qualitative data analyses.

Researchers, academicians, scientists, engineers, and commercial industries use data analysis tools and techniques for mining data. Data analysis tools help users reach conclusions that could be further used for verifying theories, existing scientific models and hypotheses.

Features of Data Analysis Software

• Data Visualisation: The dashboard includes graphs, charts, maps, sparklines, and data visualisation features to make data analysis easier. Both paid and free data analysis tools allow for organising visuals in the dashboard with the help of various tabs/buttons. Dashboards present information in a very presentable format so data can be easily interpreted.
• Drill-Down and Roll-Up: A drill-down and roll-up feature is used for a more detailed analysis of the reasons for falls and rises in sales. Using the drill-down option, you can learn more about the reasons for declining sales and conduct further research to identify those causes. On the other hand, the roll-up feature is used for calculating the factors behind the profit incurred so far.
• Service Call Reporting: Service call reporting ensures transparency in the customer services team and tracking agents, specific calls, etc. It is used for training staff, volunteers and agents. Data analysis tools are a great support for finding more information about the efficiency and performance of customer services in your department.
• Flexible Data Sources: You could use different data schemes on your system. Using another data source could involve integration issues that may not let such analysis tools work efficiently on your computer. However, flexible data analysis software ensures compatibility with the existing programs in your computer system.
• Easy Result Formats: Companies of all sizes use data analysis software to make decisions. These decisions are made for setting future goals and developing strategies. Thus, real-time results provided by data analysis software offer a clear and in-depth analysis of different business scenarios.
• Raw Data Processing: Data analytic models collect data from various sources such as text files, Microsoft Access, Excel, etc. and import them into data analysis software for interpretation purposes. The software's data processing capability helps collect information from multiple formats, thus successfully saving the time required for data conversion. In addition, the collected information can easily be organised.
• Identity Management: Data analysis software's identity management feature monitors data protection, access and identity issues. This feature is necessary for supporting a computer system, protocols, and network passwords. Identity management application has fraud analytics and other security features that ensure that only authorised users can access the system.
• SSO Feature: The single sign-on feature saves users from the hassle of signing in multiple times during the same session. Data encryption converts readable information into unreadable ones through several codes and algorithms, providing security and safety to computing systems.
• Scalability: As a company grows, so does its data. The more rapidly a business progresses, the more rapid the data enhancement process. Data analysis software for beginners and professionals is highly scalable, and the data can be easily scaled up for assessment purposes. Furthermore, previous data can also be integrated into the new one.
• Quick Integration: Different individuals and organisations use different sources for storing and sharing data, and a huge range of devices and sources are available for this. Data analytics models allow data to get easily integrated with cloud apps, warehouses, and databases. Easy integration allows quick sharing of information with developers and data scientists.

Types of Data Analysis Software

• Quantitative Data Analysis Software: Quantitative data analysis software like SPSS performs statistical analysis on data from databases and spreadsheets. SPSS has a core feature of missing value analysis to fill in missing data, followed by data preparation and management.
• Qualitative Data Analysis Software: Data analysis software analyses qualitative data such as interviews and transcripts. It uses Boolean text coding and retrieval for quick analysis and offers code frequency analysis with visualisations like bar charts, tag clouds, and pie charts. Graphs can be exported in BMP, WMF, and PNG formats.
• Big Data Analysis Software: Big data analysis software handles a huge amount of data with the help of various programming models. This software is programmed to handle all types of data, such as JSON, XML, images, video and plain text. It is possible to process, administer, collect, discover and distribute a large amount of data through data analysis software.
• SAS Analytics Software: SAS analytics software is used in business intelligence, machine learning, and data science to analyse, report, and mine data and perform predictive modelling. It offers visualisation features that present information from large datasets in a neat and presentable format. The software's analytics feature generates comprehensive reports that aid in decision-making.
• Log Analysis Tools: Log analysis tools are meant to identify potential threats and issues in a system. Through the software, it is possible to keep a tab on your devices, systems, workstations, servers, and networks. Log management tools are increasingly being used by engineers, system engineers and web developers to ensure their systems' security.
• Statistical Analysis Software: Statistical analysis software is used for collecting, organising and interpreting statistical data. The software is primarily used to identify market trends for making investment decisions. The factual data produced by this software is crucial for organisations to take vital business decisions.

Benefits of Statistical Analysis Software

• Comprehensive Integration Support: Online data analysis software can integrate with various data sources, including CSV files, web services, SharePoint lists, and databases. It supports streaming big data, news feeds, social media, BI platforms, and excel spreadsheets for easy data connectivity.
• Data Cleansing: Analytical tools correct the errors from data sets by cleansing those mistakes. This procedure ensures that the data quality is up to the mark and of mutual benefit to customers, institutions, banks, financial institutions and insurance companies.
• Machine Learning Algorithms: Machine learning algorithms are used in data analysis software to improve productivity and escalate revenues for the company. The software does this by highlighting such advertisements on shopping websites that are in accordance with end users' purchasing behaviour and history.
• Historical Data Analysis: The software identifies fraudulent customers by using historical data analysis. The methodology is instrumental in banking processes wherein banks can decide whom to issue credit/debit cards and whom to deny the same.
• Surveillance and Monitoring: Data analysis software helps prevent mishaps due to malware or spyware. It uses many sensors for management and monitoring and is primarily deployed by security agencies.
• Data Analytics: Data analysis tools extract and process data from databases for various industries, such as retail, finance, and healthcare, to create reports and visuals for decision-making.
• Extensibility: HTML 5-based operational intelligence dashboards can be easily embedded in other applications. In other words, the software can fully integrate itself with the existing data warehouse and business intelligence tools.