Data Analytics is the process of examining and analyzing data sets to draw conclusions and identify trends & metrics. Armed with insights from data, organizations, and enterprises make informed choices rather than mere guesswork.
Through Data analytics, businesses and organizations are able to get a clear understanding of their target customers, online audience, business performance, etc. This makes them better equipped to make decisions and in better planning.
Data analytics is not only used in commercial settings but also by scientists and researchers. The ultimate goal of data analytics in commercial settings is to boost overall organizational productivity.
The techniques of Data Analytics have been automated with the aid of specialized systems and software, which has greatly benefited businesses in optimizing their performances.
Data Analytics provides key answers to the following questions while making business decisions:
- What happened (Descriptive Analytics)
- Why did it happen? (Diagnostic Analytics)
- What will happen? (Predictive Analytics)
- What should be done? (Prescriptive Analytics)
Top 10 Data Analytics Tools in 2023
In this technology-driven era, data analytics have evolved rapidly. Various tools have emerged to assist Data Analysts to solve discrete questions about Data. Data analysts should be skillful in carrying out Data mining, Statistical analysis, and Database Management Reporting.
Data Analysts are always on the quest to find out the best tool for Data analytics. The opinions of Data analyst experts often stand divided when it comes to the Power BI vs Excel debate. Given below is the compiled list of the top 10 Data Tools in 2023 that you should be aware of
- Power BI
- Microsoft Excel
- Apache Spark
- Konstanz Information Miner (KNIME)
- Rapid Miner
- Apache Hadoop
Here's a brief explanation of the features of these tools:
1. Power BI:
Business Intelligence (BI) is collecting raw data and converting it into meaningful information that provides actionable insights to make tactical business decisions.
Power BI is a collection of tools that combine to turn unrelated data sources into coherent, visually immersive, and provide deeper interactive insights to the users.
Microsoft develops power BI and stands out with streamlined publication and distribution capabilities. There is also a provision to integrate with other Microsoft Products and Services and Power BI users.
One of the benefits of Power BI offers a wide range of custom visualizations where developers can take your requirements and convert them to charts, graphs, maps, etc. Have a look into the key components of Power BI.
- Power Query: This is an important component of Power BI, which can also be included in Excel Sheets. Power Query extracts data from multiple sources like SQL Server, MySQL, Oracle, etc. It can also be used to delete data from numerous data sources.
- Power View: Power View is an interactive component that connects to data sources and helps retrieve metadata that can be utilized for Data Analysis. You can filter data for each visualization component or the entire report with the help of Power View.
- Power Pivot: Power Pivot is used for modeling both simple and complex data. It is a calculation engine that helps you set relationships between different tables and to calculate values.
- Power BI Desktop: It is a new integrated development tool for Power Query, Power View, and Power Pivot. It comes with the latest advanced features.
- Power BI Website: It is a platform where you can create reports and share them with other users.
- Power BI Mobile Apps: Power BI Mobile Apps provide you with an interactive view of reports and dashboards on the Power BI Site.
- Power Map: This particular component is used to visualize geospatial data in 3D Mode. It works with Bing Maps based on geographical latitude or longitude and gives you the best visualization.
- Power Q & A: It is a component where users can ask questions and get solutions to those questions.
2. Microsoft Excel
Microsoft Excel is one of the widely used tools for data analysis. Excel is an effective tool for calculating, organizing, and evaluating quantitative data. Excel is equally beneficial to managers and staff in making important decisions. Following are the Functions in Excel used for Data Analysis
- CONCATENATE: This function is used for creating API endpoints, Product SKUs, and Java queries
- LEN: This function is useful for determining the differences between different Unique Identifiers (UID)
- COUNTA: This function allows you to evaluate any gap that the dataset might have without reorganizing the data.
- DAYS/NETWORKDAYS: =DAYS function determines the number of days between two calendar dates. =NETWORKDAYS is a robust function that determines workdays between two dates.
- SUMIFS: This is an essential function used in determining how each product is contributing to top-line revenue.
- AVERAGEIFS: This function allows you to take an average of more than one criterion.
- VLOOKUP: It is one of the useful and recognizable data analysis functions in Excel.
- FIND/SEARCH: This is a powerful function used for isolating specific text within a data set.
- IFERROR: This effective tool can be utilized by data analysts at any point in time
- COUNTIFS: This function is used to count the number of instances a dataset meets a set of criteria.
