An Ultimate Guide to Big Data
An overview of what is Big Data?
Big Data and the collection, processing, and storage of a substantial amount of user-generated data has been around since the Internet was invented with the first data centres and development of relational databases between the 1960s and ‘70s.
Today, in a digitally driven world, numerous enterprises are after Big Data and the valuable insights that can be derived from it.
Big Data can simply be defined as massive amounts of data which cannot be processed effectively with conventional applications. The processing of Big Data starts with raw data which is not aggregated and often impossible to store on one single computer.
Not only does Big Data inundate businesses day-to-day, but it is something which can be used for analytic purposes to gain insights which can lead to better decisions and strategic business moves.
Big data can be described as high-volume, high-velocity, and high-variety information assets. It demands a cost-effective and innovative form of information processing.
Through this, businesses can gain enhanced insights, improve business decisions, and make process automation possible.
The existing amount of digital data is growing at a ferocious rate and doubling almost every two years. With more data being created, it is imperative to know the basics of the field and how it can be used efficiently as we move deeper into our Digital Age.
Big Data can be characterized by the three V’s namely volume, variety, and velocity and it is imperative to have a more in-depth look at these to further understand Big Data.
This refers to the quantity of both generated and stored data. The more data there is at the disposal of a prediction model, the more accurate the prediction will be.
It should be noted that the size of the data determines the value thereof in terms of the amount of insight that it can offer on a subject matter.
This refers to the type and the nature of the data, for example whether the data is text or an image, amidst others.
This refers to the speed at which data is generated from the source, which is normally end-user, as well as the speed at which it is processed and stored.
Although these are three fundamental characteristics and the most predominant ascribed to Big Data, there are a few more features tat have been added as technology has progressed namely:
- Fine-Grained as well as Uniquely Lexical
- Extensional, and numerous others.
In understanding the characteristics of Big Data, it is also important to look at the three primary types of Big Data namely Structured, Unstructured, and Semi-Structured Data.
Big Data is predominantly measured in terms of terabytes, but it can also cross over petabytes.
This includes all data which can be stored in a tabular column such as relational databases.
This type of data can be stored in a spreadsheet and cannot fit into tabular databases such as audio, video and other types of data which form a large part of Big Data.
This type of data does not conform with the model of structured data and although it can be searched in a similar way, it does not offer the ease with which it can be done on structured data.
Semi-structured Data is made up of both structured and unstructured data and although the data sets include a decent structure, it may still prove difficult to sort or process such data due to certain constraints. This type of data includes XML data, JSON Files, and more.
Big Data Main Components
Some of the main components of Big Data include:
- The techniques used for analysing data including machine learning and natural language processing, otherwise known as NLP
- Business intelligence, cloud computing along with databases which are used to both process and store data which has been collected, and
- Data visualization which is done through graphs, charts, and other visual representations.
An essential feature of a Big Data system is the real-time and near-real-time handling of data. One of the major challenges is latency in the connection and there is substantial focus placed on attempts to reduce latency whenever and wherever it is present.
Big Data and its uses or applications
Big Data is used in a great variety of enterprises and businesses, and their activities, few of which include:
- Product Development – Predictive models are built for new products and services based on key attributes of those past and current along with the modelling of the relationship between attributes and commercial success of offerings.
- Predictive Maintenance – By making use of structured data to analyse existing indications of potential issues before problems occur to allow for maintenance before mechanical failures.
- Customer Experience – To gain insight by making use of data from social media, web visits, call logs, and other sources to improve the interaction experience from customers and to maximize the value which is delivered.
- Fraud and Compliance – Big Data can help businesses identify patterns in data that may indicate fraud. It also allows for large volumes of information to be aggregated and for regulatory reporting to be done faster and more efficient.
- Machine Learning – machines can now be taught instead of conventional programming and Big Data and its availability makes it possible to train machine learning models.
- Operational Efficiency – Big Data allows for the analysing and assessing of production along with customer feedback, returns, and other factors. This allows for the reduction of outages and to anticipate any future demands.
- Drive innovation – Big Data helps businesses with innovations by allowing for the study of interdependencies that exist among humans, institutions, and entities. This can be processed and new ways to use the insights can be determined.
