6 Best Big Data Analytics Tools for Marketers

Introduction

Data is every marketer’s best friend. Today, data has become an important asset to every marketer. We require data to identify what’s working well in our campaigns and what’s not, to diagnose potential problems, and so much more.

However, many marketers fail to understand that some data simply holds more valuable than others. so, knowing which data can help you excel can be key. Extracting data at scale can be overwhelming; however, web scraping using Python, and other coding languages, can simplify that process.

Marketers must know metrics to monitor as they can mean the difference between success and failure.

And to make your task a little simple, there are hundreds) of marketing analysis tools available on the internet just a click away. Some of these tools are free while others are not. Some tools are specifically good at one thing whereas others offer a broad range of functionality.

Most importantly all of them hold one promise, to provide you the sweet data that can help with run campaigns more effectively as packaging printers pledge to make you marketing campaign proactive

What to look for before selecting big data analytics tools?

Analytic Capabilities: Different tools have different types of analytics capabilities. Different models for various types of analysis include Predictive mining, Decision trees, Time series, Neural networks, Path analysis, Market basket analysis, and Link analysis.

Integration: Statistical tools and programming languages are also needed by businesses for other forms of custom analysis.

Data Import and Export: This is a key consideration as inputting data in and out tools is a vital feature. It helps understand the level of difficulty with which you can connect the analytics tool to the big data repository.

Visualization: Data that is displayed in a graphical format often becomes more use-able and appealing to the users.

Scalability: Big Data tends to grow bigger over time. Hence, businesses need to consider scalability as an option for the analytics tools they choose.

In today’s blog post, we will provide you with the best big data analytics tools and help you see what they do and what they hold for you.

#1. Skytree

Source: Butleranalytics

Skytree is one of the best big data analytical tools out there. It has highly scalable algorithms to even help data scientists build more accurate models faster.

It offers accurate predictive machine learning models that are easy to use as it is designed to solve robust problems with data preparation capabilities. It can provide users with important analytics on customer segmentation, churn prediction, fraud detection, and others.

It is an excellent data analytics solution for small and mid-sized companies. The platform is designed to equip smaller teams with all-encompassing ML mechanisms. It makes data science a simple process; hence even first-time users easily comprehend it.

Its features include:

• Highly Scalable Algorithms
• Easy to adopt GUI or programmatically in Java
• Model Interpretability
• Data accumulation and filtering
• Simple data visualization
• Customizable sets of algorithms
• AI-based predictive analytics
• Extensive data interpretation models

Besides, it analyzes data streams for everything be it relational databases to machine learning libraries. And last but equally important is to mention the customer service team behind the software. You can reach out to their agents around o’clock and they will be ready to help you with your inquiries.

#2. Mixpanel

Mixpanel is a powerful analytical tool that can offer any business invaluable insights on its audience’s behavior.

The designers of Mixpanel along with the 33 percent of marketers agree that having the right technologies for data collection and analysis can immensely help in understanding customers.

Businesses need to constantly track user behavior on their websites. This tool can help track comprehensive user behavior to help them see what potential customers are doing on their websites.

It organizes the data and allows you to see patterns in web usage which then help you make informed future marketing strategies.

Its features include:

• Advanced analysis
• Send targeted messages
• Analytics infrastructure for global scale
• Data governance
• A/B testing
• Annotations
• Data visualization
• Retention features
• Auto track customer engagement

It provides a wide range of functionality without even requiring a single line of code. This means that even non-technical personnel can easily access data about your site.

#3. Apache Spark

Source: Prweb

Apache Spark is another very powerful and massive open-source big data analytics tool. It offers more than 80 high-level operators to easily build parallel apps. This tool can help you figure out datasets and design the analytics reports.

Based on cutting-edge technologies such as a query optimizer and DAG scheduler it is used by large businesses to process large datasets. It helps in running applications in the Hadoop cluster, up to 100 times faster in memory, and ten times faster on disk.

The platform can work with all the major programming languages including the likes of Java, SQL, R, Python, and Scala.

