Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. The extremely large data sets may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.
I always thought it’s not the size that matters, but what you do with it. However, turns out size really does matter 😉
Technology is now so powerful that it can store, process and analyse huge amounts of information that was previously just never possible.
So while we can now technically crunch the data, the challenges with big data include analysis, capture, data curation, search, sharing, storage, transfer, visualisation, and information privacy.
Why is Big Data important?
- Determining root causes of failures, issues and defects in near-real-time.
- Generating coupons at the point of sale based on the customer’s buying habits.
- Recalculating entire risk portfolios in minutes.
- Detecting fraudulent behaviour before it affects your organisation.
In a real-world setting, Big Data allows Facebook to analyse millions of feeds to determine what products I might be interested in so it can push related adds to me – a personalised experience.
I already predict that ‘personalisation’ will be the new CX battleground and the ability to plan, understand and react to individual Customer Journey Maps will be enabled through the analytics of big data.
Businesses are already collating vast arrays of information about the customers through purchases, loyalty cards, CRM etc.
In fact, we have never had this much data available.
Now the challenge lies in being able to analyse the data to improve business outcomes.
Advantages of Big Data
Like I’ve touched on above, for organisations that can make sense of all their data there are lots of advantage including:
- Improved customer service
- Improved revenue
- Improved operational efficiency
- Improved decision making
Kind of important stuff hey?
Type of Big Data
There are three types of Big Data:
Any data that can be stored, accessed and processed in the form of fixed-format is termed as a ‘structured’ data. Think of a database that contains employee information with ‘structured’ columns like first name, surname, employee ID, salary etc.
Any data with an unknown form – making it a lot more difficult to obtain value from. Think about when you do a Google Search – the results could contain text files, images, videos etc.
In a call centre or CX setting, we get huge amounts of data – from customer phone calls, website visits, store visits, purchases, emails, tweets, Facebook posts, Instagram posts, Whatsapp messages and so on that are all in different formats.
Semi-structured data sits somewhere in the middle and can contain both forms of the data. Data can be seen in a semi-structured data as a structured form but it is actually not defined with, for example, a table definition in relational Database Management System (DBMS) such a data represented in an XML file.
Yeah, it can all get a bit confusing but thankfully there are experts out there that can help!