Businesses are always looking for ways to improve their internal operations, gain competitive advantages, and better services for their customers. Data analytics helps businesses with all of these tasks by collecting, analyzing, and interpreting data for use in meaningful business applications. There are many programs out there that are data analytics software tools that are very capable of handling everything you need.
If you want to become a data analytics expert, then this trusted guide is just what you need. This e-book explains all the tools and technologies you need to be an expert as well as how to get the most out of them. It helps you find the right tool for the job, whether it’s an open source or enterprise solution, and provides advice on how to handle data projects on your own.
FineReport
FineReport, with its self-service data analysis, has become one of the best Data Analytics tools, suitable for small to medium size organizations. It has a simple drag and drop operation and easily builds a data decision analysis system that enables the users to create various styles of reports. It connects directly to all kinds of databases, enabling it to quickly customize and create diverse styles of weekly, monthly, and annual reports.
MicroStrategy
MicroStrategy has Hyper Intelligence linking technology that uses Application Programming Interface (API) connectors to connect all the competing platforms using MicroStrategy as the unifying layer. This innovative twist makes MicroStrategy one of the most popular modern data analytic tools. Its ability to join all business content from browser-based systems and competing-analytics software offers a usable and insightful analytics dashboard. When a user clicks on a link, the dashboard offers real-time insights.
R & Python
Programming languages like R & Python are a few of the significant Data Analytics tools. They are flexible and powerful and make for some indispensable open-source Data Analytics tools. R language enhances your statistical analysis and data distribution by using algorithms to classify clusters and regression analysis. Python helps you with predictive analysis along with statistical analysis.
Sisense
Sisense is a sophisticated analytics software, suitable for organizations that work with complex and ongoing data processing of an enormous amount of data. This Data Analytics software leverages the advantages of a cloud-native infrastructure which enables it to scale up in tandem with container technology and the growth of your data needs. Its proprietary caching engine enables faster data crunching, empowering it to manage a wide range of difficult enterprise analytics workloads.
BOARD
Platform: BOARD
Related products: BOARD Cloud
Description: BOARD combines business intelligence, performance management, and predictive analytics into one platform. As a result, any change to data, data models, security profiles or business rules is immediately propagated to every application. The solution provides all the tools required to create and update databases, data presentations, analyses, and process models. The company also offers BOARD Cloud, a SaaS version of the platform, backed by Microsoft Azure.
Chartio
Platform: Chartio
Description: Chartio is a cloud-based data discovery platform that lets you create charts and interactive dashboards. The product features a proprietary, visual version of SQL that enables any user to explore, transform and visualize data via a flexible drag-and-drop interface. There is no need to build data models in advance. Chartio includes a set of pre-built connections to data sources like Amazon Redshift, Google BigQuery and Snowflake, while also enabling direct access to CSVs and Google Sheets.
Domo
Platform: Domo
Related products: Domo Everywhere, Domo integration Cloud
Description: Domo is a cloud-based, mobile-first BI platform that helps companies drive more value from their data by helping organizations better integrate, interpret and use data to drive timely decision-making and action across the business. The Domo platform enhances existing data warehouse and BI tools, and allows users to build custom apps, automate data pipelines, and make data science accessible for anyone across the organization through automated insights that can be easily shared with internal or external stakeholders.
GENERAL-PURPOSE PROGRAMMING LANGUAGES
Programming languages are used to solve a variety of data problems. We have explained R and statistical programming, now we will focus on general ones that use letters, numbers, and symbols to create programs and require formal syntax used by programmers. Often, they’re also called text-based programs because you need to write software that will ultimately solve a problem. Examples include C#, Java, PHP, Ruby, Julia, and Python, among many others on the market. Here we will present Python as one of the best tools for data analysts that have coding knowledge as well.
PYTHON
KEY FEATURES:
An open-source solution that has simple coding processes and syntax so it’s fairly easy to learn
Integration with other languages such as C/C++, Java, PHP, C#, etc.
Advanced analysis processes through machine learning and text mining
Python is extremely accessible to code in comparison to other popular languages such as Java, and its syntax is relatively easy to learn making this tool popular among users that look for an open-source solution and simple coding processes. In data analysis, Python is used for data crawling, cleaning, modeling, and constructing analysis algorithms based on business scenarios. One of the best features is actually its user-friendliness: programmers don’t need to remember the architecture of the system nor handle the memory – Python is considered a high-level language that is not subject to the computer’s local processor.
Another noticeable feature of Python is its portability. Users can simply run the code on several operating systems without making any changes to it so it’s not necessary to write completely new code. This makes Python a highly portable language since programmers can run it both on Windows and macOS. An extensive number of modules, packages and libraries make Python a respected and usable language across industries with companies such as Spotify, Netflix, Dropbox and Reddit as most popular ones that use this language in their operations. With features such as text mining and machine learning, Python is becoming a respected authority for advanced analysis processes.
Conclusion
Many large businesses are collecting more information than they have the capacity to manage. It’s time that small business caught up. This data can help you compete with larger companies, save money, and increase revenue, but to do so requires an analytics toolkit.