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DevSecOps

What Is Big Data? Definition, Benefits, Management.

What Is Big Data? Definition, Benefits, Management.

Nearly 74 zettabytes of data were created in 2021 alone, which only grows as time passes. Big Data, a term used to denote huge databases, is trending for obvious reasons. As you want to use the power of data, learning about Big Data architecture and other aspects is crucial. Here is crisp and updated data to refer to.

Learning Objectives

What Is Big Data?

The world is surrounded by data, and Big Data refers to the collection of all the concerning data. It includes fully-structured, partially-structured, and unstructured data that an organization gathers throughout its operational duration.  

The dataset is often so huge that legacy data analysis software fails to process it. Hence, advanced tools and techniques are required to derive value from Big Data. As history suggests, the term came into use around the late 90s, and John Mashey popularized the term.

Big data technology makes the foundation of data analysis, because it generates raw data which is further sorted, analyzed, and managed to drive results and insights from datasets. Most commonly, technologies like machine learning, predictive modeling, automation, and advanced analytics are used to make sense of the available big data.

History of Big Data

As mentioned above, this concept came into being in the late 90s officially. But, its existence is older than this. We can trace its origin to the 1960s when the concept of data was shaping. As big data represents the entire data collection, it existed when the world started using data in huge quantities.

A very evident instance of big data was observed in 1880 in the form of a census. The Hollerith Tabulating Machine was used for the job.

In 1928, Fritz Pfleumer developed magnetic data storage on tape that laid the foundation of digital data storage.  

The term became famous in 2005 as, by that time, the power of data was unleashed greatly. Internet penetration became deeper, and organizations started using data in almost every workflow.

In the same year, the world introduced technologies like Hadoop and NoSQL that speed up the data collection process. More and more data is being collected, analyzed, and stored. As data collection is amplified, big data becomes less humane and more automated.

Presently, 90% of big data jobs are automated, backed by AI, and use technology like ML. Cloud computing is not the foremost choice for effective data storage as it enables businesses to access data anytime and from anywhere.  

By 2014, cloud-based ERP and IoT device usage had touched new highs, and more real-time data was collected. If the current trend continues to flow at the same pace, the world will likely have over 180 zettabytes of data by the end of 2025.

The Importance Of Big Data

With time, big data is strengthening its grip and is becoming more relevant and important for businesses as it plays a key part in improving operations, customer experience, marketing campaigns, sales strategy, and various other operations.

Effective utilization can help any business to gain an edge over peers as it leads to direct access to result-driven data. Here are a few workflows that become perfect through the intervention of big data:

  • On-target marketing

Effective marketing is only possible when your audience optimizes the market strategy according to their needs and wants. With the help of big data, you can collect information such as demographic data, past purchases, search results, preferences, and so on.    

Marketing efforts, when optimized according to all these databases, will certainly deliver results.  

  • Predicting trends  

Big data is a great resource to use when a business or service provider wants to predict future trends, as it proffers substantial historical and present data. Such data, when analyzed properly, can make fruitful predictions. For instance, the medical research domain makes accurate disease diagnosis by looking deeper into past medical history.

  • Risk management

IT organizations, financial institutes, and other businesses use big data for timely and result-driven risk management. They can gather copious data about the risks, their occurrence possibilities, and their likelihood as they create viable risk management strategies.

  • Finding right opportunities

Even though the world is full of opportunities, not all are meant for a business. Businesses need to spot the right opportunities at the right time, and big data is of great help here. For instance, the energy industry used big data to spot prospective drilling locations by analyzing certain geographical data.

  • Optimized operations and service delivery

Those who are involved in the transportation and manufacturing industry bank heavily on big data to optimize key workflow and service delivery. Big data enables businesses to find the right delivery partner and optimize the data route on various fronts.

This is just a quick overview of the far-reaching capabilities of big data. Based on the business capabilities, one can use big data on various other fronts as well.

Types Of Big Data

The big data definition explains three key types of big data.

  • Unstructured data is crude data that lacks formatting and standardization
  • Semi-structured data is neither fully structured nor fully unstructured. Data in the XML file is an example of this data type.
  • Fully-structured data that is properly defined and stored in a standard format.

