The primary goal of data analysts is to analyze a given set of data and draw meaningful insights from it. There are so many Data Analysts tools available in the market which helps them in collecting data. Data analysts start with collecting the data from various sources, sort the entire set of data, arrange the data and then make it presentable for others to comprehend the data sets and draw inferences accordingly.
Data analysis is essential to find out crucial information with the help of data and make informed and rational decisions.
Essential factors to be considered while evaluating the data analysis tools.
Things to Consider Before Choosing Data Analysis Tools
Modern-day businesses are religiously following data analysis practices. While there are so many data analysis tools available, it’s not easy to choose the right data analysis tools to match your organization’s and business needs. To learn better about the tools that can be used to your advantage, you can opt for a data analyst course in Dubai. The course will help you gain essential knowledge of data analysis-related topics and help you choose the right tools that will work for the best results.
We are here to help you in picking the appropriate data analysis tools to best suit your business requirements and objectives.
Certain things must be taken into account while selecting the right data analysis tools, which include:
- Firstly, you need to understand well the dynamics of your business and overall business goals.
- In view of the business goals, you need to know about the kind of data your organization intends to analyze and additionally your data integration requirements.
- Then, you must be aware of the appropriate data sources and tables, and columns within the data pool to extract the relevant data.
- Lastly, you need to verify whether you have in place the right data management and security tools, and in case if the data needs to be shared among different team members, there should be proper access control and data protection tools.
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How do Data Analysts Choose Their Tools?
To proceed with it, you can first start with analyzing the key objectives of your business and knowing who will be handling the overall data processing. Upon extracting the relevant data, you can analyze it using a plethora of data analysis tools.
But the question is, how do you decide which tools will be appropriate for analyzing your data adequately. That is, whether technical people or non-technical individuals will use the tools. Different data analysis tools use different platforms; for e.g., a few tools offer interactive platforms for technical people allowing data analysis only using SQL, while other tools are based on point and click analysis for non-technical users.
In addition to this, data analysts look for the data modelling capacities of the tools and the ability of the tools to allow good visualization to comprehend the data trends.
Finally, data analysts look for the cost and licensing of the data analysis tools. A few tools aid in data analysis free of cost, while a few are the paid ones. Irrespective of the cost, data analysts look for the most comprehensive tools to take care of the entire data processing. This is because costlier tools do not ensure that they will have all the features to process the data adequately, and similarly, even the free tools need not be inefficient and can be better than the paid ones.
Best Data Analysts Tools
By now, you must be aware of factors and ways based on which data analysts choose the best data analysis tools. The most extensively use data analysis tools used by data analysts are discussed below:
R and Python
Both R and Python are open source programming languages commonly used by technical professionals like data scientists and analysts. R has a broad spectrum of graphical tools and 15000+ open-source packages supporting loading, manipulation, modelling, and data visualization.
Python is used extensively worldwide, even more than Java, and has 20,000+ packages. With the help of Python, one can easily filter and align a large set of data. Furthermore, it’s capable of handling a majority of the data analysis process independently and can be collaborated with 3rd party applications for ML and data visualization.
Of all the data analysis tools, MS Excel is the most widely used by technical and non-technical professionals to sort data and carry its further processing. It is used for primary data analysis; however, for advanced data analysis, one has to rely on other tools.
It’s a data visualization and analytics tool that enables professionals to develop reports and share them over desktop and mobile platforms, along with a browser, and can also be embedded in an application. It’s highly versatile as it can run both on the cloud or on-premises. Considering the applications, Tableau is regarded as a better data extraction tool than Excel because Tableau can analyze a big chunk of data with its inherent capability of spotting the visual patterns in no time.
This tool primarily supports integration, data cleaning, and data modification over a simple graphical interface before data analysts can run predictive analytics and statistical models. RapidMiner is optimized explicitly for its graphical interface to facilitate the data analysts to process data and run models themselves.
Also known as ‘Konstanz Information Miner’ KNIME is a free, open-source data analysis tool that enables integration of data, it’s processing, visualization, and reporting. It’s one of the best tools for data scientists who collaborate and process data for ML and different statistical models and lacks strong programming skills.
It’s one of the top data analysis tools used by professionals. It enables users to create and share reports, visualizing data, and generating dashboards. For the simple distribution of data, users can integrate various dashboards and reports into the Microsoft Power BI tool. Furthermore, Power BI allows users to develop automated ML models and can also integrate with Azure ML.
We believe this article has helped you get well-versed with the ways that are taken into consideration and the tools used by data analysts to process a big chunk of data to draw meaningful conclusions. All tools have various merits and demerits and must be analyzed first to align their usage with the business objectives.