Top 12 Data Analysis Software: How to Choose the One That Will Drive Your Growth
To unleash the power of raw data, marketing experts use data analysis software that ensures the granularity of acquired insights and streamlines all routine data processes. Even though the market is overcrowded with different solutions, not all of them are equal in terms of quality and value. Besides, data analysis software is divided into different categories that are tailored to entirely different analysis goals.
Whether you’re a junior analyst trying to understand the topic or a marketing team lead looking for a powerful solution for your department, it may be hard to find the right software that will meet your requirements. In this post, we’ll break down the concept of data analysis software, take an in-depth look into each category with the best examples for each use case, and figure out how this software can help businesses.
What is Data Analysis Software?
Data analysis software is an application used by data analysts to line up, perform, and manage data analytics processes. It enables employees and stakeholders to make informed decisions faster.
In the marketing field, data analysis software creates new opportunities for tracking various metrics, such as ROMI, ROAS, CPC, lead attribution, and more. Put simply, it allows organizations to minimize the price of attracting prospective customers and increase the efficiency of marketing campaigns.
Different types of data analysis software are intended for different purposes. While some solutions store data, others perform complex calculations based on the user’s queries. Data visualization, statistical analysis, predictive modeling, and other analysis variations require different data analysis software to be used.
When choosing a data analysis solution, you have to take into account several factors. The major step here is to assess your company’s marketing analytics maturity. You should decide on the data analysis software depending on the number of marketing channels you use and your overall outreach volume. If you don’t promote your brand across different regions and markets, you may not need massive, expensive tools. And vice versa, if you’re accumulating gigantic datasets from dozens of marketing channels, you’ll probably need narrowly focused analysis tools to extract particular insights from your data.
Furthermore, you have to assess the skills of your team and ask yourself a question: “Will this software be used by experienced data analysts, or by junior specialists who require a clear and concise UI, or maybe the software should fulfill the needs of both groups?” You have to pick a software solution depending on the end-user. To help you understand the types of data analysis software and get to know leading providers better, we’ve compiled a list of the best software for data analysis.
What is the Best Software for Data Analysis?
To turn your raw data into actionable insights, you have to know what type of data analysis suits your needs and what software is the best in your particular situation. We’ll highlight the most common analysis methods and prominent tools that help you get a holistic view of your data.
#1. ETL Platforms
We’ll start with one of the most important types of solutions for accelerating data analysis. ETL or extract-transform-load systems help companies automate their routine data processes and concentrate on growth. Such platforms set up a data pipeline that extracts raw data from all required sources, unifies data according to the user’s requirements, and loads it to any destination. With all these processes streamlined, analysts can achieve analysis-ready insights in no time. What’s more, unlike average analysts who can make mistakes from time to time, a well-thought-out algorithm always produces predictable results and eliminates any data discrepancies.
Large enterprises that deal with data across different regions, markets, platforms, and destinations tend to implement ETL systems because of the complicated data consolidation process. It’s much easier to set up a unified ecosystem once than continuously aggregate disparate data manually.
Improvado is a full-cycle marketing ETL platform used by marketing specialists across different industries for streamlining their routine data processes. The platform automatically extracts data from 200+ marketing channels. With its pre-built data extraction templates, even inexperienced analysts can start gathering data right after the integration.
Furthermore, Improvado’s MCDM (Marketing Common Data Model) algorithm harmonizes raw data and unifies it based on gender, age, device, location, and a vast range of other custom metrics. The platform groups data, removes duplicates and pointless information, and generates crystal-clear marketing insights that are ready for further analysis.
Improvado’s data dictionary
After the normalization, this marketing analysis software loads data to a data warehouse chosen by the user. It’s much easier to optimize a marketing strategy when all data is just a few clicks away.
Finally, Improvado pushes all data from the warehouse right into visualization tools. With pre-built marketing dashboards, analysts get a comprehensive picture of their marketing efforts and an in-depth perspective of all marketing metrics. Automated marketing reports available to all employees across the company save marketers’ time and improve collaboration between departments.