Benefits Of Big Data

  • Improved decisions as you have enough data to back your choices
  • Better innovation as big data analytics allows businesses to find new growth opportunities and create inventive resolutions
  • Enriched customer experience as you will have a hold over customer behavior and thought process  
  • Cost optimization as you can spot the operational flows and replace them with viable solutions
  • Smart recommendations, as Big Data can help businesses to understand behavior, make predictions, and provide optimized solutions.

“Three Vs” of Big Data

The six defining V’s of big data are:

  • Large data volume gathered from different ecosystems
  • Data variety as data of various sorts are collected
  • High-velocity data  
  • Data veracity that represents neatness and precision
  • The value that the insights/data bring to the business
  • There should be enough Variability, i.e., the data should be able to get formatted and incorporated in variable manners.

The first 3 defining traits will be introduced first in 2001. The last three Vs were added much later. As the first 3 Vs are most commonly used and hold maximum significance, we’ll explain them in detail next.

  • Volume

Big data is so huge that traditional data size units like megabytes and gigabytes are not used to denote it. It is calculated in zettabytes and petabytes. For those who have no idea how huge these units are, one zettabyte is equal to 250 billion DVDs together.

  • Variety

The majority of data is unstructured and features data from diverse resources.

  • Velocity

Data is created at high speed and in real time. In a blink of an eye, thousands of megabytes are captured. One fitting example of high-velocity data collection is the sensor data that a health device collects. It captures real-time data at full speed at a high pace.

The Other Three Vs

  • Veracity: Not all data can be trusted. Veracity indicates the trustworthiness of data.
  • Variability: Big data has high variability, which simply means that data is flexible enough to be formatted and used in various formats.
  • Value: Big data certainly adds some value to key businesses and processes.  

How Does Big Data Work?

The standardized methodology for this technology demands a deeper knowledge of underlying data and its detailed processing. The first stage is data collection. Businesses need to define their goals and collect relevant data. For instance, if an organization wants to collect data for marketing, it needs to define the type of data.

Then comes data preparation which is mainly about data profiling, filtering, validating, and transformation so that data is all set for the analytics. At this stage, all the collected data is categorized according to their values, and redundant data is filtered out from the datasets so that efforts and time are invested only in the data that holds certain significance.

After this stage comes the data science applications. At this stage, businesses use multiple data science tools & techniques to fetch essential details from the data gathered. Deep learning and ML are commonly used techniques here. In addition, data mining, data branching, streaming analytics, text mining & predictive modeling are also used.

Here is a snippet of standard processes that are part of big data analysis:

  • Relative analysis that involves close examination of end-user behavior and engagement/interaction with services/products of a business. It helps business ventures find their position in the cutthroat competitive world.
  • Social media listening. As SM is now a powerful platform, businesses can’t afford to ignore it. With this analysis, businesses can easily track what words are spread out of their business on social media.
  • Up next, we have marketing analytics that is used to check the real-time viability of running marketing campaigns.  
  • Lastly,  we will discuss sentiment analysis which refers to finding the data that shows how a customer feels about a business.

Big Data Processing And Storage

More than anything else, intelligent big data processing and storage are required to ensure the collected data is not at risk. In general, a data lake is used for big data storage, which is way more advanced than data warehouses.

A data lake is a flexible solution that can support a wide range of data types, mainly based on the Hadoop cluster.

As far as big data processing is required, the job is done using data mining and data preparation resources. These two prepare the data for further processing. Effective processing demands heavy computing architecture. In most cases, clustered systems deliver the demanded processing power.

Big Data Analytics

It is an easy way to have your hands on validated and relevant sights from collected data.  

The procedure generally begins with profiling of the details/data and then reaches phases like cleansing, validation, and database transformation. It allows data scientists and analysts to have an insightful hold over available data and make sense of it.  

Data is further processes to get rid of conflicts and redundancies. The next data analytics stage uses data science and data tools such as data mining and AI to analyze the final dataset.

