Big Data essentially means all data. And there is quite some data nowadays: from customer data and social data to data regarding all possible areas of business, technology, science, customers and beyond.
The term Big Data has a technology and processing background in an age where ever larger data sets became available and ever more data sources were added bringing huge amounts of data with them. Just think about information-sensing devices, for instance. The definition of the term has been evolving, moving away from its’ original definition in the sense of controlling data volume, velocity and variety,as described in this 2001 (!) META Group / Gartner document (PDF opens).
The renewed attention for the term was caused by combination of open source technologies to store and manipulate data and the increasing volume of data as Timo Elliot writes. Today, and certainly here, we look at the business, intelligence, decision and value/opportunity perspective).
With the Internet of Things happening and the ongoing digitization in many areas of society, science and business, the collection, processing and analysis of data sets is indeed a challenge and opportunity for many years to come, even if the term as such doesn’t really matter and is somewhat hyped. The core challenge and at the same time opportunity is one of value, results and progress in the broadest sense.
“Big data” is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.(Gartner’s definition of Big Data).
As such Big Data is pretty meaningless or better: it’s (used) as an umbrella term. And as is the case with most “trending” umbrella terms, there is quite some confusion. Analyzing data sets and turning data into intelligence and relevant action is key. In an increasingly real-time world, this aspect of purpose and relevance in many areas goes hand in hand with speed, leading to another term: fast data.
Big Data: a Consequence and a Catalyst
While Big Data is often misunderstood from a business perspective (it’s about using the ‘right data’ at the right time for the right reasons) and there are debates regarding the use of specific data by organizations, it’s clear that Big Data is a logical consequence of a digital age that will not go away anytime soon. At the same time it’s a catalyst in several areas of digital business and society. Just one example: Big Data is one of the key drivers in information management evolutions.
Big Data is used in many areas already. It is a much debated topic and the way it is covered obviously depends on the application area, the way it’s looked upon (technology, business, science, marketing, privacy, agility, etc.) and the background of who covers it. We like to look at Big Data from the practical application and value perspective: innovation, agility, relevance, business, progress,… At the same time we know there are debates regarding some areas or ways Big Data is used. These debates are good and necessary.
The importance of Big Data and more importantly, the intelligence, analytics (content analytics), interpretation, combination and smart action smart organizations derive from a ‘right data’ and ‘relevance’ perspective will be driving the ways organizations work and impact recruitment and skills priorities. The winners will understand the Value instead of just the technology and that requires data analysts but also executives and practitioners in many functions that need to acquire an analytical, let alone digital, mindset. A huge challenge, certainly in domains such as marketing and management.
The V’s of Big Data: Adding Value
Value is an essential ‘V’ that should be added to the so-called three big data ‘V’s’. IBM added a fourth one to that list in times where reliable and right data at the right time increasingly matter in an era of ongoing digitization.
The Vs of Big Data (including velocity):
- Volume: the sheer volume of data and information that gets created whereby we mainly talk infrastructure, processing and management of big data, be it in a selective way.
- Velocity: this is where analysis happens and where we also look at the speed and mechanisms at which large amounts of data can be processed for increasingly near-time or real-time outcomes, sometimes even leading to the need of fast data.
- Variety: on top of the data produced in a broad digital context, regardless of business function, societal area or systems, there is a huge increase in data created on more specific levels. Variety is about the many types, being structured, unstructured and everything in between.
- Veracity. This has everything to do with accuracy which from a decision and intelligence viewpoint becomes certainty and the degree in which we can trust upon the data to do what we need/want to do.
Add to that Value, the outcome and the relevance which lies in the eye of the beholder and the stakeholder. Welcome to Big Data in Action.
More about big data and its evolutions and applications
Smart data: beyond the volume and towards the reality
Big data is…big. With increasing volumes of mainly unstructured data comes a challenge of noise within the sheer volume aspect. In orde to achieve business outcomes and practical outcomes to improve business, serve customer betters, enhance marketing optimization or respond to any kind of business challenge that can be improved using data, we need smart data whereby the focus shifts from volume to value.
Fast data: speed and agility for responsiveness
In order to react and pro-act, speed is of the utmost importance. However, how do you move from the – mainly unstructured – data avalanche that big data really is to the speed you need in a real-time economy? Fast data is one of the answers in times when customer-adaptiveness is key to maintain relevance.
Big data analytics: making smart decisions and predictions
As anyone who has ever worked with data, even before we started talking about big data, analytics are what matters. Without analytics there is no action or outcome. While smart data are all about value, they go hand in hand with big data analytics. In fact, big data analytics, and more specifically predictive analytics, was the first technology to reach the plateau of productivity in Gartner’s Big Data hype cycle.
Actionable data: what makes data actionable in practice
What are the conditions data has to meet in order to become actionable? Data needs to be correct, legible, interoperable, accessible, contextual, qualitative, liquid and so much more. A good overview of the traits of actionable data and conditions to enable the actionable data is provided by Hielix.