Big data comprises large quantities of data collected at high speeds from new data sources. These collections of data are so large in size that many of the traditional data processing tools are not able to process them. This data can come from various sources such as message exchanges, photo uploads, information storage, etc. The New York stock exchange is a prime example of big data as it produces over 1 TB of new data every day.
The main characteristics of big data, which are also known as the three V’s are:
Volume
With big amounts of data, the data being processed is mostly low density and lacks structure.This data can at times be of unknown values; eg. Twitter data streams, clickstreams from a website or mobile application. In some cases, this can accumulate hundreds of terabytes of data.
Velocity
This refers to the fast rate at which data travels, and is collected and worked upon. Usually the high-velocity data streams directly into memory, rather than being written to disk.
Variety
This refers to the many different kinds of data that is available. Traditionally data was structured and easy to fit into a database.
Now with the evolution of big data, data comes in various unstructured and semi-structured types like images, videos, audios, texts, etc. requiring extra processing to derive meaning and fit into a database.
How Data Analytics is been used?
Big Data and data analytics have their roots in database management systems. Database administration during the time mainly relied on techniques like storage, extraction, and optimization techniques employed in Relational Database Management Systems. Database management and database warehousing were the early phases of Big Data evolution. Modern data analytics developed as a result of the database management system’s evolution. Techniques including database queries, database processing, and reporting tools were used at the time.
Many data analytics companies place a premium on semi-structured and unstructured data. The ability to obtain critical information from mobile devices has opened up a whole new universe of possibilities. The biometric data collected by IoT devices dominated the third phase of Big Data. Companies can collect health-related data using devices like wearable activity trackers. Along with user location monitoring, they can evaluate a lot of new important data. The data generation has risen to a new level as a result of these internet-based sensor gadgets. Sensors are being integrated into a wide range of equipment. To manage inventory, everything from everyday goods like washing machines and refrigerators to automobiles, trucks, and even warehouses is tracked. The applications for these data are limitless. The best thing is that we’ve just started extracting and analysing data from various sources.
Big data is used in consumer behaviour marketing to examine data points along a customer’s journey from discovery to purchase, arming marketers with the tools and information they need to make better decisions.
Predictive behaviour modelling may provide modern marketers with a wealth of information to help them maximise their strategy efforts. This regression model goes beyond evaluating past data to create informed projections about what will happen in the future using mathematics and statistical studies. When you back up statistics with fact-based data, you’ll always get better outcomes than depending purely on intuition, and you’ll be able to drive a more effective plan.
Here are four strategic focus areas where predictive analytics can help improve a company’s Return on Investment :
Personalized Marketing
Any marketing plan relies on accurately delivering a message to your customers at the right time. With the recent surge in e-commerce sales, it’s more important than ever to reach out to your clients ahead of your competition.
Marketers may better target groups with individualised marketing techniques by segmenting the market into particular subgroups based on behavioural similarity, geographic location, or other variables. Focusing customers on the correct product and positioning becomes easy with this knowledge. Based on prior purchase habits, segmentation can also show the most profitable groupings.
Customized suggestions based on a user’s watch history, emphasising things a customer may be interested in inside banner adverts, and recommendations of items “you may also like” when purchasing on a company’s product website are all instances of personalised marketing.
Resource Allocation
It is critical to have correctly allocated resources in place in order to achieve your organization’s goals. A corporation can better foresee and categorise where resources will need to be deployed with predictive analytics in place.
It might be difficult for an individual to handle big data sets that are tracked against objectives in this procedure. By incorporating an ERP system into current planning processes, these data sets may be better organised for easier examination. ERP systems also make it easy to see how far you’ve come in terms of attaining your goals and objectives over time.
Demand Pricing
Predictive analytics may also be used to help create better demand pricing. Marketers can better understand the impact of price changes on demand by analysing customer purchase trends in each data set. If demand is high enough, more competitive price may still help meet ROI goals.
The Disney Theme Parks, for example, have shifted to a surge pricing model after noticing significant variations in demand at various times throughout the year. For example, visiting “the happiest place on earth” during off-peak seasons (such as Mondays or the month of September) may be significantly less expensive than visiting during peak times (think Spring Break, weekends and Christmas). However, many customers would be satisfied because the increase helps with attendance and wait times for popular attractions during busy seasons.
Forecasting
Forecasting sales and ROI is perhaps one of the most significant advantages of using data in consumer behaviour analytics. These forecasts are critical for developing budgets and developing strategies. Based on current and previous sales performance statistics, predictive forecasting generates intelligent and evidence-based projections of sales targets.
This can assist to decrease or even eliminate human error, which is typically blamed for errors in manual sales forecasting.
Predictive analytics may be a significant tool for marketers when it comes to increasing a company’s ROI. It may be time to explore incorporating predictive analytics into your approach when you examine your tactics in the new year.
The majority of big data specialists think that the quantity of data created in the future would expand dramatically. IDC estimates that the global data sphere will reach 175 zettabytes by 2025 in its Data Age 2025 research for Seagate.
What makes specialists believe in such a fast rate of expansion? The first is the growing number of internet users who conduct their whole lives online, from business to shopping to social networking.
Second, there are billions of linked devices and embedded systems all over the world that produce, gather and exchange a plethora of IoT data analytics every day. Enterprises will be able to produce and manage 60% of big data in the near future as they obtain the ability to store and analyse large amounts of data. Individual consumers, on the other hand, play a key role in data growth. In the same analysis, IDC predicts that by 2025, 6 billion people, or 75% of the world’s population, would engage with internet data on a daily basis. In other words, every 18 seconds, each connected user will have at least one data transaction. In terms of storage and processing, such massive datasets are difficult to deal with. Until recently, open-source ecosystems like Hadoop and NoSQL were used to overcome huge data processing problems. Open-source solutions, on the other hand, need manual configuration and debugging, which may be difficult for most businesses. Businesses began migrating huge data to the cloud in pursuit of more flexibility.
Due to the dominance of open-source platforms, most enterprises have been unable to access machine learning and AI applications until recently. Despite the fact that open-source platforms were created to bring technology closer to people, most organisations lack the necessary expertise to configure solutions on their own. The irony, oh the irony.
Commercial AI manufacturers began to construct connections to open-source AI and ML platforms and deliver inexpensive solutions that did not require sophisticated setups, which transformed the scenario. Furthermore, commercial vendors provide functionality that open-source platforms do not, such as machine learning model management and reuse. Meanwhile, scientists anticipate that sophisticated unsupervised algorithms, greater personalisation, and cognitive services will significantly boost computers’ capacity to learn from data. As a result, computers that can read emotions, drive automobiles, explore space, and cure patients will become more sophisticated.
Overall, big data is a huge industry with a vast professional scope as well as a cumulative understanding of the person’s journey through the internet allowing one to not just learn and analyze the patterns but also use them to influence and generate revenue. Big data is an important part of all industries since it helps one understand exactly where the customer is with their needs, wants, desires.
