You probably know about the Apollo Guidance Computer, the computer used for the Apollo 11 Mission, which helped launch the astronauts to the Moon and back. But did you know that it had just 72KB as read-only memory (ROM)?
At that time (1969), being able to store 72KB of data was an incredible feat. Fast forward 50 years, humans generate more than 2.5 quintillion bytes of data per day. (One quintillion is 1,000,000 trillion)
Managing such colossal amounts of data is nearly impossible for traditional computers, let alone for humans. Thus enter big data analytics, a revolutionary method for organising and analysing massive sets of data.
What Is Big Data Analytics?
Big data analytics refers to the usage of advanced analytical methods to collect, organise and analyse vast and diverse sets of data. Such datasets may include three types of data:
- Structured data – It has a fixed format and will generally be numeric in nature. They are grouped into rows and columns and can be quantitative data such as age or mobile numbers.
- Unstructured data – It is unorganized and doesn’t have a predefined format. This data can be anything, for example, it can be books (text), images, or videos.
- Semi-structured data – It can contain both structured and unstructured data.
Big data can be considered as complex and large data sets that may stretch up to terabytes or zettabytes. This humongous volume of data cannot be captured, processed, or analyzed using traditional relational databases or applications, and this is where big data analytics comes into play.
Big data comes from multiple sources, for example, from IoT devices, log files, social media, and transactional applications – all of which may be captured in real-time and grow exponentially in size with time.
And its applications are endless. Using big data analytics, researchers can predict the possibilities of terrorist attack occurrences and even determine the Facebook ads that you are more likely to click on.
Four Types of Big Data Analytics
Big data analytics helps in determining the “what, why, if and how” of events.
1. What Happened – Descriptive Analytics
It is the most basic form of analytics by which historical data is analysed and interpreted to better understand the changes that occurred. Almost every business intelligence tool relies on descriptive analytics and can be considered as the starting point of your analytics strategy.
Techniques like data mining and data aggregation are used for this type of analytics and help businesses understand what has happened, rather than make guesses. One simple example is the preparation of monthly profit and loss statements.
2. Why Did It Happen – Diagnostic Analytics
Diagnostic analytics is more sophisticated than descriptive and will allow analysts to identify the root causes of events. This analytics method primarily uses techniques like data mining, correlation, data discovery and drill-down, to determine what factors or circumstances led to an outcome.
For example, diagnostic analytics will help you understand why sales increased or decreased for a particular month. But just like descriptive analytics, diagnostic analytics too looks at historical data.
3. What Is Likely to Happen – Predictive Analytics
Predictive analytics determines what is likely to happen in the future and is all about forecasting. However, it doesn’t predict an event per se. Instead, it forecasts the probability of an event occurring.
One use case of predictive analytics is sentiment analysis, which collects and analyses data of an individual from social media posts and interactions (existing data) and predicts whether the individual will be positive or negative to a particular subject.
4. How to Make It Happen – Prescriptive Analytics
Prescriptive analytics is the most advanced level of big data analytics that allows you to determine how to make certain things happen by identifying trends, causation and correlations.
This means prescriptive analytics will look into what has happened, why it has happened and a number of what might happen scenarios to determine the best actions to take.
A simple example of this is the Google Maps app that suggests you the best routes to take by taking into consideration the distance and real-time traffic conditions.
What Are the 3 Vs of Big Data?
Variety in big data refers to structured, unstructured and semi-structured data, collected from multiple sources. In the past, data could be obtained only in the form of spreadsheets and databases; but today, any kind of unstructured data such as images, audios or videos can be collected.
Velocity in big data refers to the rate at which data is generated and collected. The flow of data is enormous and continuous and how fast the data is collected and processed determines its usability – the faster, the better. Every second, around 6000 messages are tweeted on Twitter.
The term big data itself signifies that the data collected is enormous. The volume of the data collected plays a crucial role in determining whether it will be valuable. And the volume of data determines whether it will be considered big data or not. Facebook alone generates four petabytes of data per day.
Why Is Big Data Analytics Important?
Incorporation of newer technologies such as artificial intelligence (AI), smartphones, social media networks and the Internet of Things (IoT) means that businesses or researchers need to deal with high volumes of data in various forms and from multiple sources.
With big data analytics, businesses, researchers, and analysts can make fast and accurate decisions, which would have been previously impossible. More precisely, big data analytics helps companies in the following ways.
- It allows companies to offer better customer service.
- It gives businesses a competitive edge over rivals.
- It increases effectiveness and reduces the costs of marketing.
- It empowers businesses to gauge customer satisfaction and needs and release products accordingly.
- It allows app creators to find the right set of target audiences and take proactive action to decrease churn rates.
