Get to Know What is Big Data Analytics and The Importance

Home » Get to Know What is Big Data Analytics and The Importance – What is big data analytics and how does it impact businesses? Big data is now getting so much attention in the business world. Customers create massive data daily through their activities and transactions. Those data sets come in various forms and from many sources that refer to big data.

Organizations to realize the importance of working with big data. They know that collecting and storing big data is not enough. They have to use it properly so it can give maximum benefits to the business. The fast-growing technology has helped organizations turn terabytes of data into applicable actions.

5 Important Things to Know Aside from What is Big Data Analytics

Although people begin to recognize big data analytics, there are still other important things to know. Aside from the definition, you should know how the process work and what includes in the process.

Some advantages and challenges affect the success of big data analytics. These several things help you to dig deeper into one of the essential processes related to big data.

1. What is Big Data Analytics?


Big data analytics denotes a process of unlocking hidden patterns, trends, and correlations from massive data. It is beneficial for the next step in the business which is making the right decision. The data analytics process applies common analysis techniques in statistics such as regression and clustering.

The process also uses newer tools to apply the results to more extensive data sets. Big data keep rising in popularity as software and hardware development allows organizations to manage enormous unstructured data. Even we can find more innovations in databases due to the explosion of data from multiple sources.

Those innovations provide reliable data storage and support big data processing. These large amounts of complex data come from a variety of sources such as smart devices, networks, websites, sensors, transactions, and so on.

Now, big data analytics works together with developing technologies like machine learning. It enables organizations to find out and sort out more complex insights. Hence, they can use the most suitable insights for different purposes for the success of their businesses.

2. How Does Big Data Analytics Work?


Aside from what is big data analytics, you must know how this process works. It is very important especially if you want to have a career in this field. Here are several processes of operationalizing big data.

a. Collect Data

Each organization uses different techniques in collecting data. The development of technology allows organizations to collect structured and unstructured data from various sources. Hence, they can take data from mobile apps, IoT sensors, cloud storage, and other sources.

Structure data will stay in data warehouses so it is accessible for business intelligence tools and solutions. Meanwhile, unstructured data which is too complex for warehouse storage will come to a data lake. Hence, organizations can process it later and take reliable insights from it.

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b. Process Data

After collecting and storing data, large and unstructured data requires a proper organization to create accurate results based on analytical requirements. Besides what is big data analytics, organizations must be aware of the exponential growth of data. This condition makes data processing more challenging.

This activity includes batch processing which means looking at large data blocks continuously. Batch processing is beneficial when it goes through a long turnaround time from collecting to analyzing data. Meanwhile, stream processing refers to observing small batches of data quickly.

It reduces the delay time in collecting and analyzing data so it makes decision-making even faster. However, stream processing is not only a more complex process but also more expensive.

c. Clean Data

Regardless of your data size, it needs scrubbing for better data quality and stronger results. All data should come in the correct formats. Moreover, you have to remove duplicative or irrelevant data. Dirty data may cause misleading and obscure, and possibly create flaws in the gathered insights.

d. Analyze Data

Preparing big data to be ready to use will take time. When it is ready, you can use advanced analytics processes to transform big data into big insights. Aside from what is big data analytics, you need to know about the methods in data analysis such as follows:

  • Data mining sorts across massive data sets to unlock hidden patterns. This method helps to identify the relationships by observing anomalies and building data clusters.
  • Predictive analytics uses previous data from organizations to create predictions about what possibly happen in the future. It forecasts potential risks and upcoming opportunities.
  • Deep learning resembles human learning patterns through artificial intelligence and machine learning. The function is to identify algorithms and discover patterns in the most complex and raw data.

3. What Are Tools and Technology Used in Big Data Analytics?


Big data analytics does not just associate with a single tool or technology. Instead, it combines the use of different tools and technologies to support the work. Here is a list of several tools and technologies which are common in the data analytics process.

1. Tableau

It is an end-to-end platform for the data analytics process. Not just giving an idea of what is big data analytics, it enables you to prepare, analyze, collaborate, and share insights from big data. Tableau is the best in providing self-service visual analysis.

It allows you to ask new questions about collected big data. Hence, you will be more understanding about what kinds of necessary data. It offers easier sharing of insights throughout an organization.