Big Data Analytics versus Data Science
What is Data Science?
To highlight the difference between Big Data and Data Science, it is necessary to understand what Data Science is, seeing that a lot of factors and definitions pertaining to Big Data have already been discussed to gain insight into what it is and what it can do.
Data Science can be ascribed to a field which comprises everything which relates to the cleansing, preparation, and analysis of data.
It is a combination of the following:
- The capturing of data in ingenious ways
- The ability to view things from a different perspective, and
- The activity involved with cleansing, preparing, and the alignment of data.
It is holistic, or umbrella, techniques which are used in the process of extracting both insights and information from data.
The difference between Big Data Analytics and Data Science consists of the following factors:
- Big Data Analytics processes only structured data while Data Science can process all types of data.
- Big Data Analytics makes use of statistics and data modelling while Data Science incorporates Hadoop, coding, and Machine Learning.
- The domain expanse of Big Data Analytics is relatively smaller when compared to Data Science which is massive.
- Where Big Data does not require new ideas, Data Science is dependent on them.
How is Big Data Processed?
To be able to process Big Data, Cloud and physical machines are needed and with the advent of technology, Cloud Computing as well as Artificial Intelligence can also be included within the ambit of Big Data Processing.
This allows for the reduction in manual inputs and allows for automation to take over. Data Analytics is the set of quantitative along with qualitative approaches used to derive valuable insights generated by data.
There are numerous processes through which data can be extracted as well as categorizing it so that various patterns, relations, and connections can be analysed and subsequently through which these insights can be gathered from.
Most enterprises today are data-driven which results in the deployment of data-driven approaches which allows for more data to be collected relating to various business areas including customers, markets, and business processes.
Challenges concerned with Big Data
The most substantial challenge involved with Big Data is that there must be efficiency along with real-time handling of the inflow of great amounts of data at any moment. This is a demanding task which can easily overwhelm a single server or a cluster of servers.
This can be countered by ensuring that there are numerous, albeit hundreds, of servers or server clusters which can work collaboratively to process data quickly and efficiently through using certain technologies.
Another challenge associated with Big Data is cost. The servers and the amount thereof, needed to ensure efficiency and speed, are capital intensive.
Single organizations generally purchase the necessary hardware for all computing as well as storage whereas other organizations buy processing time and memory which is used from the previous company so that they can use it for their own purpose, which lowers the cost.
An introduction to Big Data Analytics
Big Data Analytics allows for a range of diagnostic questions to be answered regarding business needs. It allows businesses to derive actionable results in addition to providing more data and sophisticated analytics.
It also allows businesses and their teams to explore deeper diagnostic questions which may not have been thought of otherwise and reveals a new level of insight.
Big Data Analytics also helps to identify the necessary steps which can be taken to improve business performance.
However, Big Data Analytics is quite complex when it is deployed during Big Data applications, but it is needed as data is generated at extremely high speeds. It is necessary as it helps to make sense of massive volumes of data and to:
- Transform, and
- Model the data according to the requirements of a business along with identifying patterns and the ability to draw conclusions from it.
What are the different types of Big Data Analytics?
The focal point is on analysis which is based on rules and recommendations along with the prescription of a certain analytical path for the business.
This ensures that there is a predicted path for future course of action. In answering questions of “How” and “Why”, it reveals specific patterns in detecting when outcomes will occur.
These analytics build on the diagnostic analytics in identifying such patterns to see what is going to happen.
This is based on incoming data and to mine this type of data, analytics are deployed, and descriptions are formed based on the data.
When this type of analysis is done, Big Data Analytics is applied to answer diagnostic questions over how and why something happened.
This involves having a look at the past and determining why a certain event occurred. This revolves around making use of a dashboard and in conjunction with Big Data helps to:
- Eliminate analytic blind spots brought by the digital age, and
- Deliver insights through the “How” and “Why” questions which pinpoint actions that need to be taken.
How are Business Insights derived with the help of Big Data Analytics?
Big Data Analytics comprises of various tools which can be deployed successfully to not only parse data, but to derive valuable insights therefrom.
Due to challenges faced with computational and data-handling, the tools deployed need to be able to work with certain kinds of data.
Thankfully, due to Big Data, analytics were changed forever as it changed the requirements for extracting meaning from business data.