Improvado and data visualization tools
#2. Statistical Analysis Software
Statistics are one of the top priorities for organizations. With its help, analysts can predict future industry trends, conduct competitor research, and adjust the company’s development vector. Statistical analysis software helps data scientists manipulate and extract new insights from large data clusters with various programming languages.
With the rising popularity of different languages, statistical analysis has developed its own rules, data plots, and scenarios. R, a programming language for statistical computing, is the most popular programming language for data analysis.
R is a free programming language and software environment for statistical computing and data plots. The language can be used for data cleansing, analyzing, and graphing both raw and structured data. It’s commonly used by analysts and researchers from different fields to assess the performance of some processes and display its results. RStudio is by far the most popular IDE (Integrated Development Environment) for data analysis. R’s data analysis capabilities make this language the best solution for both commercial, general, and academic statistical analysis.
The language started out as an academic tool exclusively. However, industry giants like Google, Facebook, Amazon, Airbnb, and others quickly turned their attention to this statistical analysis software.Today, it’s widely spread across various companies that deal with big data.
Databases are used to store and manipulate data gathered from different sources. Without properly structured data relationships, it’s hard to store new insights and analyze information. To operate databases, data analysts should be familiar with SQL queries used for all kinds of manipulations with data. If they don’t have coding experience, companies allocate developers to help analysts operate with data.
There are many different database management systems, such as:
- MS SQL
Each of them has its own advantages and drawbacks, syntax, and unique features. Next, we’ll consider MySQL, the most popular DBMS system today.
MySQL is a relational database management system based on SQL language (structured query language). This DBMS is used for different analysis objectives, like data aggregation, normalization, warehousing, and more. This database solution also offers on-demand scalability with its broad customization capabilities. Large companies that store gigantic amounts of sensitive data choose MySQL due to its efficient data management and outstanding flexibility.
What’s more, analysts can rest assured that their information is in safe hands. MySQL ensures sensitive data security and provides extensive backup possibilities. Modern data encryption algorithms prevent unauthorized access to information while SSL protocols ensure a safe connection.
#4. Big Data Analytics Software
Data scientists use big data analytics software to conduct thorough research of large datasets. With this type of analytics software, organizations can predict market tendencies and analyze the behavior of their target audience. This opportunity helps companies reduce operating expenditures, provide more customer care, increase personalized services, and convert their efforts into more revenue and growth. We’ll consider Apache Spark as an example of big data analytics software.
Apache Spark is an open-source, distributed computing system used by analysts to process big data. The platform implements in-memory caching in tandem with optimized query execution, so that the operations against large data clusters run fast and smoothly.
In plain language, Apache Spark is an optimized engine for large-scale data operations.
The secret of Apache Spark’s performance is that it runs on memory (RAM) that makes processing much faster compared to outdated approaches with disk drive processing. Additionally, this data analytics software can be used to run a DBMS, create data pipelines, load data into warehouses, train neural networks, and has many other use cases.
#5. Data Science Software
Data science is booming today, and that’s why data science software deserves a separate place on our list. Behind the generic term “data science software” stand platforms that facilitate data preparation, analysis, and reporting. With this kind of platform, you’ll be able to generate actionable insights in less time. Let’s consider RapidMiner as a notable example of data science software.
RapidMiner is an AI-powered data science software. It unifies the entire data science lifecycle, starting with data preparation and moving all the way through to predictive modeling. The platform is based on three automated data science solutions that help RapidMiner deploy analytics processes. With broad exploration functionalities, like data visualization and descriptive statistics, the software enables analysts to obtain all of the insights they need. In addition, the predictive analytics module helps to mitigate decision-making risks and segment customers.
