Big data is part of the Performance Management syllabus. But what exactly is big data?
Big data is part of the Performance Management syllabus.
What is big data?
There are many definitions of the term ‘big data’ but most suggest something like the following:
‘Extremely large collections of data (data sets) that may be analysed to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.’
In addition, many definitions also state that the data sets are so large that conventional methods of storing and processing the data will not work.
The characteristics of big data, known as the 3Vs, are:
These characteristics, and sometimes additional ones, have been generally adopted as the essential qualities of big data. The commonest fourth ‘V’ that is sometimes added is Veracity: is the data true and can its accuracy be relied upon?
The volume of big data held by large companies such as Walmart (supermarkets), Apple and EBay is measured in multiple petabytes. A typical disc on a personal computer (PC) holds a gigabyte, so the big data depositories of these companies hold at least the data that could typically be held on 1 million PCs, perhaps even 10 to 20 million PCs.
The scale of this is difficult to comprehend. It is probably more useful to consider the types of data that large companies will typically store.
Via loyalty cards being swiped at checkouts: details of all purchases you make, when, where, how you pay, use of coupons.
Via websites: every product you have every looked at, every page you have visited, every product you have ever bought.
Social media (such as Facebook and Twitter)
Friends and contacts, postings made, your location when postings are made, photographs (that can be scanned for identification), any other data you might choose to reveal to the universe.
Mobile phone companies
Numbers you ring, texts you send (which can be automatically scanned for key words), every location your phone has ever been whilst switched on (to an accuracy of a few metres), your browsing habits. Voice mails.
Internet providers and browser providers
Every site and every page you visit. Information about all downloads and all emails (again these are routinely scanned to provide insights into your interests). Search terms which you enter.
Every receipt, payment, credit card information (amount, date, retailer, location), location of ATM machines used.
Some of the variety of information can be seen from the examples listed above. In particular, the following types of information are held:
- Browsing activities: sites, pages visited, membership of sites, downloads, searches
- Financial transactions
- Buying habits
- Reaction to advertisements on the internet or to advertising emails
- Geographical information
- Information about social and business contacts
- Numerical information
- Graphical information (such as photographs)
- Oral information (such as voice mails)
- Technical information, such as jet engine vibration and temperature analysis
This data can be both structured and unstructured:
Structured data: this data is stored within defined fields (numerical, text, date etc) often with defined lengths, within a defined record, in a file of similar records. Structured data requires a model of the types and format of business data that will be recorded and how the data will be stored, processed and accessed. This is called a data model. Designing the model defines and limits the data which can be collected and stored, and the processing that can be performed on it.
An example of structured data is found in banking systems, which record the receipts and payments from your current account: date, amount, receipt/payment, short explanations such as payee or source of the money.
Structured data is easily accessible by well-established database structured query languages.
Unstructured data: refers to information that does not have a pre-defined data-model. It comes in all shapes and sizes and it is this variety and irregularity which makes it difficult to store in a way that will allow it to be analysed, searched or otherwise used. An often quoted statistic is that 80% of business data is unstructured, residing in word processor documents, spreadsheets, PowerPoint files, audio, video, social media interactions and map data.
Here is an example of unstructured data and an example of its use in a retail environment:
You enter a large store and have your mobile phone with you. That allows your movement round the store to be tracked. The store might or might not know who you are (depending on whether it knows your mobile phone number). The store can record what departments you visit, and how long you spend in each. Security cameras in the ceiling match up your image with the phone, so now they know what you look like and would be able to recognise you on future visits. You pass near a particular product and previous records show that you had looked at that product before, so a text message can be sent perhaps reminding you about it, or advertising a 10% price reduction. Perhaps the store has a marketing campaign that states that it will never be undersold, so when you pass near products you might be making a price comparison and the store has to check prices on other stores websites and message you with a new price. If you buy the product then the store might have further marketing opportunities for related products and consumables and this data has to be recorded also. You pay with an affinity credit card (a card with associations with another organisation such as a charity or an airline), so now the store has some insight into your interests. Perhaps you buy several products and the store will want to discover if these items are generally bought together.
So just walking round a store can generate a vast quantity of data which will be very different in size and nature for every individual.
Information must be provided quickly enough to be of use in decision-making and performance management. For example, in the above store scenario, there would be little use in obtaining the price-comparison information and texting customers once they had left the store. If facial recognition is going to be used by shops and hotels, it has to be more or less instant so that guests can be welcomed by name.