Its features include:

• Run application in Hadoop cluster
• 100 times faster in-memory
• Ten times faster on disk
• Lighting fast processing
• Sophisticated analytics
• Built-in APIs in Java, Scala, or Python
• In-memory data processing capabilities
• Works with HDFS, OpenStack and Apache Cassandra
• Enable superior data processing
• Seamless integration with external apps and software solutions

Another crucial advantage of the tool is that it supports all sorts of devices, apps, and operating systems.

Apache Spark contributors list has over 1.2 thousand names. So, you can either contact their expert community via email or use Jira for tracking issues.

#4. Tableau

Source: Tableau

Tableau is a great data visualization tool that makes it quite different from other platforms. It is a very powerful big data analytical tool as it communicates insights in the form of visual data.

The platform offers a drag-and-drop function that enables users to customize and rearrange visual elements of data. In the analytics process, its visuals allow quick investigations and an easy way to explore data.

However, the tool goes beyond this to offer a whole set of additional functionalities to make it such an important data analytics tool.

Its features include:

• End-to-end analytics
• Advanced Visualizations
• Live and In-memory Data
• Collaboration and Sharing
• Responsive user interface
• Pre-installed information maps
• Advanced data calculations
• Simple content discoveries
• Fully protected system
• Reduced security risks
• Improved activity feed, Ask data, SAP HANA connectors

The tool is available in multiple formats including individual and team-focused platforms.

Tableau is considered one of the best Business Intelligence and data visualization tools. Its most important quality includes organizing, visualizing, managing, and understanding data for its users.

#5. Talend

Source: sysbus.eu

Talend is a great tool for integrating enormous real-time data libraries while making sure that it remains of high quality. The big open-source data analytical tool makes the process of automating data integration, filtering, and management easy.

Gartner says that poor data quality costs U.S. businesses anywhere between $9.7 million and $14.2 million annually. So, Talent helps you take necessary preventative measures with its data-cleaning services and saves you from trouble.

The developers of this platform say, “Talend uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.”

Its features include:

• Faster completion of big data projects
• Handles multiple data sources
• Simpler and more accurate ETL and ELT processes
• Easier to use Spark thanks to the native code generation
• Powerful natural language processing and machine learning
• Smarter data quality with NLP and ML
• Provides numerous connectors
• Simplifies using MapReduce and Spark
• Agile DevOps to speed up big data projects
• Streamline DevOps processes

The platform’s important ability is to allow big data integration which helps master data management to check data quality by accelerating time.

#6. Splice Machine

Splice Machine is an open-source big data analytics tool. Its architecture has been designed to be portable across public clouds such as AWS, Azure, and Google. The seamlessly custom packaging of these compute engines help you save time and money to duct tape systems yourself.

The tool can scale dynamically from a few to thousands of nodes to enable applications at every scale.

Its optimizer can automatically evaluate queries to the distributed HBase regions for faster deployment, reduce management, and reduce risk.

Its features include:

• Scale-out SQL RDBMS
• ACID transactions
• In-memory analytics
• In-database machine learning
• Low latency OLTP reads and writes
• Auto-shards row-based storage across region servers
• Secondary indexes to support many access keys
• Dynamically scale from a few to thousands of nodes
• Automatically evaluates the query
• Consume fast streaming data

Platform’s dual model leverages columnar external tables. It provides cost-effective storage on cloud block storage, HDFS, or local files as Parquet, ORC, or Avro files. For hybrid computation, first-class tables also be joined with low-latency row-based storage.

Final Thoughts:

In this blog post, I have tried to cover the most popular big data analysis tools used for marketers. These tools can help you with all your big data issues so that you can concentrate on improving your campaign results.

Author Bio: 

Shreeya Chourasia, Writing is food to my soul and the quote that inspires me to keep learning is “Learning never exhausts the mind” by Leonardo da Vinci. Tech Research Online has given me a platform to do just that, I learn and I write and then I learn a little more.