Big Data Management And Tools

While one is willing to bring big data into action, it’s important to use some viable tools to avoid goof-ups. With the right kind of big data management tools, it’s easy to automate menial yet crucial acts, attain speed while making no compromise on accuracy, and bring more value to the table. Here is a rundown of consider-worthy options.

  • Hadoop – This open-source framework was released in 2006 and soon became the core of big data. Apache Spark is a dedicated big data tool that can log files securely, handle machine data, and control databases.
  • Storage repositories – With storage repositories, data management becomes easier than ever. Hadoop Distributed File System, Amazon Simple Storage Service, and Google Cloud Storage are some consider-worthy options to try out.
  • Data lake and data warehouse platforms – As big data is all about data processing, data lake tools are must-have resources. You can place your bet on Amazon Redshift, Delta Lake, Kylin, Google BigQuery, and Snowflake for sure.
  • Additionally, there are certain fully-managed IT services that you can try for pre-managed big data operations. Amazon EMR, Cloudera Data Platform, Google Cloud Dataproc, and Microsoft Azure HDInsight are a few names to take here.

Big Data Examples

Big data has deeper penetration in today’s business sphere and comes from various means. Every system/tool/process is a real-time example of big data. For instance, if you’re using a POS at your business, the data it’s collecting as your customers make the payment is an example of big data.  

Similarly, documents, email, mobile apps, social networks, and every other system that are part of an IT architecture and are part of customer/employee/workflow handling are examples of big data.

Big Data Challenges

While big data is a promising approach, it’s not challenge-free. As you plan you use it, make sure you’re aware of these challenges.

  • The evident challenge is the size of big data. As the name suggests, big data is very big. It’s no big deal that, at times, it becomes too tough to handle it. Organizations need to find viable strategies to store and analyze such huge data, pull off which is not easy.
  • The second biggest challenge is ensuring that data is safe during usage, storage, and at every other stage. Averagely, one cyber attack takes place every 39 seconds. Threats like malware attacks, unauthorized access, data leaks, and many more are there to harm the data stored. It takes a lot of effort to maintain data integrity at every stage.
  • Effective data curation is another major challenge that businesses of the current era are dealing with presently.  
  • Organizations have to struggle hard to ensure they have a hold over clean data that is worth the analysis efforts. However, data curation is indeed the hardest part of big data handling. If stats are to be believed, nearly 50 to 80% of total big data efforts are invested towards data curation only.
  • Lastly, the attention-worthy big data challenge to deal with is to maintain the pace with changing pace. Customer behaviors are changing, new technologies are evolving, and fresh datasets are required. Businesses have to be on their toes to find out what’s the most relevant trend and technology to follow.

Big Data Use Cases

The technology plays the backbone for assorted operations and workflows. Opportunities are endless. Here are the most common use cases of big data.

  • Product Development

Big data empowers product development for businesses of all sorts.

We have already seen what wonder big data has done for Netflix. The leading streaming platform used customer behavior data to find out what kind of content they were looking for and curated its services accordingly.

  • Predictive Maintenance

Big data is used widely as a preventive maintenance resource. It’s easy for organizations to find tool, operations, and workflow failures based on past big data and prevent further failure.

  • Customer Experience

Businesses that are winning customers’ hearts have won the race. Big data is helping the organization to learn about customers’ buying patterns, interests, behavior, and other aspects that influence a purchase.  

  • Fraud & Compliance

By analyzing past fraud patterns, big data can prevent fraud and even let businesses adhere to leading compliances.

  • Machine Learning

Big data technology empowers ML from behind as it’s used widely to teach machines. The more data you have, the more learned machines you’re making.

  • Improved Cyber Security

The growing number of cyberattacks is a serious issue for everyone, and big data technologies are helping to combat this challenge. With them, it’s easy to predict threats and create viable API security and IT security solutions.

Conclusion

Big data is a key process to adopt if one wants to feel empowered using data. It’s a standard approach assisting businesses to improve workflows, customer experience, overheads, and many other concerns. This guide explained the key big data concepts in an easy-to-understand manner. Refer to it as you wish to gain maximum benefits from the approach. While you do so, make sure to have a reliable Big Data security plan in place.

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