All of us have collected data, whether it’s in the form of something as simple as a shopping list or maybe even gathering receipts for the end of financial year tax time. Big Data is basically the same, only on a much larger scale. Let’s take that shopping list for example and build upon it. Say you have an item you need to pick up for lunch, so you head to your local grocer, you walk around the store to find the item, head to the register to pay and then head back home. In Big Data terms the grocer could have captured something that looks like this (simplified form):
Customer enters shopping centre at 11:30am on Sunday (19/8/2012) with parking ticket
Customer purchases - Lamb Roast, Generic Brand, 1.2kg, Fridge Section 2 - $24.00
Customer scans loyalty card – Capturing - name, address, age, and gender
Customer leaves car park at 11:53am on Sunday (19/8/2012)
There’s much more that can be captured but this will do for the purposes of this explanation. The grocer can collect immense amounts of data about its customers, this data when analysed correctly, can generate some great insights in order to increase sales and generate more revenue. In this case, we can get information such as this:
Generic Brand Lamb Roast Analysis Target Market 80% male, 50% aged 29 to 33, and 30% live in Richmond, Victoria.
Price / Demand Demand is greatest when priced at $19.80 per kg, 75% of sales are 1.2kg portions, 60% of sales occur between 9am and 11am on Sundays, and demand is 30% greater when placed in Fridge Section 3.
Inventory 3 remaining, next delivery 23 hours, no stock expected in 4 hours, require 4 units per hour to meet demand.
Advertising 5% used vouchers and 8% ROI from marketing campaign A.
Association 10% buy premium brands, 90% also buy generic apple juice and 98% also buy potatoes.
Convenience 30% of customers come to the shopping centre only come to my shop, 80% of my customers drive cars, and customers travel an average of 5.2km.
This information is invaluable and can be produced in real-time for targeted strategies that can be executed to drastically increase ROI. So, why wouldn’t any enterprise business want to get their hands on these insights?
Nothing comes easy; there are many difficulties that surround Big Data, the big three being – storage, visualisation and analysis. Storage is a diminishing problem with cloud computing and hardware becoming evermore inexpensive, so let’s skip over that point. The other two – visualisation and analysis, is where the major problems lie. It’s all too easy for enterprise businesses to be overly dependent on databases that operate exclusively from each other to provide limited insights across departments, agencies and even countries.
Enterprise businesses are using data more than ever to make decisions, but in a decreasingly effective manner. This is due to one simple fact; add more datasets and you exponentially increase the complexity in the analysis process, which leads to the inevitable - increases in cost, resources and time. We believe in a Big Data platform that is cost effective and is constantly evolving by simplifying the visualisation and analysis process to extract all the rewards that Big Data offers, instantly.