- LEFT/RIGHT: This is a straightforward method used for extracting static data out of cells
- RANK: This is used to denote how values rank in a dataset in ascending or descending order.
- MINIFS: This is used to take a minimum of a set of values
- MAXIFS: This is used to take a maximum of a set of values
- SUMPRODUCT: It is an excellent function used to calculate average returns, price points, margins, etc.
3. Apache Spark
Apache Spark is an open-source data processing framework that quickly performs processing tasks on very large data sets. It also distributes data processing tasks across multiple computers. At present, it has become one of the important big data distribution frameworks in the world. The following are the features of this tool:
- Spark Core: This feature allows everybody from app developers to data scientists to harness its scalability and speed in an accessible manner.
- Spark RDD: RDD represents Resilient Distributed Datasets. It is a programming abstraction that represents an immutable collection of objects that can be split across a computing cluster.
- Spark SQL: Spark SQL emphasizes processing structured data using a data framework approach borrowed from R and Python.
- Spark MLlib: It includes a framework for creating machine learning pipelines and also for the transformation of any data set.
- Spark GraphX: It consists of a selection of distributed algorithms for processing graph structures.
- Spark Streaming: Spark Streaming extended the concept of batch processing into streaming by breaking it into micro-batches.
- Structured Streaming: This feature allows developers to create infinite streaming data frames and datasets.
- Deep Learning Pipelines: Apache Spark also supports deep learning via Deep Learning Pipelines.
4. Konstanz Information Miner (KNIME)
KNIME is an open-source data analytics designed for teaching, research, and collaboration platform. It is a modular environment that enables simple integration of new algorithms and tools as well as data manipulation. There is no need for any prior programming skills to use KNIME. It is an integration tool designed for analytics using a GUI-based workflow.
5. R Programming
R Programming is an open-source language used for statistical computing or graphics. This tool is not only used to analyze organizational data but also for the creation and development of software applications that perform statistical analysis.
R democratizes analytics by enabling business users with interactive data visualization and reporting tools. It also helps reduce the time spent on data preparation and provides accurate, in-depth analyses. Some of the functions that can be carried with this tool are,
- Time series analysis
- Data Mining
- Text Mining
- Web Analytics
- Social Media Analytics
- Sentiment Analysis
6. Rapid Miner
There are various sources of data, such as databases, data warehouses, and other repositories. Interesting patterns and knowledge can be obtained from these data through the process of Data mining. Rapid Miner is a data science tool popularly used for data mining. The activities that can be performed using this tool are given below,
- Estimation & Forecasting
- Clustering & Association
- Data Cleansing
- Validation & Scoring
7. Apache Hadoop
At present various organizations has started using Hadoop for both research and production purposes. This software offers a flexible data management solution for big data analytics. The ecosystem of Hadoop comprises the following.
- Hadoop Common: It's the common utilities that support other Hadoop Modules
- Hadoop Distributed File System (HDFS): This provides high throughput access to application data and high fault tolerance
- Hadoop YARN: YARN stands for Yet Another Resource Negotiator, is a framework for cluster resource management and scheduling jobs
- Hadoop MapReduce: It is a YARN-based system for processing large data sets.
- Hadoop Ozone: It is a scalable, redundant, and distributed object store designed for big applications.
MongoDB is a leading NoSQL Database. MongoDB is written in C++ and is usually a popular database choice for application development. MongoDB uses JSON objects to store data, and this helps in the efficient handling of big data by partitioning the database into shards.
9. QlikView/ QlikSense
QlikView is a Business Intelligence (BI) tool that provides business solutions by allowing the user to easily create highly interactive dashboards without any dependence on stack technology. This tool has benefited major industries globally. QlikSense was introduced in 2014 as a cloud narrative platform with innovative AI capabilities that supports the full range of analytics.
Splunk is a digital platform that manages data, which is in an unstructured format. Splunk has become a front-runner in the tech sector because of its flexibility in machine learning and widespread applications in big data data analytics. Following are the functionalities of Splunk.
- Data Indexing
- Error detection
- Search mapping
- Alerts schedule
- Splunk reports
Data Analytics Tools help in delivering personalized experiences to customers in products and services. Businesses and organizations have started to realize the significance of Data Analytics Tools in mitigating risks and in resolving recurrent problems. By choosing the right tool, you can obtain the best results from data analytics.