What is Big Data Analysis and Why It Is important?

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What Is Big Data Analysis?

Big Data analytics is, simply put, the process of analyzing Big Data. 

Big Data, on the other hand, is (very) large amount of data sets that is also diverse at the same time. Diverse, in the sense that the data can come from many different sources, in different sizes(from ‘just’ terabytes to zettabytes), and in different types.

Thus, big data analysis will require the use of advanced analytic techniques and in most cases, huge computational power. 

On a bigger scale, data analytics is the process of analyzing data sets—using a combination of technologies and techniques— with the goal of finding relationships or patterns between different variables, extracting value, and draw conclusions to help businesses in taking informed decisions. 

Big Data analytics, as a form of advanced analytics, takes this concept into a bigger scale, using more complex techniques such as predictive analysis models, statistical techniques, and the use of advanced analytics hardware.

History of Big Data Analytics

Although Big Data only got mainstream attention in the past decade or so, actually the idea has been around since the 1950s. Although the term ‘Big Data’ hasn’t been coined, businesses already know by then that if they can capture more data and analyze it, they can get more insights and value to apply to their businesses.

Back then, businesses use simple spreadsheet and manually analyze the data to uncover trends and insights—-a practice we still do today—. 

With recent advancements in technology and the invention of advanced analytical techniques, we have access to more speed. Meaning, we can analyze a lot more data in a much faster time frame. 

In the past, data analytics is about finding insights to make future decisions. Now, we can access immediate, real-time insights to make decisions almost immediately. This will only get even faster in the future. 

The actual term Big Data was first used in the 1990s, with the increasing amount of generated data thanks to the popularity of the internet. Doug Laney—back then an analyst at Meta Group consultancy—, was the first to discuss today’s concept of Big Data at greater length, including the concept of 3V’s of Big Data (which we will discuss below) back in 2001. Gartner acquired Meta Group in 2005 (hiring Laney in the process), and popularized Big Data and the 3V’s. 

At the same time, around 2006, the Hadoop project was launched in 2006. The idea behind Hadoop—an Apache open source project— allows the development of clustered platforms that can finally run Big Data applications. 

By 2011, both the technology and the concept have flourished, and Big Data analytics began to be implemented in major organizations, turning Big Data into a major buzzword back then. Big internet companies like Google and Facebook was the early adopters of Big Data analytics, but today, Big Data has been utilized by various industries from financial services to insurers to healthcare providers, among other industries. 

In the past decade, Big Data as a field is still widely discussed, with various new technologies and techniques have been developed to better manage, store, and analyze Big Data. 

More About Big Data

What actually is Big Data? Is it just data with a really big file size? Or just a huge amount of data?

To answer, a data, or dataset is only categorized as Big Data when it possesses at least one of the following characteristics:

High Volume: very big amount of generated data

High Velocity: the data is generated in a very high speed

High Variety: the data consists of many different types, can be both structured and unstructured

These are known as the 3V’s of Big Data. In most cases, Big Data has at least two out of the 3V’s. For example, we get a lot of generated posts on social media every second (high volume), it updates every second (high speed), and the data can come in many different types from photos to unstructured texts to videos (high variety). 

Structured VS Unstructured Data

Big Data consists of both structured and unstructured data (and also semi-structured data, containing characteristics of the two). So, understanding the differences between the two is essential in learning about Big Data analytics.

It’s important, however, to first understand that the case of unstructured data vs structured data doesn’t denote a conflict between the two. Big Data analysts don’t really choose between the two types of data, but rather it will depend on the source of data.

Structured data is always (significantly) easier  for Big Data tools to analyze, but we often don’t have the luxury of a choice if the source of data that want to be analyzed produce unstructured data. 

Structured Data Definition

Structured data, simply put, is any data that already comes in a structured, pre-defined form, usually relational databases. Every row and column is defined, and easily searchable by the Big Data analytics tool. For example, transaction data coming from Point of Sales (POS) system is structured data.

