Big data analytics: What it is and how it works?
What is Big Data?
Big Data is a field that deals with analyzing, extracting information and dealing with set of data that are too large to be treated with traditional data processing methods. In simple terms, big data is a large or huge volume of data that may be structured or unstructured and is an important part of any business on day-to-day basis. Historically, the quantum of data was difficult to be stored, analyzed and accessed and in 2000’s Doug Laney, an industry expert came up with the idea of big data characterized with 3Vs:
Volume: Data is collected from varied sources such as smart (IoT) devices, social media, business transactions, industries equipment and more; hence, its size and quantity are large. Big data platforms like Hadoop and data lakes are helpful in managing the bulk of data.
Velocity: It is the speed at which the data is generated and processed and with this growth, it is important to manage the data in real time. The big data are produced more continually, and the kind of velocity generated are frequency of generation of data and frequency of handling, publishing and recording the data.
Variety: Data cannot be fixed and saved in a set format. It has different types and nature. The data comes in different formats — structured, non-structured, numeric or in the form of emails, videos, audios and many more.
There are few more characteristics of big data:
Variability: Data flows are unpredictable, and it keeps changing.
Veracity: As data comes from varied sources, it is transformed and processed into one using advanced tools.
Exhaustive: The data must be exhausted or recorded.
Relational: Different data has common fields, that can be conjoined from different data sets.
Scalability: Data can expand rapidly.
Types of Big Data
Big Data can be found into three different forms:
Structured Data: The data can be easily stored, processed and accessed into a fixed format can be termed as structured data. With advancement in computer science and technology, success has been achieved in managing such data.
Unstructured Data: The data with any unknown form, format and structure is the unstructured data. Along with huge size, dealing with this kind of data involve many challenges while processing and deriving the same.
Semi-structured Data: The data that has both forms of data — structured and unstructured is called semi-structured data. This kind of data has structure in form but is not defined.
Examples of Big Data
· Education industry is flooded with quantity of data related to students, faculty, courses and results.
· Government generates huge amount of data as it keeps a track on its citizens, development, resources, requirements, surveys and many more.
· With digital gadgets and social media, plenty of data is generated every day.
· Healthcare industry generate quantum of data everyday with patients, medicines and doctors’ information.
· With immense transactions every day, the banking industry creates plenty of data.
· Route planning, traffic control, level of safety, big data is used in smooth flow of transportation industry.
· Weather reports, sensors and satellites changes are sources of producing and contributing to big data.
How Big Data works?
Before Big Data to work for them, the organizations must consider certain factors how it will flow among different users, owners and sources. They can take the following steps to make the Big Data work for them:
1. Setting a data strategy: data strategy should be planned and designed to improve the way the data is collected, stored, shared and managed. While designing the strategy, the existing and future goals of the business must be considered in advance.
2. Knowing the sourcing of Big Data:
Streaming data is available through Internet of Things (IoT) and connected to various smart devices, smart cars, industrial equipment and many more.
Social media data is available through different social media platforms in the form of images, videos, texts, sales and marketing functions.
Open-to-public data is available through varied pubic platforms that can be accessed by any user.
Other data can be fetched from data lakes, cloud sources or consumers and suppliers.
3. Access and manage data:The power, speed and flexibility required to easily and quickly access large amount of data is available through computing systems. With reliable access, organizations need methods of integration of data and ensure its quality.
4. Analyze Data: The high-performance technologies help organizations use and choose their big data for analyzes.
5. Make sensible data-driven decisions: Organizations must take well-managed, organized and trusted decisions.
Advantages of Big Data
· Big Data helps cut costs.
· It increases efficiency of businesses.
· It helps organizations compete with big businesses.
· It helps in improving pricing and profits.
· It helps increase sales.
· It helps in hiring right resources.
· Varied, multiple and unlimited information can be carried in a single platform.
· It helps in understanding, targeting and reaching right consumers.
Disadvantages of Big Data:
· It requires talent and experts to handle it.
· Data quality issues need to be addressed on time-to-time basis.
· It requires attention for if the data analytics are complying with government regulations.
· Cyber security could be a concern in big data analyzes.
· With rapid and cultural change, it must be managed at par with the changes.
· Big data is not useful in short run.
· The data can be misleading at times.
· It may require a lot of hardware space and increase its needs.
Entrada offers consulting, implementation, and support services in Big Data Analytics.