You will understand that the volume and variety conspire against velocity and, so, methods have to be found to process huge quantities of non-uniform, awkward data in real-time.
Processing and analysing big data
The processing of big data is generally known as big data analytics and includes:
- Data mining: analysing data to identify patterns and establish relationships such as associations (where several events are connected), sequences (where one event leads to another) and correlations.
- Predictive analytics: a type of data mining which aims to predict future events. For example, the chance of someone being persuaded to upgrade a flight.
- Text analytics: scanning text such as emails and word processing documents to extract useful information. It could simply be looking for key-words that indicate an interest in a product or place.
- Voice analytics: as above but with audio.
- Statistical analytics: used to identify trends, correlations and changes in behaviour.
The analytical findings can lead to:
- Better marketing
- Better customer service and relationship management
- Increased customer loyalty
- Increased competitive strength
- Increased operational efficiency
- Improved cost models
- The discovery of new sources of revenue.
Examples of the uses of big data
Netflix: this company began as a DVD mailing service and developed algorithms to help it to predict viewers’ preferences and habits. Now it delivers films over the internet and can easily collect information about when movies are watched, how often films might be stopped and restarted, where they might be abandoned, and how users rate films. This allows Netflix to predict which films will be popular with which customers. It is also being used by Netflix to produce its own TV series, with much greater assurance that these will be hits.
Amazon: the world’s leading e-retailer collects huge amounts of information about customers’ preferences and habits which allow it to market very accurately to each customer. For example, it routinely makes recommendations to customers based on books or DVDs previously purchased.
Airlines: they know where you’ve flown, preferred seats, cabin class, when you fly, how often you search for a flight before booking, how susceptible you are to price reductions, probably which airline you might book with instead, whether you are returning with them but didn’t fly out with them, whether car hire was purchased last time, what class of hotel you might book through their site, which routes are growing in popularity, seasonality of routes. They also know the profitability of each customer so that, for example, if a flight is cancelled they can help the most valuable customers first.
This information allows airlines to design new routes and timings, match routes to planes and also to make individualised offers to each potential passenger.
Target: Target is the second largest discount retailer in the USA. There is an often quoted story about their ability to predict when a customer is pregnant – frequently before the customer has informed her family. By looking at about 25 products it is claimed that they can create a pregnancy predictor. For example, early pregnancy often causes morning sickness so consumers would perhaps change to blander food and less perfumed shower gel. Why would Target be interested in knowing whether a consumer is pregnant? Well that person will require different products during the pregnancy then in a few months the baby will have its own product needs: nappies, baby shampoo and clothes. Early identification of pregnancy can allow Target to establish the shopping habits of the mother and perhaps even the preferences of the child.
Tesco: British supermarket group Tesco has operations in several countries around the world. In Ireland, the company developed a system to analyse the temperature of its in-store refrigerators. Sensors were placed in the fridges that measured the temperature every three seconds and sent the information over the internet to a central data warehouse. Analysis of this data allowed the company to identify units that were operating at incorrect temperatures. The company discovered that a number of fridges were operating at temperatures below the -21◦C to -23◦C recommended. This was clearly costing the company in terms of wasted energy. Having this information allowed the company to correct the temperature of the fridges. Given that the company was spending €10 million per year on fridge cooling costs in Ireland, an expected 20% reduction in these costs was a significant saving.
The system also allowed the engineers to monitor the performance of the fridges remotely. When they identified that a particular unit was malfunctioning, they could analyse the problem then visit the store with the right parts and replace them. Previously the fridges would only be fixed when a problem had been discovered by the store manager, which would usually be when the problem had developed into something more major. The engineers would have to visit the store, identify the problem, and then make a second visit to the store with the required parts.
Dangers/risks of big data
Despite the examples of the use of big data in commerce, particularly for marketing and customer relationship management, there are some potential dangers and drawbacks.
Cost: It is expensive to establish the hardware and analytical software needed, though these costs are continually falling.
Regulation: Some countries and cultures worry about the amount of information that is being collected and have passed laws governing its collection, storage and use. Breaking a law can have serious reputational and punitive consequences.
Loss and theft of data: Apart from the consequences arising from regulatory breaches as mentioned above, companies might find themselves open to civil legal action if data were stolen and individuals suffered as a consequence.
Incorrect data (veracity): If the data held is incorrect or out of date incorrect conclusions are likely. Even if the data is correct, some correlations might be spurious leading to false positive results.