Cloud applications such as SCM (Supply Chain Management), ERP (Enterprise Resource Planning), SLM (Service Life-Cycle Management), and CRM (Customer Relationship Management) are generating an enormous amount of data, and its growth rate is rapidly increasing.
Famed technology enterprises and emerging startups worldwide are producing analytical tools and strategies that provide effortless analytics solutions, assisting corporations in deriving practical knowledge from their data.
The cutting-edge analytical tools available today offer a plethora of robust capabilities that enable businesses to gather, analyze, and present data in real-time, ultimately enabling organizations to pinpoint patterns and trends in large datasets and develop innovative business analytics models.
What Are Business Analytics Tools?
Business analysts utilize various types of application software which gather quantitative and qualitative data from diverse business systems. Subsequently, this data is integrated into a repository for analysts to extensively review and analyze, facilitating more intelligent decision-making.
can help inform decision-making processes. The deployment of business analytics software varies among companies, but typically involves a combination of statistical tools, predictive modeling, and data mining software. These applications enable organizations to gain a comprehensive understanding of significant insights critical to informed decision-making.
Difference Between Business Analytics and Business Intelligence Solutions
Business analytics software goes beyond compiling and analyzing data, by providing a thorough examination that highlights deficiencies, notifying decision-makers of unexpected occurrences, remedying potential issues, and even predicting future performance.
Business analysts utilize top-quality tools for requirement gathering, which furnish organizations with the aptitude and assurance to enhance customer contentment, elevate their competitive advantage, and achieve business objectives.
Experts suggest that due to the abundant features and benefits that advanced business analytics software offer in terms of enabling business success and future growth, there is an unprecedented surge in popularity and demand for premium-quality analytics tools. The increasing significance of data analysis is predicted to result in better business decisions and the opening up of fresh job opportunities across various industries. Here, we present the ten most frequently employed business analytics solutions and tools that are tailored for contemporary enterprises.
Top 18 Business Analytics Tools Used by Companies Today
1. SAS Business Analytics (SAS BA)
With the SAS business analytics software, users have access to advanced text analytics features that enable them to convert unstructured textual data into useful information for analysis by analysts to uncover valuable findings.
The SAS BA comes equipped with a variety of statistical instruments that facilitate the analysis of vast data sets in real-time. Furthermore, this state-of-the-art business analytics solution forecasts future trends using insights gained from big data analysis.
Due to its exceptional attributes, including patented technology and in-memory processing that enable the generation of rapid business analytics reports, QlikView is among the top choices for enterprise analytics tools.
QlikView is a highly rated tool for business analysts that enables visualization of data relationships. It allocates particular colors for related and unrelated data and maintains data association automatically while compressing it to only 10 percent of its original size.
The board has secured a spot on the list of the most highly rated tools for business analysis due to its superior business analytics model which enables users to generate user-friendly and dynamic business analytics reports and dashboards.
A business analytics platform, which is designed for high scalability, combines the most effective analytical tools in the industry and includes a robust statistics suite.
By manipulating the data fed to scenarios, the Board analytics tool can facilitate precise forecasting and scenario analysis, while also being capable of handling colossal volumes of data.
Small and medium scale industries commonly utilize Splunk as a business analytics tool.
Splunk was first introduced to handle data from machine log files, but has since expanded to incorporate numerous advanced capabilities, such as an intuitive web interface and a range of display alternatives.
With its dynamic and robust text analysis capabilities, Sisense has become one of the most sought after software tools for business analysts, as it allows them to effortlessly turn unstructured data into useful business insights.
The Sisense platform for business analytics utilizes Sisense Crowd Accelerated BI, their proprietary tool, and open-source computational statistics language. This enables users to easily conduct in-depth analysis and create visual representations of complicated data. By doing so, it encourages informed decision-making based on data and enhances the ability to predict upcoming trends.
BONUS: How to Learn Big Data?
Big data refers to a vast quantity of information that is produced on a daily basis by individuals, encompassing a wide range of content, such as a single post on Facebook.
The rate at which data is growing is rapid, as evidenced by a report stating that the global production of data is expected to reach 463 exabytes daily by 2025, which is equivalent to 212,765,957 DVDs.
Big data analytics are used to handle and analyze enormous amounts of data, which is typically unstructured and may include image, text, audio, and other forms of data.
Big data analytics refers to the methodology of identifying valuable patterns in substantial volumes of disorganized data. This entails numerous stages, beginning with data cleansing and culminating in pattern identification. The concept involves storing and computing extensive data quantities. Big data is characterized by the three V’s: volume, variety, and velocity.
- Volume- It refers to the size of data, which means how much data is generated.
- Variety- It refers to the type of data, which means which type of data is generated like structured data or unstructured data.
