Big Data Analytics: Secret Crazy Tech Behind The Advancement Of Top Businesses

Let’s believe you and I have just started a business together. Now, what’s the core purpose of our business! To serve our customers, right? Well, how will we serve your customer then! Obviously by knowing some specific facts about them. But the crucial part is these facts don’t remain the same all the time. Most of these are variables and their values change over time and it’s quite common. So, what we need to do is, we will have to collect all kinds of data related to those facts from time to time. It may seem easy when we are dealing with a few queries. We can take help online or go to the field and collect all data we need, sounds ok for now. But how about we have a huge list of queries and we need to collect them at every moment! Looking online or going to the field won’t work anymore. Things will get much tougher then. Sometimes, it will happen like the data we have collected doesn’t fulfill all the things that are required right now to make smart decisions for our business. We need more data to keep your business up and running smoothly. Well, not only we, the whole world is already facing this crisis. That’s where the charm ‘Big Data Analytics’ has come into play with a marvelous solution.

The Definition Of Big Data Analytics

Big Data Analytics (BDA) is a complex process that works on very large, diverse data sets that include structured, semi-structured, and unstructured data collected from various sources and the size of data may range from terabytes to zettabytes (one zettabyte is equal to a trillion gigabytes).

Big Data Analytics is used to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. The data sets for Big Data Analytics aren’t like those normal databases that are used in various business organizations. The type and size of these data sets are beyond the ability of our traditional relational databases. The ability to capture, manage and process the data of these data sets with low latency is also much higher. Big Data Analytics can give us all kinds of data we need for our business using a bunch of great tools and technologies following a set of instructions. IoT, sensors, credit cards, facial recognition cameras, social media activity, heatmaps, cookies, GPS tracking, signal trackers, in-store wifi activity, gameplay, satellite Imagery, employer databases, inbox- all of these are playing as major sources of information for Big Data Collection.

How Big Data Analytics Came Into Existence

People are using the term ‘Big Data’ since the early 1990s. Though it’s not clear how it first came into our life or who first used this term, but the most known fact is about a man named John R. Mashey, who at the time worked at Silicon Graphics, for his effort of making this term popular.

Don’t think that the whole concept of Big Data is new. People have been trying to use proper data analysis and analytics for better decision-making over the course of centuries. Around 300 BC, the ancient Egyptians tried to collect all existing data and store them in the Alexandria library for future use. Also, the Roman Empire used to analyze the military statistics in terms of determining the optimal distribution for their armies. History will surely give us more examples like these.

Time to take a close look, the data generation speed and volume have changed in the last two decades and it has crossed the human limitation of understanding all of them properly. In 2013, the world’s total amount of data was 4.4 zettabytes. But here in 2020, that amount has now become 44 zettabytes. In a word, this is a clear exponential growth. Our modern technologies don’t have enough capabilities to deal with this giant amount of data and give us our required solution. Hence, for the proper use of these huge amounts of data our traditional data analysis opened the gateway of Big Data Analytics.

Types Of Big Data

The whole gigantic world of Big Data is the combination of three types of data- structured, semi-structured, and unstructured data. Let’s take a brief look into these.

1) Structured Data: These data consist of fixed-format, highly organized, and well-defined structures. Most of the time, these data are handled by machines rather than humans. This kind of data is already managed by business organizations in databases and spreadsheets stored in SQL databases, data lakes, and data warehouses.

2) Unstructured Data: This kind of data does not follow any specified format. Unstructured data (Raw data) is hard to categorize in existing databases and it comes in many different formats. Emails, text messages, documents, images, videos, metadata are common examples of unstructured data. To retrieve valuable information from these data Hadoop like clusters or NoSQL systems are used.

3) Semi-structured data: The data that contains semantic tags and elements, do not follow the structure attached to the relational databases are known as semi-structured data. XML, JSON files are common examples of semi-structured data.

How Big Data Analytics Works

The whole process of Big Data Analytics is done by four consecutive steps- collecting, processing, cleaning, and analyzing. Large datasheets need to go under these 4 steps to extract the quality data that can help a business for their further operations. Let’s see how these 4 steps are done.

1) Data Collection: In today’s world, the process of data collection is different for each business organization. With the help of today’s technology, business organizations collect both structured and unstructured data from cloud storage to mobile applications to in-store IoT sensors and beyond. These data are collected from a vast amount of resources. Some data are stored in the data warehouses, which makes the business intelligence tools and solutions (BI Platforms) access these data efficiently. Unstructured data are stored in a data lake because of its complexity.