RapidMiner also integrates with other data analysis software and programming languages, like Python and R. Its vast library of 1,500 data functions may be extended with third-party packages. For novice data scientists, the platform offers comprehensive guides and video tutorials that simplify the onboarding process.
#6. Data Visualization Software
Data visualization software stands out among all data analysis platforms. Visualized insights provide more understanding of the data you’re analyzing. Charts, bars, and other types of visualized data representations form a clear picture of the analyzed subject, not only for intelligence specialists but also for upper management and other employees in the organization.
Some data analysis software solutions, like business intelligence tools or ETL systems, provide their own visualization features. However, standalone visualization platforms offer more features, chart presets, and customization options that will suit even the most proficient data analysts. Let’s take a look at Google Data Studio, one of the most common visualization tools for today.
Google Data Studio
Google Data Studio, as well as popular solutions like Tableau and Power BI, allows companies to unlock the power of their data with fully customizable dashboards and insightful reports that accelerate the decision-making process. Intelligence specialists feed analysis-ready data to Google Data Studio from the following sources:
- Various DBMS
- Marketing connectors
- Analytics solutions
- Other sources
The platform processes these data and converts them into metrics and dimensions displayed on visualization dashboards and reports.
As for the collaboration, Google Data Studio is built with the same technology as Google Drive, meaning that you can share your visualized insights with your colleagues right away. Moreover, you can work on the same report with your teammates simultaneously.
Improvado’s integration with Google Data Studio
#7. Spreadsheet Software
Spreadsheet applications are a conventional type of data analysis software. The spreadsheets research approach is popular across all industries and organizations. Spreadsheets are used for minor analysis tasks, building entry-level charts, and even storing data. Even though this method brings some value for business analysts, companies that take analysis seriously opt for more proficient solutions. To take a detailed look at spreadsheet software, we’ve chosen Airtable as an example since it’s one of the most popular solutions on the market.
Airtable is a combination of spreadsheet software and a database management system. This solution stores information in a clear and straightforward spreadsheet format. At the same time, it is capable of serving as a database that companies can use as a CRM, task management system, project planning software, and inventory management solution. It even allows users to create data relationships between tables.
#8. Data Modeling Software
Data modeling software is used to create models that depict database structure and design architecture with diagrams, shapes, text, and symbols. These diagrams represent data relationships and depict how data is ingested in the storage and where it’s transferred to. Business analysts use data modeling tools to facilitate the storage and tracking of data in the future. There are dozens of diagram drawing solutions, but we’ll consider MySQL Workbench as an example.
MySQL Workbench is a visualized tool for database architects, engineers, developers, and analysts. The software itself provides a more convenient way to work with data from the database. Instead of console commands, users can navigate through the visual interface and interact with tables and fields via UI elements instead of SQL queries. Here, all database adjustments and configurations can be made.
However, MySQL Workbench is also a great tool for designing the architecture of your future database and relationships between stored information. With a straightforward UI, database architects can set one-to-one and one-to-many relationships in just a few clicks and modify pre-built entity-relationship diagrams right away.
#9. Predictive Analytics Software
Predictive analytics is a technology that is shaping the future for businesses that deal with marketing and take strategic decisions seriously. This data analysis software combines data mining, predictive modeling, machine learning, and AI to forecast future events. Some of the tools that we listed above already had primary predictive capabilities. However, their capacities are limited. True predictive analytics features can only be found in standalone solutions. That’s why we’ll take a look at SAP Analytics Cloud, which is a popular software solution among enterprise-level organizations.
SAP Analytics Cloud
SAP Analytics Cloud empowers business analysts to safely work with accumulated data and create outstanding data analysis dashboards. The platform offers a fresh look at your insights and efforts across the enterprise. Based on the acquired data, you can make decisions with confidence and drive better business outcomes.
The extended planning and analysis module in SAP Analytics Cloud grants a full alignment across all business areas. Visual stories combined with augmented analytics and planning functionalities ensure precise forecasts and detailed reports. As an additional bonus, the mobile version of SAP Analytics Cloud allows you to stay on top of insights and predictions while you’re on the go.
