What is Big Data and how did a pizza delivery company become a technological leader in data usage and analysis?
Big Data Basics
Big Data is said to be the next big thing within digital marketing, Chaffey, (2016) instigated a poll which collated over 1,500 responses, resulting in a prediction of big data being subjected to be the (second) biggest digital marketing trend in 2016.
Big data has been defined by Gartner as “high volume, velocity and/or variety of information assets that demand new, innovative forms of processing for enhanced decision making, business insights or process optimisation” (Pemberton, 2015). However, the true definition of big data is still being contested, 154 global executives were asked to define big data, some definitions focusing on what it is, whilst some definitions focus on what it does (Gandomi & Haider, 2014).
Characteristics of big data as described by Laney (2001) suggests that volume, variety and velocity as the three dimensions that refer to big data, agreeing with Gartner’s description of big data.
Volume refers to the magnitude of data, we hear of gigabytes, terabytes, petabytes etc. The big in big data refers to the size of datas, however what maybe defined as big data today may not be so big in the near future due to storage capacities ever growing pace. Moreover, two datasets of the same size may require different data management technologies based on their type, for example; tabular datasets against video datasets, resulting in what is meant by big data generally depends upon the industry (and datasets) (Gandomi & Haider, 2014).
The variety of big data refers to the structural heterogeneity in a dataset. A defining characteristic of big data is its high level of variety. Data that is omitted holds the form of structured, semi-structured and unstructured. Structured data is the tabular data found in databases relational to spreadsheets. “Structured data constitutes only 5 percent of all existing data” (Cukier, 2010). Semi-structured data is data that does not conform to strict standards, Extensible Markup Language (XML), a textual language that exchanges data across the web is a typical example of semi-structured data. Un-structured datasets hold the form of images, audio and video; data which is generally difficult to assess by machines for analysis (Gandomi & Haider, 2014). The way in which organisations process, collate and hypothesise these datasets will be imperative to how an organisation administers big data.
The velocity of big data refers to the rate upon data is generated and recognised and the speed at which the data is analysed and acted upon. Data is now created at an unprecedented rate with devices such as smartphones creating data at an incongruous pace, this surge in data creation creates a need for organisations to implement real-time analytics. For example, Walmart handles and processes one million transactions per hour, imported into databases, this hourly processing can amount to an estimated 2.5 petabytes of data, per hour (Cukier, 2010).
What is a petabyte of data? To imagine two petabytes of data, you would have to imagine 40 million four-drawer filing cabinets full of text (TechTarget, 2016).
Other (‘V’) dimensions of big data include:
- Veracity – the inherent unreliability of some sources of data.
- Variability (and complexity) – Variability is the variation in data flow rates; complexity the generation of big data through a myriad of sources.
- Value – data received usually has a low value of relativity to its volume.
Dominos Pizza and Big Data
Dominos is the worlds largest pizza delivery chain, with over 10,000 outlets around the globe. Pizza delivery does not, at first, seem the most glamorous of high-tech industries, however Dominos has been somewhat of the vanguard of the big data discussion. Dominos are one of the most technologically advanced companies in the way they harness big data (Daley, 2016). Dominos Pizza enlisted the assistance of Splunk, a self-confessed ‘data operational-intelligence’ organisation to deduce and monitor the real-time sales Dominos was making during the 2011 Super Bowl. After its initial success, Splunk has since been used for app-monitoring, security monitoring and e-commerce monitoring. Russ Turner, the Engineering manager for site reliability at Dominos described Dominos as “an E-commerce company that sells pizza” due its advancements in the way the organisation analyses, harnesses and uses its datasets (Schramm, 2014).
The way in which Dominos lets its customers order pizza, through social media platforms, smart devices, and even car entertainment systems (Ford’s Synch Dominos ordering app), allows Dominos the ability to capture an abundance of data. Data captured via all these channels is fed into Dominos Information Management Framework, this data amounts up to 85,000 daily data-sources which are both structured and un-structured. The way in which the data is analysed allows Dominos to segment and target individual customers whilst assessing individual and household buying patterns. Dominos can understand individuals buying patterns, also Dominos can understand multiple individuals in a household to understand who is the dominant buyer, who reacts to coupons and most importantly, Dominos can understand how customers react through the devices and platforms that are being used to order. This means that individual customers or even households can be presented and targeted with tailored coupons and product offers based on statistical modelling of its customers (Marr, 2016).
Dominos can collect real-time revenue insights and statistics from over 10,000 stores allowing the tweaking and targeting of campaigns in real-time leading to device and promotion improvements that impact instantly (Dutcher, 2014).
This video by Splunk shows how Dominos has become a leader in big data usage:
Data is everywhere, data is always growing, it is the way in which organisations assess this data that will be paramount to an organisations success. As technological advancements in storage and communications grow, a cost-effective capture of big data in a timely manner is ever more so permeable (Gandomi & Haider, 2015). Big data is a relatively contemporary concept, the way in which datasets are analysed in real-time through location aware social media and smart devices will result in the myriad of big data. However, the more this volume of data grows, so does the volume of unreliable and less trustworthy data.
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