2. Hadoop

It refers to an open-source framework that offers efficient storage and processing of large data sets on clusters of hardware. It can manage massive amounts of both structured and unstructured data. Most importantly, it is free to use which also makes it a valuable option for operating big data.

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3. MapReduce

It is an important component that supports Hadoop in delivering its two functions. The first function is mapping which refers to filtering data to diverse nodes inside the cluster. The second function is reducing which denotes organizing and reducing the results from a node to answer the question.

4. NoSQL databases

It is non-relational data management system without a fixed scheme. This type of system becomes a good option for analyzing large, raw, and unstructured data. It is similar to the function of what is big data analytics. Its name, NoSQL is an abbreviation for ‘not only SQL’.

It means that these databases can manage various data models in the process of big data analytics.

5. Spark

It is a cluster computing framework in open source type that applies implicit data parallelism and tolerates fault. It comes with an interface to program the whole cluster. This framework supports quick computation because it can manage both batch and stream processing.


It comes from the abbreviation of Yet Another Resource Negotiator. It is also a component of the second generation of Hadoop that works for cluster management. This technology is beneficial not only for resource cluster management but also for job scheduling.

4. What Are the Main Advantages of Big Data Analytics?


A faster way of analyzing data is one of the big data analytics advantages for organizations. It allows organizations to use data more efficiently to provide an answer to important queries. It enables organizations to use massive data in various formats and many sources for different purposes.

It helps organizations to move faster, identify opportunities and risks, and improves their weaknesses. These are the main advantages of big data analytics:

  1. It is useful for product development by giving a better understanding of the needs of the customer.
  2. Like what is big data analytics, it provides market insights by tracking shopping behavior and identifying market trends.
  3. It promotes cost savings for business operations as it finds out how to do the business more efficiently.

5. What Kinds of Challenges to Face in Big Data Analytics?


Besides its main advantages, there are also main challenges of big data analytics. The challenges related to making the right solutions to meet the business needs, privacy and security factors, and user accessibility. To use arriving data, organizations should address these several challenges:

1. Data security

Since the amount of data keeps growing continuously, the issues related to privacy and security also increasing. Organizations need to protect the data securely, especially if it is about business confidentiality. Customer personal information is also valuable information that is prone to privacy issues.

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People may hack the system to steal customers’ personal information and use it for their benefit. Besides what is big data analytics, organizations must put important data in the right place. It is to prevent strangers to get benefits from this data.

2. Data quality

Despite its responsibility to maintain massive and complex data, organizations face some challenges related to this maintenance process. Organizations require time to scrub duplication of data and handle an error during data maintenance.

While performing what is big data analytics, they must deal with data absences. Not to mention conflicts that might appear and disturb the quality of data.

Sometimes organizations have to solve the inconsistencies in data collecting that can decrease the data quality. They have to make effective strategies to ensure that data quality remains in a good condition.

3. Data accessibility

When conducting what is big data analytics, organizations may face another main challenge which is providing data accessibility. The exponential amounts of data growth have made data processing more difficult. It also comes with some challenges that may disturb the data collecting process.

Organizations should make data to be accessible for users of all skill levels. Hence, they need to make data to be easy and comfortable to use. Hence, those who work with insights from big data can get benefits and use it for different purposes.

4. Appropriate tools and platforms

Besides what is big data analytics, organizations should concern about new technologies that can support big data processing and analyzing. They have to use the right technology to work in their recognized ecosystem. The technology used in data analytics process should meet the specific needs of organizations.

It is the reason why new tools and platforms in this filed keep developing. Most likely, the right solution for tools and technologies is the flexible one. It is because the solution likely to support infrastructure changes in the future. It means that technologies should be dynamic with the changes in big data analytics.

Similar to what is big data analytics, organizations must understand about some important related to this process. Doing so helps the organizations to achieve the goal in data analytics. They not only can improve the efficiency of time and money from their operational but also empower the new model of their business.

From effective big data analytics, organizations can increase their awareness on potential challenges they may face. Hence, they can plan powerful strategies to handle specific challenges. They understand the tools and technologies required to support the work of big data analytics and how to use them properly.

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