#10. Programming Languages
Common programming languages aren’t meant to solve data analysis tasks exclusively. However, with additional data analysis libraries, every language acquires new data research capabilities. Almost every modern language has a custom library for analysis purposes. Python, C#, Java, PHP, Ruby, Scala, and many others are used by programmers and analysts to examine data. We’ll explore Python here, as the language has a large number of libraries and software development kits for data scientists and analysts.
Python is a favorite programming language of numerous engineers due to its straightforward syntax and open-source concept. Since anyone can modify Python’s source code, the language gave rise to dozens of data analysis frameworks, libraries, and SDKs. For example, NumPy, a fundamental package for data analysis, adds support for multi-dimensional arrays and brings new mathematical functions to manipulate these arrays.
Python is also a highly portable data analysis solution. Analysts can run the same code on several machines without modifying it. A large number of additional modules and its simplicity made Python a popular choice among large companies like Spotify, Reddit, Netflix, and so on.
#11. Business Intelligence Software
BI software is the most widely used form of data analysis software. Business intelligence solutions are a jack-of-all-trades on the analysis market. Even though BI platforms don’t offer narrowly focused capabilities like predictive analytics tools or big data analytics software, their capacities are enough to deal with the majority of analysis tasks. Let’s take a closer look at Oracle BI, an enterprise-level business intelligence software.
Oracle’s product ensures that all companies have access to their business insights and can make informed decisions. The platform combines machine learning and artificial intelligence so companies can scale across all business verticals and get the most out of their data. Oracle BI automatically updates data within third-party providers, maps all your data to help you monitor analytics, provides custom metrics, and much more. However, the solution isn’t the best option for data visualization. Still, it copes well with all tasks entrusted to it.
#12. Unified Data Analytics Software
If you’re dealing with large clusters of data and searching for software to manage big data, unified data analytics is the most advanced solution to your problem. Analysts need a reliable tool that will put them in charge of their data environment. Here, we’ll go through the benefits of Google BigQuery, a well-established unified analytics solution.
Google BigQuery is a serverless data warehouse that offers the ability to analyze large datasets in no time. This data analytics software works out of the box without installations, configurations, and so on. A built-in cache also positively influences BigQuery's performance. If analyzed datasets aren’t changed completely, the software performs better. However, BigQuery isn’t a good choice for small datasets. As such, you shouldn’t use it as a regular cloud-based database. BigQuery is only suitable for big data processing and analysis.
Why is ETL So Important in Data Analysis?
Among all data analytics software, ETL systems should be distinguished due to the vital role they play in analytics. Data extraction, cleansing, harmonization, and aggregation take up too much of business analysts’ time. By automating routine processes, companies mitigate the risk of human error during data organization and lighten the load for their analysts.
Additionally, the data pipeline automatically pushes all insights to visualization and business intelligence tools. With a properly adjusted loading algorithm, analysts can get rid of data discrepancies and incorrect representations on dashboards.
It’s obvious that the data analysis software itself is essential. However, without established data extraction patterns and a smooth data pipeline, your final results might be inaccurate and misleading. That’s why every data analysis software should be accompanied by a fault-proof ETL system.
Accelerate Your Data Analysis With Flawless ETL Data
Business development vectors and success are closely linked to the quality of raw data and insights that analysts are able to retrieve. To shield yourself from misleading information and get the most value out of your analysis efforts, you should entrust your data processes to the ETL system.
Improvado is a full-cycle ETL marketing solution that helps businesses automate routine processes and make informed decisions faster. With our platform, you can choose from 200+ marketing data sources and integrate any of them into your data infrastructure. You don’t need to handle complex SQL queries or maintain in-house ETL developers. We’ll connect all data sources and arrange a data pipeline on our own. That way, you can focus on data analysis and business growth instead of routine manual processes.