‘Data is the new oil.’ Clive Humby, a data science entrepreneur coined this phrase back in 2006. Just after five years of it, the senior vice-president of Gartner, Peter Sondergaard expanded it to analytics by saying, “Information is the oil of the 21st century, and analytics is the combustion engine.” Big data analytics tools and technologies have made a huge impact on the way we work in less than two decades. Globally, more companies rely on big data tools to remain competitive and achieve growth by making meaningful decisions in real time.
In this article, we are going to dig deep into big data analytics tools and technologies. Let’s start from scratch and get a brief introduction to big data analytics.
What is Big Data Analytics?
Primarily, big data is the volume of huge data sets that contain tens of terabytes of data. We can categorize big data into three types of data sets- structured, semi-structured, and unstructured. Big Data Analytics is the process of extracting useful information from such big data sets of different types. Big data analytics can discover hidden patterns, consumer preferences, market trends, and the like. All such information is useful for facilitating organizational decision-making. Big data analytics involves several steps and technologies.
Big data tools and technologies are of several types as well. Before understanding these types, we will know more about big data technology.
What is Big Data Technology?
As mentioned above, big data refers to a large volume of massive data sets that have varieties of information assets. It is not possible to extract actionable insights or useful information from such large data sets using traditional data processing. Companies, therefore, rely on advanced technologies to gain more insight and make profitable decisions. In a way, big data technologies are defined as software utilities designed to analyze, process, and extract information from huge datasets that have a highly complex structure.
Big data technologies bridge the gap between traditional technologies including file systems and RDBMS and rapidly growing business requirements. Big data tools and technologies are meant for analyzing and evaluating data. As an umbrella term, big data technologies consist of various tools, data frameworks, and techniques for storing, examining, and processing corporate data. Big data analytics software can help companies reduce future risks while giving them a competitive edge and agility.
Types of Big Data Technology
Advancing technology enables developers to come up with a robust big data analytics software. We can convert big data technologies into two types on the basis of their functionality- Operational and Analytical.
All the data generated from day-to-day activities including online transactions, social media posts, or any corporate information fall under the operational big data category. It serves as raw data and Operational big data technology is useful to analyze it effectively. Here are some most common examples of operational information.
- Online trading or shopping-related information
- Online information related to social networking sites like Facebook and WhatsApp
- Online ticket booking for trains, flights, movies, etc.
- Data of employees who work in MNCs
Analytical big data technologies are more complicated than operational big data technologies.
This is an advanced and modified version of big data technologies. It is useful when performance metrics are involved and a need of making critical business decisions based on operational big data analysis. In a way, analytical technologies are designed to analyze big data that can drive decision-making. Here are some examples of analytical technologies for big data.
- Stock market-related data
- Data related to weather forecast
- EHR and other health-related data
- Space mission databases for future missions
These days, many big data tools and technologies are available in the market. We can divide them into four broad categories and give some popular big data tools for each of these categories.
- Data Storage- Apache Hadoop, MongoDB
- Data Mining- Presto, RapidMiner
- Data Visualization- Plotly, Tableau
- Data Analytics- Kafka, Splunk
It is, however, essential to choose the right big data tool and technology to leverage the benefits of big data. Here we mention the top factors you need to consider to select the most suitable big data tools and technologies for your enterprise.
Choosing the Right Big Data Tools and Technologies: Important Considerations
Selecting the right big data tools and technologies is quite a task especially when many options are available in the market. Big data software can simplify and streamline the process in your enterprise, and therefore, you need to select the most suitable big data tool for your company. Following characteristics will help you choose an ideal big data tool.
Understand Business Needs
A big data tool you select should meet both current and future requirements of your business. You can define essential goals of your enterprise and make a roadmap to achieve them. In the second stage, these goals are broken into quantitative analytics goals. Finally, you can choose the big data tools and technologies that assist you to meet these quantitative goals.
It is necessary to calculate costs associated with the big data analytics software before selecting. These costs include memberships, additional expenditures, licensing, and other expenses. Different big data tools and technologies have different structures, and therefore, it is necessary to consider the cost factor.
Keep It Simple
Big data tools should be user-friendly. The tool you select should be simple enough so that even non-technical employees can also understand dashboards. Simple and interactive UI (User Interface) can make the difference as you get the benefits of big data software.
A big data tool can be standalone or an integrated one. Though multiple options are available in standalone solutions, integrated solutions offer Analytics from the familiar apps. But then, you need to ensure that the Analytics platform is capable of integrating efficiently with the existing business system and third-party data sources.
Ensure High Security
It is essential for the big data tool to remain highly secure and yet scalable. The tool deals with confidential and sensitive corporate data and therefore, it is necessary to check whether it can safeguard it effectively. Also, the big data tool you choose should be agile and scalable enough to expand with the expansion of your business.
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The Key Advantages of Big Data Tools and Technologies
Let’s go through some of the major benefits of big data tools and technologies for your company.
1. Enhanced Risk Management
Big data insights are useful in predicting risks and preparing businesses for any unexpected scenario.
2. Improved Decision-making
Actionable insights in real-time can improve the decision-making process significantly for your company.
3. Innovative Approach
Accurate information related to market trends and customer behavior assists you to implement innovations.
4. Better Customer Experience
Real-time data related to shopping behavior and customer expectations can help you improve customer experiences.
Big data tools and technologies can be a game-changer for your thriving business. On one hand, big data analytics can assist you to improve services and on the other hand it can keep your company ahead of the curve. Hope these important considerations will assist you to choose the right big data tool for your company.
Silver Touch Technologies is a renowned and reputed big data analytics solutions provider. We assist industries to leverage the benefits of big data for making insightful decisions. Companies can identify growth opportunities and drive digital transformation through effective data management capability irrespective of industry sectors and scales. Our solution can unleash the potential of big data analytics to give our clients a competitive edge and resilience with high agility. Would you like to know more about our big data analytics services? Simply mail us at email@example.com.