- Velocity- It refers to the speed of data, which means at what speed data is generated.
Next, we will proceed with the detailed guide on how to approach Big Data.
How to Learn Big Data Step by Step?
Step 1- Learn Unix/Linux Operating System and Shell Scripting
It is important to have proficient knowledge in shell scripting as numerous tools operate through a command line interface that heavily relies on Unix commands and shell scripting.
Data pipelines can be created using Shell Scripting which involves creating a text file with a series of UNIX-based operating system commands.
These resources will enable you to acquire knowledge and skills in Unix/Linux Operating System and Shell Scripting.
- Linux Command Line Basics (FREE Course)
- Shell Workshop (FREE Course)
- Configuring Linux Web Servers (FREE Course)
- Linux Fundamentals (Coursera)
- Introduction to Bash Shell Scripting (Coursera Project)
Step 2- Learn Programming Language (Python/Java)
Popular Big data tools have significant core modules coded in Java, which explains why Java remains the foundation for numerous big data frameworks.
While Python is capable of processing Big Data, Java is more straightforward and does not require the assistance of third-party tools.
The choice of learning Java or Python is yours.
Hadoop is a framework that facilitates the creation of big data applications in Java, whereas Python boasts of numerous open-source libraries and tools. If you are a novice, Python might be the better option since it’s comparatively easier to understand and implement. On the other hand, if you have experience, you could choose Java.
Next, we will explore the available materials for acquiring knowledge in Java and Python.
- The Python Tutorial (PYTHON.ORG)
- Python for Absolute Beginners! (Udemy)
- Python for Everybody (Coursera)
- Python 3 Tutorial (SOLOLEARN)
- CS DOJO (YouTube)
- Programming with Mosh (YouTube)
- Corey Schafer (YouTube)
- Python Crash Course (Book)
- Java Programming Basics (Free Course)
- Become a Java Programmer (Udacity)
- Become a Java Web Developer (Udacity)
- Core Java Specialization (Coursera)
- Introduction to Java (Coursera)
- Java Programming Masterclass covering Java 11 & Java 17 (Udemy)
Step 3- Learn SQL
Having a strong understanding of SQL is crucial as it is the most sought-after skill for managing Big Data. Additionally, familiarity with NoSQL is necessary since unstructured data may need to be handled at times.
Experimenting with SQL within relational databases allows us to grasp the process of querying extensive sets of data.
- Learn SQL Basics for Data Science Specialization– Coursera– This specialization program is dedicated to those who have no previous coding experience and want to develop SQL query fluency. In this program, you will learn SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more.
- Excel to MySQL: Analytic Techniques for Business Specialization– This Specialization program is offered by Duke University. This is one of the best SQL online course certificate programs. In this program, you’ll learn to frame business challenges as data questions. You will work with tools like Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights.
- W3Schools– You can learn DBMS and its concepts from the Free Tutorial of W3Schools.
- NoSQL systems– In this course, you will learn how to identify what type of NoSQL database to implement based on business requirements. You will also apply NoSQL data modeling from application-specific queries.
Step 4- Learn Big Data Tools
After obtaining proficiency in Python, Java, and SQL, the subsequent stage involves gaining expertise in Big Data applications, such as Hadoop and MapReduce, as well as Apache Spark, Apache Hive, Kafka, Apache Pig, and Sqoop.
- Intro to Hadoop and MapReduce(Udacity)- This is a completely Free Course to understand the concepts of HDFS and MapReduce. In this course, you will learn what is big data, the problems big data creates, and how Apache Hadoop addresses these problems.
- Spark (Udacity)- This is another completely Free Course to learn how to use Spark to work with big data and build machine learning models at scale, including how to wrangle and model massive datasets with PySpark. PySpark is a Python library for interacting with Spark.
- Hadoop Developer In Real World (Udemy)- This course will cover all the important topics like HDFS, MapReduce, YARN, Apache Pig, Hive, Apache Sqoop, Apache Flume, Kafka, etc. The best part about this course is that this course not only gives basic knowledge of concepts but also explores concepts in deep.
- Big Data Specialization (Coursera)– In this specialization program, you will get a good understanding of what insights big data can provide via hands-on experience with the tools and systems used by big data scientists and engineers.
Step 5- Start Practicing with Real-World Projects
Congratulations on acquiring your Big Data skills! Now, it’s crucial to start focusing on Real-World projects as they are vital to securing a job as a Big Data Engineer.
The greater number of projects you undertake, the deeper understanding of Data you will acquire. Moreover, projects will add more advantage to your Resume.
To begin your learning journey, you may utilize APIs on social media platforms such as Twitter to access real-time streaming data.
This is it! By following these steps and acquiring the necessary skills, you will be unstoppable in your pursuit of a career in Big Data.