What are the different Databases used for Big Data Analytics?
These databases are used specifically when working with unstructured data as it cannot be stored in a conventional, regular tabular form. Two of the most important unstructured data types are JSON and XML.
JSON allows for tasks to be written in the application layer which subsequently allows for enhanced cross-platform functionalities.
There are numerous processing engines where Big Data is concerned such as Hadoop and MemSQL.
MemSQL is a relational database which handles transactions as well as real-time analytics at a scale. It is a distributed database which can be accessed through standard SQL drivers.
MemSQL supports ANSI SQL syntax such as the following:
- Filters, and
- Analytical capabilities such as aggregates, group by, and windowing functions.
In modern data processing ecosystems such as orchestration platforms, MemSQL maintains a broad compatibility with common technologies and it features a distinct data ingestion technology by the name of MemSQL pipelines.
MemSQL pipelines can stream substantial amounts of data at a high throughput directly into the specified database.
Additional features to MemSQL include:
- Programmatic Pipelines
- Exactly Once Semantics
- Pipelines for Data Lakes
- Streaming Inputs
- Database Inputs, and
- Storage Inputs.
Hadoop Hybrid: Data Storage and Processing
Hadoop can be used for data storage and processing systems with the storage being the Hadoop Distributed File System and processing being MapReduce. Due to a need for such processing engines, Hadoop is more readily and increasingly accepted.
What is the importance of Data Mining?
Where cost-effectiveness and increased revenues are concerned, Data Mining is being used more often as it is a fundamental step in the process related to Data Analytics.
This step is comprised of Extract, Transform, and Load to obtain the relevant data and move it into the correct data warehouses. Data mining tackles the task associated with both storing and managing data which is based in multidimensional databases.
Within data mining there have been some recent phenomena which has been based on contextual analysing of Big Data sets with the purpose of discovering the relationship which exists between separate data items.
The objective with this is to use one data set for various purposes by various users. Data Mining is also tasked with the presentation of analysed data in a simple but effective way.
Sectors that make use of Big Data Analytics
The top industries that are currently deploying Big Data Analytics include:
- Retail – to understand what customers are purchasing and to offer products and services which have been tailor-made for such customers.
- Technology – technology companies are using it to find out how customers interact with websites or apps and through that, to gather key information to optimize sales, customer service, improve customer satisfaction, and more.
- Healthcare – this helps healthcare personnel to diagnose their patients’ health through various tests. This is then run through computers to indicate tell-tale signs of anomalies and maladies, amidst other patterns.
- Manufacturing – manufactures can improve yield along with reduce the time that it takes to market, enhance quality of products, optimize supply chains and logistics, and to build prototypes before products launch to realize implications.
- Energy – from the discovery of oil sources to finding out what prices will be, what the output should be, and whether an oil well will be profitable, Big Data Analytics area heavily used by this industry.
How IoT and Big Data are connected
Bit Data and IoT are closely related as Big Data is the very backbone of a true IoT project. Big Data connects the parts of an IoT network and subsequently enables automation.
Once connected technology and the business process has been integrated, the system can start producing and access to Big Data is provided. In using Big Data with IoT, businesses can do the following:
- Sell or share IoT data
- Reuse IoT data, and/or
- Redesign products, services, and processes.
Final Thoughts – Why big data should be incorporated into all enterprises
The key factor is that despite how much data a business has but how the collected data is utilized. Each business will use its data in its own way but the more efficiently data is used, the more potential the business has in which it can grow.
Big data does provide for cost-effective solutions where Hadoop and Cloud-based analytics are concerned as the tools not only save on costs, but they also identify more ways in which business can be done.
There are numerous time reductions depending on the tools used and Big Data also allows for new product development when trends of customer needs and satisfaction can be uncovered through analytics.
Big Data helps businesses understand the market conditions better and businesses can control their online reputation better through using tools which can do sentiment analysis.
Big Data and its uses are becoming more common these days in the race for businesses to outperform their peers and to meet the needs of customers more efficiently, thus ensuring customer satisfaction.
There are numerous benefits which can be derived from analysing Big Data and in the end, when used correctly, can ensure that business decisions are made more informatively which leads to more business improvement and ultimately, business growth and success.