2) Data Processing: To get the required information from the collected data, data processing is mandatory. Data is being generated at an exponential rate, so properly organize these data has now become a real challenge. We have two options in our hands for data processing. The first one is batch processing, which is an effective way of processing a huge amount of data that has been collected over a period. And another option is Stream processing that lets the users get their required results based upon their query within the shortest possible time. Point to noted that stream processing is much costly than batch processing.

3) Data Cleaning: To ensure data quality and get stronger results, data cleaning is a must. In this process, all data get proper formation and the useless data gets eliminated. Then the final data gets accounted for better decision making.

4) Data Analyzing: Here comes the last and final step. Now all things that require data analysis is done and it’s time to convert the valuable data into valuable insights. There are lots of methods for Big Data analytics, I am mentioning some of them below-

• Data Mining: This process analyzes a massive volume of data to discover hidden patterns of data according to different perspectives to categorize useful information.
• Predictive analytics: This is a branch of advanced analytics that uses data with the help of statistical algorithms and machine learning techniques to make future predictions of various unknown events based on present data.
• Machine Learning: In this system, Big Data is used by its algorithms to define the incoming data and to identify patterns attached to it. This system transforms data into valuable insights that we can use for further business operations and automate some processes of decision-making.
• Deep Learning: This is part of machine learning that imitates the way humans gain certain types of knowledge and patterns by relying on the layers of Artificial Neural Network (ANN).

The 6Vs Behind Big Data

We can describe Big Data properly with 6Vs. When Big Data first came into existence, there were only 3Vs. Don’t think that it’s over in 6Vs, the number of Vs is always increasing depending on the need for Big Data. Let’s dive into these 6Vs.

1) Volume: In ‘Big Data terms, volume refers to that we are dealing with a gigantic amount of data. These data can be of unknown value, like how often a website has been visited, how many clicks have happened on a webpage, or mobile phone. Gigabyte can’t help us now to measure the size of these data, we need Zettabytes (ZB) or even Yottabytes (YB) to help ourselves in this field.

2) Velocity: Velocity shows us how fast data is being received or transmitted. When data is streamed directly into memory rather than written on a disk, the speed will be much higher, and it can provide real-time data in this environment. Velocity is the most vital V among others cause it’s linked to machine learning and artificial intelligence.

3) Variety: Variety means the types of data that are available in the large datasheets. These data can be structured or unstructured. Processing this data is a crucial part of Big Data Analytics when the data itself is changing rapidly.

4) Veracity: Veracity describes the data accuracy level of the data sets. Data are coming from lots of resources. We need to verify our data every time to avoid any chaos. Unverified low-quality data will direct organizations to make irrelevant decisions.

5) Variability: A single piece of data can be used for multiple purposes. So, to use data for multiple purposes, we need to format data in many ways. This will save a huge time as the total process to come this far again is too lengthy. Variability refers to the same thing as using data for multiple purposes to get efficient results in a short time.

6) Value: Value is the last V of these 6Vs. Valuable data will be extracted out from the large data sheets that will benefit organizations in better decision making. Irrelevant data are needed to be cleaned out to avoid any kind of false alarm for any current purpose.

Big Data Tools And Technologies

No matter what, we need Big Data but we can’t stop its growth rate in any way. To deal with this massive amount of data, we need more advanced technologies. The good news is the tools and technologies that we use for Big Data Analytics are developing faster to tackle the situation. Different organizations use different kinds of tools and technologies for Big Data Analytics. And the most interesting part is there are lots of tools and a huge part of them is open source. Let’s dive into the top 10 open sources Big Data Tools that are used in this field.

1) MongoDB: MongoDB is a cross-platform document-oriented database program, comes with the scalability and flexibility that is needed for querying and indexing. MongoDB is categorized as NoSQL and distributed database program. In MongoDB, we don’t have to worry about the data structure like- the number of fields, types of fields, or store values.

2) Pandas: Pandas is one of the most popular Python libraries in data science that is fast, powerful, flexible, and easy to use for developers or professionals. This open-source platform is vastly used for data analysis and manipulation purposes. Pandas have a great community, built-in Data Visualization, Built-in support for CSV, SQL, HTML, JSON, pickle, excel, clipboard, and a lot more that makes it one of the most efficient tools.

3) Hadoop: Hadoop or Apache Hadoop is a java-based open-source software framework that allows storing and running applications on clusters of commodity hardware. Hadoop stores huge unstructured data without the specification of any schema. Hadoop has high scalability function. We can get maximum performance from it by increasing the number of nodes. For those who want to choose data science as their career, learning Hadoop is a must.

