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Hello. My name is Wendy Gunther.
I am currently at the VU Amsterdam.
I'm part of the KIN Research Group which is a multidisciplinary group of researchers
that focuses on a range of topics related to organizing for digital innovation.
In my own research,
I focus on how organizations can realize social and economic values from data.
In this lecture, I will talk about challenges and
strategies for managing and utilizing big data.
Let me present to you a brief overview of the talk.
First, I will explain what people mean by
big data and how it is defined by scholars and practitioners.
Then, I will talk about what value may be gained from big data by organizations.
After, I will present the results of
a systematic literature review that my co-authors and I performed,
in which we focused on tensions that organizations
face when they tried to realize value from big data.
Finally, I will illustrate how organizations may deal with these tensions.
Let's begin by exploring what big data actually is.
You've probably heard about big data before-
many white papers, academic papers, books,
news articles and even TV shows have used the term.
But, what do people actually refer to when they talk about big data?
Researchers and practitioners generally define
big data by three technological characteristics.
Those characteristics are volume, variety, and velocity.
As they all start with a V,
they're also called the three V's of big data.
The first V stands for volume-
volume refers to the size of the data.
Think of it as the amount of disk space that a dataset occupies.
Whereas, traditionally, organizations dealt with gigabytes of data,
today we are talking about terabytes or even petabytes.
To this date, the volume of data collected and
also stored by organizations is ever-increasing.
The second V stands for variety.
Variety refers to the number of different data sources,
and inherently, the number of different formats in which the data are presented.
Organizations now have access to data from many different sources,
even beyond the boundaries of the organization.
For example, organizations may collect data
from social network sites and wearable devices,
and combine these data with their own internal data.
The third V stands for velocity.
Velocity refers to the speed at which data are generated, collected, and analyzed.
For example, the Large Hadron Collider by
CERN generates petabytes of information per second.
Many organizations cannot keep up with the pace at which data are generated,
and need to find ways to filter out irrelevant data.
More Vs have been added in time.
For example, scholars refer to veracity when they consider the quality of the data,
including how much noise is present in the data.
Others say it's variability to refer to
the fact that data can have different meanings in different contexts.
Additional characteristics can be added that do not start with a V, such as granularity,
which refers to the level of detail of the data.
Still, there are no rules for how much volume there needs to be,
and how many different forms data should come,
how detailed they must be,
or how fast data needs to be generated for them to be classified as big data.