- It will enable human resource departments to find the right talent quickly by providing comprehensive data, collected from multiple sources.
- It helps insurance agencies with fraud detection.
- It allows the banking sector to determine the credit risks associated with individuals.
How Does Big Data Analytics Work?
Big data analytics companies will require stable storage infrastructures to deal with a high volume of data. For this, a single server won’t do; instead, there must be clusters of hundreds or thousands of machines.
For this purpose, technologies such as Hadoop, Apache Spark, NoSQL databases, and data lakes are used. Once the needed infrastructure is set up, big data will go through four significant stages as follows.
1. Data Collection
The process of data collection will vary across different organisations. Both structured and unstructured data will be collected from multiple sources. For example, data can be obtained from mobile apps or even from IoT devices.
Some of the data collected will be stored in data warehouses, where business intelligence tools can access the data. Unstructured or raw data which is too complex to be stored in warehouses will be stored in data lakes instead.
2. Data Processing
Once the data is accumulated, it must be organised to be utilised. One method to do this is by implementing batch processing, which looks at large blocks of data over time. This method is ideal if there is a longer turnaround time between the collection and analysis of data.
Another way to do this is by stream processing, which looks at small blocks of data at a time, significantly reducing the time between collection and analysis of data. However, stream processing can be expensive and a complicated process as well.
3. Data Cleaning
The data collected may contain irrelevant or duplicated data, which can result in flawed insights. For that reason, any data collected is cleansed of redundancies and errors, and the entire data set is formatted.
4. Data Analysis
Once the data is cleansed, it is analysed using one or more analytics methods previously mentioned, which include:
- Descriptive Analytics
- Diagnostic Analysis
- Predictive Analysis
- Prescriptive Analysis
5. Reporting and Data Visualization
Once the data is analysed, you’ll be left with just numbers that may be difficult to understand or visualize, unless converted into reports. Reports help in understanding the effectiveness and return on investment (ROI) of a new venture, product, or a marketing campaign, for instance.
Similarly, the data analysed may contain massive amounts of information, which can be made more comprehensible with the help of data visualization. Data visualization is the process of graphically representing information and data, in the form of graphs, charts or maps.
As humans are attracted to colourful visual elements than bland tables, using data visualization is a faster way to convey information, all while keeping an eye on the message. It is almost like storytelling and explaining the journey from point A to point B.
Benefits and Applications of Big Data Analytics
Big data analytics helps the banking sector with early fraud detection, credit risk reporting and anti-money laundering. For example, the Securities Exchange Commission (SEC) uses big data to monitor the market and detect illegal trading activities.
The healthcare industry generates a substantial amount of data and is used to deliver personalized medicine and identify patterns such as side effects of drugs.
With the incorporation of wearable devices in the industry, data is being generated at an exponential rate, far beyond the comprehension of traditional computing. Using geographical and historical data sets, predictive analytics makes it possible to predict diseases that will affect specific locations.
Big data analytics is extensively used by both public and private sectors for route optimization and controlling traffic. For example, Uber uses big data to track and analyse the services that the users avail the most. Uber also uses it to change cab fares, depending on the demand and supply of its ride services.
Retailers like Walmart use big data and data mining to deliver personalized product recommendations to their customers. They also use Wi-Fi technology to track the location of customers inside the store and determine the aisles that users visit the most.
They also monitor what customers are talking about their brand in social media networks, and tweak their marketing strategies accordingly.
In the manufacturing industry, big data can be used for supply planning and to test and simulate new production processes. It will also allow companies to increase efficiency by monitoring the quality of products and also improve sales by studying customer needs.
OTT platforms like Netflix collect user data to determine the likes and dislikes of its users and to deliver personalized content recommendations – all made possible by big data analytics.
These platforms take into account multiple data sets such as search history, watch time, how often a user pauses or stops the movie, and ratings.
Big data analytics allows marketers to get a 360-degree view of their audiences. They can determine which content is more effective at a particular stage of a sales cycle and gain actionable insights to enhance customer acquisition strategies.
Also, contextual marketing, a method by which targeted ads are served based on a user’s recent search history, is made possible with big data analytics.
Data Is Knowledge
Data is knowledge, and knowledge is power. With the help of big data analytics, organisations can make data-driven decisions and use it, for instance, to understand why some customers behaved in a particular way, and some didn’t.
The usefulness of big data depends on the way it is collected, processed and analysed. When rightly done, businesses will have a competitive advantage over their peers and will be able to effectively pinpoint the needs and wants of their customers.
Big data analytics also allows companies to improve the quality and efficiency of their products and foresee demands, to streamline their supply chain processes accordingly. Powered by data, businesses can make precise decisions, which will benefit the organisation as a whole.