4) Apache Spark: Another cool name that appears where Big Data comes is Apache Spark. Apache Spark, also known as one of the key cluster-computing frameworks in the world, is a unified analytics engine for large-scale data processing. This open-source platform is a distributed processing system that can quickly run complicated algorithms to achieve higher performance for data streams.

5) Apache Storm: Apache Storm is a distributed real-time big data processing framework that was released by Twitter. This platform is mostly known for its reliable processing of unbounded streams of data and doing real-time data processing. Spark can also give guaranteed data processing and the ability to replay the unsuccessfully processed data for the first time.

6) Cassandra: Another open-source Big Data tool, developed by Apache Software Foundation (ASF), well recognized for its scalability. Cassandra can simultaneously handle a petabytes amount of data and other operations per second with continuous availability(zero downtime). This platform is internally used on Facebook, Twitter, Netflix, Cisco, and many more. Cassandra gives a huge help to the business organizations while managing large datasheets across hybrid cloud and multi-cloud environments.

7) RapidMiner: RapidMiner is a java-based open and extensible data science platform that unites data prep, machine learning & predictive model deployment. This is one of the leading cross-platform tools that offers some interesting key features like real-time scoring, enterprise scalability, graphical user interface, scheduling, one-click deployment, and more. The report from the official website of RapidMiner says, around 40, 000+ business organizations of the whole world use this tool to ensure their various major organizational issues such as drive revenue, reduce costs and avoid risks.

8) HPCC: HPCC(High-Performance Computing Cluster) is a powerful and versatile end-to-end data lake management solution that is written in C++. It gives developers enough functionality to manipulate data as needed. The key advantage of this system is its lightweight core architecture. HPCC system can ensure better performance, near real-time results, and a full-spectrum operational scale without the need for a massive development team, unnecessary add-ons, or increased processing costs.

9) Neo4j: Neo4j is referred to as an open-source, NoSQL graph database management system developed by Neo4j, Inc. Neo4j has a huge customer base with the world’s top business organizations for instance Adobe, eBay, Microsoft, IBM, Telenor, Cisco, HP, and many more. The core advantage of this platform is graph technology. With powerful optimization and simple design and it comes with a number of great features such as data model (flexible schema), ACID(Atomicity, Consistency, Isolation, and Durability) properties, Scalability, and reliability, Cypher Query Language, Built-in web application, Drivers, and Indexing.

10) R (Programming Tool): R is a programming language and free software for statistical computing, data analytics, scientific research, and graphics, developed by Ross Ihaka and Robert Gentleman in 1993. R runs on all kinds of operating systems (Windows, Linux, macOS) and it is entrusted with a large number of business organizations including Google, Microsoft, Ford, Twitter, Airbnb, and many more. This is interpreted language that means we don’t need a compiler for this to turn all the codes into machine understandable language.

Application Of Big Data Analytics In Business

Well, here comes the core part. It’s time to make Big Data our best friend. Businesses use Big Data on a wide scale that is beyond our imagination. Big Data can give us almost all kinds of business solutions if we fed it with the right data. Now let’s observe the top 10 applications of Big Data Analytics in business.

1) Big Data in Banking and Securities: Big data is helping this sector to analyze financial market activity. With the help of network analytics and natural language, Big Data Analytics is working against illegal trading activity in the financial market. This analytics plays a crucial role in fraud detection, risk management, predictive analytics, anti-money laundering, high-frequency trading, etc.

2) Big Data in Communications, Media, and Entertainment: Business organizations are using customer data and their behavioral data for content creation, high-demand content identification, content performance measurement, etc. Big Data is helping this sector to gather information related to content showing time, the reason behind the subscription or un-subscription of content, setting a target group, new feature generation, and many more.

3) Big Data for Healthcare Providers: Today’s healthcare sector is largely indebted to Big Data for fast decision-making and the right treatment for the right patient. Big Data is providing great solutions in this field such as patient data monitoring, store sensitive records with proper security and efficiency, predicting a large outbreak, analyzing conditions of patients with incurable diseases and save lives, etc.

4) Education with Big Data: Big Data is contributing to the education sector by analyzing students’ overall activity from time to time. This technology has the power to take all responsibility to ensure quality education by innovating a new data-driven approach to teach students more efficiently. Big data can store, manage, and analyze students’ records of large datasheets with maximum security, can prevent questions from getting leaked before the exam, can track student’s facial expressions and movements, etc.

