Businesses can gain an incredible amount of valuable insights from their customers when implementing data analysis tools that are built to organize, sort, and gather statistical conclusions from many sources of data.
The growth of e-commerce continues to increase with over 1 billion shoppers worldwide making online purchases, which totaled $1.19 trillion in 2016. According to Statista, by 2019, the number of consumers purchasing goods and services online is expected to increase to over two billion, while the global e-retail sales amount is projected to increase by twice this amount in 2020.
Big data analytics tools have helped to change the way businesses process information. Alongside information security, big data analytics is a powerful and effective tool that helps data scientists, marketers, and IT managers alike understand and develop increased privacy protection of how data is shared and stored.
Big data analytics tools allow for new methods of not only tracking customer behavior, but tracking cybercrimes from a variety of sources as well. Using this technology in information security will also change the way we process security algorithms. It is important for companies to analyze how trends in big data analytics tools will help minimize cyber-crimes and new threats.
Here are some of the top big data analytical tools to consider for your business throughout 2019:
# KNIME Analytics Platform is an open source software that allows for easier designing of data science workflows and reusable components accessible to all users.
Some Key Features Include:
- Users can now choose over 2,000 modules to build workflow.
- No coding needed with its convenient drag-and-drop feature for creating visual workflows.
- Choose from hundreds of example workflows.
- Open and combine simple text formats such as CSV, PDF, XLS, images, documents, and much more!
- Connect to a host of databases and data warehouses for data integration.
- Access and retrieve data from sources such as Twitter, Google Sheets, and Azure.
- Derive statistics, including: mean, quantiles, and standard deviation. Apply statistical tests to validate a hypothesis.
- Aggregate, sort, filter, and join data locally, in-database, or in distributed big data environments.
#OpenRefine, formally Google Refine, has a powerful big data analytics tool for cleaning “messy data” by transforming it from one format to the other.
What Makes this Big Data Analytics Tool Stand Out?
- Explore data
- Clean and transform data
- Match data
# Orange is an open source machine learning and data visualization tool using interactive data analysis workflows.
- Users can mine data visualization or Python scripting
- Users have the ability to fetch data from external web services to match data from other sources
# Tableau Public is one of the most popular big data tools on the market. It allows you to publish interactive data to the web. However, the free version is limited to only 1 GB of storage and 1 million rows of data. Tableau boasts of its most powerful, secure, and flexible end-to-end analytics platform. Prepare, create, explore, or view trusted data when you subscribe to its governed self-service analytics platform.
Highlights of this Big Data Tool Include:
- Users can create interactive graphs, charts, live dashboards, applications, and maps which can be embedded on the web.
- Allows for interactive elements to be displayed across the web.
# Trifacta Wrangler – This big data analysis tool helps users clean and prepare messy, diverse data faster and accurately. Their machine learning algorithms helps you prepare your data by suggesting common transformations and aggregations. Once you’re ready, you can export the file to be used for data initiatives like data visualization.
As the digital economy evolves, more companies are faced with managing loads of data, and the need for big data analytics tools increase in demand. Dr. Kamaljit Anand said, “The leveraging of machine learning and traditional algorithms to analyze the Big data for any organization can solve problems in multiple verticals and forecast the business future with greater speed and reliability.”
Big data analytics has become a necessity. According to Dr. Anand, by the end of 2020, the big data volume is projected to reach 44 trillion gigabytes, which will globally change the way we do business.