Table of Contents
Big data generally involves data sets that are huge, too fast or complex to be dealt with any traditional data-processing application software. It is a combination of unstructured, semi-structured and structured data collected by organizations that can be mined for information and strategic use. One of the most widely quoted definitions of big data is that given by the technology research firm, Gartner:
Big data Definition :
‘High-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processes for enhanced insight and decision making.’
Gartner
3 ‘V’s of Big Data:
As this definition illustrates, the key elements of ‘big-data’ can be described in relation to three ‘V’
characteristics:
1. Volume – the amount of data being generated and processed. The bigger the data, the more potential
insights it can give in terms of identifying trends and patterns.
2. Velocity – the rate at which data flows into an organization, and with which it is processed to provide
usable results.
3. Variety – the range of data types and sources from which to draw insights. In particular ‘big data’
combines structured and unstructured (ie, not in a database) data; for example, key words from conversations people have on Facebook or Twitter, the content they share through media files (tagged
photographs, or online video postings), and the online content they ‘like’ could all be sources of
unstructured data.
However, the volume and variety of data presents a challenge to organizations, as large data sets are
virtually impossible to capture, store and process with conventional technology. If data is too big, moves
too fast, or doesn’t fit with the structures of an organization’s existing information systems then, in order
to gain value from it, the organization needs to find an alternative way to process that data.
One of the key challenges organizations face is how to turn this ‘data’ into usable business ‘information’.
Data itself is not useful to organizations. It only becomes valuable to them once it is presented in a way
which enables them to gain insights from it.
As such, big data application could be seen as the combination of data sources, IT systems (to process
that data and to present it) and human skills (to analyze the data) which allows companies to undertake
more relevant and timely analysis than is possible with traditional business intelligence methods.
In its report, ‘Big Data and the Creative Destruction of Today’s Business Models’ (2013), the consultancy
firm AT Kearney notes that the enormous increase in the amount of data being generated is straining
companies’ IT infrastructures, and in many cases is slowing down their IT systems. Kearney argue ‘The
amount of data being generated can only be processed and managed with specialized technology.
Harnessing the full capability of technology advances is the key to unlocking big data’s potential.’
In particular, one of the challenges in delivering big data capabilities comes from transforming an
organization’s IT architecture to be able to efficiently handle unstructured as well as structure data,
‘messy’ external data as well as ‘clean’ internal data, and to cope with two-way data sharing with
customers and partners rather than just a one-way data flow into the organization.
Thus, 3 V’s may be extended to 8 V’s now.
8 ‘V’s of Big Data:
1. Volume–he amount of data being generated and processed. The bigger the data, the more potential
insights it can give in terms of identifying trends and patterns.
2. Velocity-the rate at which data flows into an organization, and with which it is processed to provide
usable results.
3. Variety-the range of data types and sources from which to draw insights. In particular ‘big data’
combines structured, semi-structured and unstructured.
4. Variability– One single data type may have multiple variations in data.
5. Veracity-Veracity ensures the accuracy. A huge inaccurate/garbage data cannot be referred to as Big Data.
6. Visualization-Visualization is important in today’s business. Using charts and graphs to visualize large amounts of complex data is much more effective and communicative to understand the insight.
7. Value-Data must be cost effective and must provide real value to the organization. If an information has no value, then it is waste of time.
8. Visualization — which takes a lot of time, effort, and resources —, you want to be sure your organization is getting value from the data.
Strategic Benefits of Big Data
Nonetheless, as computing resources have evolved, enabling companies to handle larger and more
complex data sets, companies benefit by not only improving their performance in existing operations
but also by identifying opportunities to expand product and service offerings.
For example, in the retail sector, big data assists the analysis of in-store purchasing behaviors in almost
real time. By having such rapid insights into changes in demand, stores can adjust their merchandise,
inventory levels, and prices to maximize sales.
More generally, AT Kearney’s report suggests that big data can create strategic value in three key areas:
Faster decisions – Big data can provide more frequent, and more accurate, analysis which helps to
speed up strategic decision making
Better decisions – Big data can estimate the impact of decisions using cross-organizational analysis, and
can help to quantify the impact of decisions
Proactive decisions – Use predictive analytics to forecast customer and market dynamics, which could
be used to help shape the decisions.