Unstructured Data Definition

Every other data that is not structured is categorized as unstructured data. The data doesn’t come in a predefined structure, and usually stored not in a relational database. Here are some examples of unstructured data:

  • Text data: word documents, spreadsheets, presentations, logs
  • Social Media posts
  • Email, often considered as semi-structured data because it has an internal metadata
  • Files coming from websites: photos, blog posts, etc.
  • Media files from digital photos to mp3s
  • Data generated from satellite, from weather data to military movements
  • Surveillance videos
  • Sensor data (i.e temperature data from your thermostat)

While, again, structured data is always easier to process, today’s newer Big Data tools are making great developments to process unstructured data.

Why Is Big Data Analysis Important?

Today, big data analytics is the competitive advantage for bigger corporations. However,in the near future, it will be a necessity to stay competitive.

One of the key challenges for many businesses is filtering irrelevant data. Almost all of the data we possess is irrelevant. Big Data analytics can help companies derive the actual value from relevant data and use the insights to find opportunities and stay ahead of the competition. 

Today, there are many different possible applications of using BigData, but here are some of the most prominent ones:

  • Finding New Revenue Opportunities

Big Data analytics can give us insights of customer needs, current satisfaction levels, new trends and disruptions, etc. 

This will allow us to develop new products and services to better meet our customers’ needs, find new opportunities to expand our business, and more.

More companies are now using Big Data in new product development. In the future, we can expect it to be the norm.

  • Faster and More Accurate Decision Making

Modern data analytics technologies and techniques have allowed us a much faster analysis process, while at the same time we are now able to analyze data coming from more sources.

Big Data analysis allow businesses to analyze more information in real time, which in turn will allow a more accurate, informed decision making.

  • More Effective Marketing

A key aspect of any marketing strategy is how well we can understand our customers. In theory, the more we know about our customers’ needs, problems, and behavior, the better we can develop marketing tactics that can meet their ‘triggers’.

Big Data analytics provide us with more insights about our customers, by analyzing trends and mentions on social media, finding patterns in online conversations, measuring the effectiveness of each marketing campaign, and so on.

  • Cost Efficiency

As discussed above, Big Data can allow more effective marketing efforts, which will produce a higher marketing ROI. Other benefits of Big Data analytics like more accurate decision making will also produce a more efficient business process.

Also, cloud-based Big Data analytics services bring significant cost efficiency over storing large amount of data in your own server. 

Big Data solutions have been proven to drive down costs in various sectors from retail to healthcare to manufacturing. A survey by McKinsey, for example,suggested that Big Data analytics in the healthcare industry could save up to $450 billion in the US. 

  • Customer Service Excellence

Quite similar to the benefit of more effective marketing strategy discussed above. The better we understand the needs of our customers, the more effective we can deliver our customer support. 

Also, Big Data analytics have enabled the development of AI-based chatbots, which have been proven effective in recent years in delivering better customer service. Again, this will also provide the benefit of cost-efficiency, as a single chatbot can serve more customers in 24/7 than 10 human customer service officers. 

  • Competitive Advantages

Big Data analytics is not only limited in analyzing data related to your customers and your own product/service. It is also an effective tool to analyze your competitors, their approaches, the performances of their marketing campaigns, or any other factors.

Combined with all the other benefits above like better insights of our market and faster decision making, we can develop better competitive advantages with Big Data analytics.

Big Data analysis enable businesses—-through data analysts and other analytics professionals— to gain insights growing amount of structured and unstructured data, including data types that are not analyzed by conventional analytics and business intelligence

The amount of generated data, the speed it generates, and the variety will only grow even bigger in later years. Big Data analytics will be even more important than ever before.

Understanding Big Data Analytics Tools

Since Big Data typically contain both structured and unstructured data, along with semi-structured data, it’s difficult to store and process Big Data in traditional data storage warehouses, that are typically based on relational databases. 

In general, relational databases can’t properly fit unstructured and semi-structured data.

This is why Big Data analytics tools and services tend to use non-relational databases such as NoSQL or Hadoop

Here are some of the most popular Big Data analytics frameworks and platforms available today:

  • Apache Spark: open source Big Data analytics platforms with over 80 high-level operators, enabling the user to perform data analytics across large, clustered frameworks
  • Apache Hive, data warehouse system to store, query, and analyze Hadoop data sets. Open source.
  • Apache Hadoop YARN: a key feature n Hadoop 2, a resource management and job scheduling technology. 
  • Apache Kafka: a distributed streaming platform to replace traditional messaging systems.
  • Mapreduce: a programming approach to allow massive scalability of thousands of Hadoop servers. 