5) Big Data in Manufacturing and Natural Resources: Big data uses geospatial data, graphical data, text, and temporal data for predictive modeling. Big data can analyze productivity data, power consumption data, data related to the amount of water or air need for machines, and give organizations the result they need for better decision-making. With the help of Big Data organizations like this can ensure manufacturing improvement, product design customization, supply chain management, better quality assurance, and potential risk management.

6) Big Data and Government: The government sector is handling several types of national and international affairs daily. Big data is collecting massive information about millions of people to make meaningful changes in this sector. The application of Big Data Analytics can create an enormous impact to improve the current situation, acknowledge public demand, predict any terrorist attack, fast emergency response, analyze workforce effectiveness.

7) Application of Big Data in Insurance: Big Data Analytics is impacting the insurance sector by analyzing the customer database to extract out the information of many customer insights- their behavior, choice, potential issue, and customer approach. It helps organizations with fraud detection, proper investigation, solid decision making, increase in profit margin, and so on. This sector is largely indebted to Big Data for workflow improvement and operational cost optimization.

8) Big Data in Retail and Wholesale trade: The improvement of customer experience using their own information is the top key fact in this field. Many world-class business organizations are using this vast amount of information to analyze customer’s spending patterns, supply-demand ratios, set up business strategies, right time decisions, transactional data, online reputation data, etc. From new product development to marketing campaigns and finally selling to customers, no doubt Big Data is the leader everywhere.

9) Transportation using Big Data: In today’s world, both government and non-government individuals are using Big Data for smart transportation systems. Traffic control, route planning, freight movements, intelligent transport systems- all of these are happening much smoother than before because of this technology. Organizations are using this blessing for better revenue management, reduce environmental impact, improving user experience, inventory determination, safety increase, and more noticeable facts.

10) Big data in Energy and Utilities: Big data is playing a strategic role in dynamic energy management in smart grids. Smart grids offer a two-way flow of data and power between consumers and suppliers. The data from smart meters are used to analyze power planning, energy optimization, and customer satisfaction.

Big Data Comes With Big Challenges

Great opportunity comes with great challenges, the same goes for Big Data too. Without proper extraction from raw data, Big Data can be a big danger for the whole world. Let’s take a look at some remarkable challenges faced in this domain.

1) Data Synchronization difficulty between multiple sources: Extracting valuable data from a giant data sea is quite a sneaky task indeed. Data synchronization is an essential part of this process to maintain data consistency. Data synchronization raises some big problems when the data origination points increase, mainly for isolated and diverse data. Though some vendors are serving quality tools, still many organizations haven’t sorted out this problem.

2) Massive amount of Data Management: Big Data can open doors for big achievements no doubt. But after the data storing part, the data management part still gives us some noteworthy troubles. As the amount of Big Data is increasing, the lack of management is getting clearer day by day. One of the solutions to sort out this problem can be the implementation of combined hybrid relational databases and NoSQL databases.

3) Data privacy and security concerns: We can easily address this as a top issue without any arguments. Big Data contains an enormous amount of sensitive information, so the security needs to be very high for maximum protection. But the security model that we see now in today’s world for Big Data is not effective enough. The open-source tools that are used for this analytics are not designed to keep security concerns in mind and this drives to many unknown threats.

4) Huge cost for Big Data management: Big Data has some obvious costs, such as cost for data storage infrastructure, software maintenance, hiring professionals, data backup cost, and networking data cost. Apart from these, there are miscellaneous costs that arise from various sectors. When organizations need more data for their business all of these costs rise proportionally. Organizations can use data lake as their data storage as it is the cheapest way, but the other costs need attention to proper planning and strategies.

5) Search for the right Big Data Talent: Finding the actual person as per an organization’s needs is too hard. Without skilled and experienced professionals, the whole setup will be a complete failure. To resolve this problem, organizations are using machine learning, artificial intelligence(AI), and automation to extract meaningful data.

Future Of Big Data

As time passes, the number of business organizations that are adopting this data-driven technology is increasing. Because of the exponential growth, storing data on physical devices won’t be possible anymore. A report from Seagate shows that the global data quantity will reach 175 zettabytes by 2025. To handle the situation data will be stored in a hybrid and multi-cloud environment. To extract out valuable data, the demand for data scientists and Chief Data Officer(CDO) will increase. To keep the whole system safe against any vulnerability more security and privacy concerns will arise. Humans will buy more algorithms than software. AI and machine learning will dominate this sector and altogether this will be used as a merge technology. Data will be more refined and will give us more fruit than the present. Maybe, we won’t use the term Big Data anymore, rather we will call it Smart Data.