The Potential of Big Data in Technological Age
Here's where we're going if you want to jump ahead:
- What is Data
- What is Big Data
- Types of Big Data
- Characteristics of Big Data
- Big Data Use Cases
- How to use Big Data
What Is Data?
Before moving on to Big Data, it is very important to know “What is data?”
Data is composed of individual units of information and can exist in different forms such as picture, sound, text, video, or bytes in electronic memory. If the data is not put into context, it is of no use to a human or computer, and therefore it is accumulated and processed for analysis. For the digital world, data can be defined as the series of bits, on which operations are performed by a computer, and which is stored and transmitted in the form of electrical signals. The CPU processes the raw data, using logical operations, and produces useful data as an output.
What Is Big Data?
Big Data is simply data with a huge volume that grows exponentially with time. It refers to the vast quantity of information that is digitized, accumulated, sorted, analyzed and modeled for better problem-solving and decision-making. Nowadays, big data is considered as capital for businesses. Recent technological shifts have reduced the cost of data storage, making it cheaper and easier to deal with big data than ever before.
These data sets are too large and complex that it is nearly impossible to sort and analyze them using traditional methods and therefore you need the most competent big data developer for your business. Most of the world’s fastest-growing companies are constantly analyzing data to generate more value and bring more efficiency in their business process. However, still, there are some companies that are not aware of “What is Big Data” and unfortunately, are unable to get benefit from this fast-growing business trend.
Types Of Big Data
There are three major types of big data:
1. Structured Data
Structured data is defined as the data that can be stored, accessed and processed in the form of a fixed format. An example of structured data is a student’s mark sheet with a proper format.
2. Unstructured Data
Any data with unknown form or the structure is defined as unstructured data. A common example of unstructured data is a data source containing a combination of text files, graphs, images, videos etc
3. Semi-structured Data
Semi-structured data is the type of data which contains both forms (structured and unstructured). Example of semi-structured data can be the data represented in an XML file.
Characteristics Of Big Data
There are three main characteristics of Big Data, also called The “Three Vs” of Big Data, which must be considered while working with it.
The term “Big Data” is self explanatory and gives us the idea that the size of data is very important when working with big data. Whether a specific data can be considered as a Big Data or not, is dependent upon the volume of data.
The term 'velocity' refers to the speed of generation of data. The potential in a data is determined by how fast the data is generated and processed to meet the demands.
Variety refers to the different forms of data in which it is originally present.
Big Data Use Cases
So how are organizations using big data today? Here are some of the most popular big data use cases.
It provides you all of the required information related to your target audience so that you can know their behavior and buying patterns.
Big data helps you to analyze consumer demands so that you can design and develop your products accordingly. These products capture more market share and help to generate more revenue.
Pricing Analytics and Optimization
Big data helps retailers to discover an accurate pricing strategy for their products and areas where profits may be leaking.
On the theme of criminal activity, organizations are using big data analytics to help them thwart cyber attackers and hackers.
By analyzing indications of potential issues in the pre-development stage, through big data, organizations can deploy maintenance more cost-effectively and maximize a product’s lifetime.
The ability of big data to teach machine learning models is helping many high-tech organizations to teach machines instead of programming them.
With big data, you can analyze your production, customer returns and feedback, and other factors to minimize outages.
Big data can help you innovate by analyzing interrelation between humans, institutions, and processes and then discovering new ways to use those insights.
Going Big Data? You need an experienced big data engineer!
How To Use Big Data
After learning what is big data and big data use cases, the first question which arises in our mind is “how to use big data”
Before organizations can make use of big data, they must analyze its flow among a multitude of systems, sources, and users. The five main steps to use big data, that must be involved in your big data strategy, are listed below:
1. Set up a big data strategy
Big data strategy is carefully designed so that you can improve the way you acquire, store, process and use data for your business purpose. This stage sets the base for the success of a business, with the help of abundance of data. When planning a strategy, an organization must consider its current position and future goals in order to work on realistic grounds and to achieve expected results.
2. Know the sources of big data
After setting up the strategy and defining the goals, an organization must analyze its sources of data which are relevant and useful for it. The most common sources of data are digital wearables, smart cars, industrial equipment, medical devices, social media, government institutions, data lakes, cloud data sources, suppliers and customers. After receiving the big data from different sources, you can decide which data to keep and analyze.
3. Integrate the data
Big data consists of data coming from many disparate applications and sources. Traditional data integration mechanisms, such as ETL (extract, transform, and load) are not always compatible. Nowadays, organizations need new technologies to analyze big data sets at terabyte, or even petabyte, scale. During this stage, you must ensure that the data is available in a form that your business analysts can work with. For this you need to process the data accordingly.
4. Access, manage and store big data
Accessing the massive amounts of big data requires modern computing systems in order to provide the required speed, power and flexibility. Along with integrating the data, organizations also require methods for its reliable access, ensuring its quality, and preparing it for analytics. As big data requires storage, you can have storage on premises, in the cloud, or on both. This data can be stored in any form and you can process it using necessary process engines.
5. Analyze Big Data
This is the stage when your investment on big data pays off, but only when you analyze it properly and according to your organization’s need. After analyzing it, you can have data in pictorial forms such as charts and graphs that can help you to make accurate and on-point decisions. Furthermore, It gives you more clarity regarding your existing business process, discovers the loop holes and guides you to devise a proper business strategy. Big data analytics is how companies gain value and useful information from the data. Increasingly, big data contributes towards today’s advanced analytics endeavors such as artificial intelligence.
In a world where large number of consultancies are offering big data services, it is very important to know the importance of Big Data and the value it has delivered to the business across the world. According to a report by ScienceSoft, the top 3 big data use cases are data warehouse optimization, predictive maintenance and customer analytics. Among the big data users, telecommunication companies top the list, followed by financial services and healthcare industry. This report also states that the biggest value big data delivers are decreased expenses and newly created avenues for innovation. Furthermore, it mentions that in the year 2018, 97% of companies indicated that they were investing in big data and advanced analytics , improved customer service and achieving cost efficiency were their top 3 priorities for investing into it. This sums up how important big data is and how necessary it is for your organization to make use of this opportunity. If you think the same, you need to hire a competent and cost efficient big data developer!