When choosing a Big Data analytics service, make sure to check the platforms/frameworks they use and whether they will fit your analytical needs. 

The Process of Big Data Analytics

There are two main approaches in pre-analyzing Big Data.

First, is to use NoSQL and/or Hadoop systems to process the raw data, essentially ‘structuring’ the data so it can fit a traditional, relational database. Then, the processed data is loaded into a traditional data warehouse. 

Second, is to use Hadoop’s data lake architecture  to ‘receive’ the raw data. With this type of architecture, the data can be analyzed directly in a Hadoop clustered system, or by using various data processing platforms like Apache Spark.  This is a relatively more common approach nowadays, but keep in mind that the data stored with this approach still need to be partitioned and organized properly. 

Once the data is processed and ready, we can use advanced analytics software to analyze the data. Common applications here include:

  • Machine learning: using AI software to ‘learn’ the data set, mainly to predict future outcome
  • Deep learning: more ‘advanced’ version of machine learning, utilizing artificial neural network technology
  • Data mining: digging down the data to find patterns and relationships between two or more variables
  • Predictive analytics: building models to predict future outcomes

Other statistical analysis tools, visualization tools, and other analytics tools used in traditional business intelligence can also be applied in combination with other analysis. Queries in big data applications are mainly written in Apache MapReduce. 

Big Data Analytics Challenges

While Big Data analysis certainly opens up many opportunities and offers many different benefits, there are still some challenges faced:

  • Talent Dependency

Due to the increasing volume of data being produced every single minute, and with more businesses realizing the importance of Big Data analysis, the demand for data scientists and analysts is obviously on the rise.

Even when a business uses a cloud-based Big Data analytics service, it’s still beneficial to hire an in-house data scientist that understood big data analysis.

However, there is currently an acute shortage of these professionals, especially when compared to the increasing volume of generated data.

  • Filtering and Utilizing Useful Insights

Even after the Big Data analysis has produced useful insights from the data analytics process, it’s still necessary to manually filter out these insights, find meaningful ones, and make decisions or delegate based on the insights.

Skilled managers and executives that are familiar with the concept of Big Data are necessary for this process, and the required skill set is still fairly rare nowadays.

  • Data Security and Privacy

The Big Data analytics platforms and tools essentially allows third-party, disparate platforms to access our data. While most of them are well respected in terms of security and privacy, there is always the risk of vulnerability. 

The bigger the amount of data, and the more valuable the analytics results, the more concerns we have regarding privacy and security. 

  • Uncertainty of Data Processing and Management

With the rise of Big Data’s popularity and the advancements in new technologies especially AI-related, more companies are offering innovative approaches in data management, processing, and analytics. 

While this can be promising for the future as we might get even more processing power to analyze Big Data, today, we are faced with uncertainty and confusion of which technology will be best suited to our needs, and whether they will pose unnecessary problems and risks. 

  • Incorrect Data Labeling

As the datasets becoming even bigger with more diversity, it’s very important to correctly label all the different data variables before we incorporate them into an analytics tools. 

No matter how good the analytics platform is, the performance is dictated by the quality of the inputted data.  Preparing the data is an integral, but also an increasingly difficult process.

With the amount and variety of data we have these days—and how they will continue growing in the future—, it can easily overwhelm even the most experienced data analyst. 

  • Data Storage

As briefly mentioned above, standard data warehouses still can’t consistently store unstructured and semi-structured data from various sources, which can lead to data errors and conflicting logics, ultimately resulting in missing data.

The storage of Big Data is still a challenging process, which will also be related to the increasing privacy and security risks. 

End Words

Big Data analysis can offer significant competitive advantage for any businesses, while at the same time can provide more cost efficiency across all the business processes and the ability for more accurate and faster decision making.

At the same time, Big Data analytics is getting more accessible and affordable, with the availability of various SaaS-based services that offer Big Data analytics at a competitive price. 

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