Embracing Big Data and Data Analytics

What Is Big-Data and Analytics and Why Is It Important?

It is vital to begin with the building blocks to set context and realign ourselves with the same foundation and definitions. Data can be defined as "basic, discrete, objective facts about something such as who, what, when, where". The knowledge pyramid, designed originally in 1986, stacks data as the base, leading to information, knowledge, then finally, wisdom. This structure was revised and inverted in 2000 to acknowledge that there is more information than data. Wisdom is also replaced by intelligence, which accounts for actionable knowledge, and intelligence ultimately leads to organizational learning in this revised version. How we think about knowledge represents the added complexity as we move from simply "data" to the advent of "big-data".

Big-data is entirely changing how we obtain knowledge and transform from intuition-based to evidence-based decision making. Big-data is primarily used to translate data into business advantages and is described as 'big' in four or sometimes five key components. These four dimensions of big-data are referred to as the 4 V's: 'volume, velocity, variety, and veracity'. 'Value' has been added recently added as a fifth dimension by some thought leaders ("Infographics Master of Science in Leadership Big Data's Growing Role in Organizational Leadership & Development," n.d.).

Volume refers to the sheer scale of data created and available. IBM estimates that 2.5 quintillion bytes of data are generated every day. Walmart is estimated to collect 2.5 petabytes of data each hour which, for reference, is equivalent to about 20 million filing cabinets of text. One interesting driver to this quantity of data is the advent of genomic technology and whole genome sequencing (WGS). WGS allows a much more robust and phylogenetic perspective and introduces a new world of possibilities. Overall, the volume of data impedes many technological systems from readily accessing data and restricts humans from using this information without implementing any analytics. This volume is exponentially growing, supported by the estimation that 90% of existing data has been created in the last two years.

Velocity is the near real-time speed related to data creation and processing. Rapid insights provide the most useful advantage and therefore act as the gold standard to data digestion and output. Variety accounts for the diversity of incoming data, originating from images, text files, social media, videos, sensors, GPS signals, and more. Challenges in this diversity come in organizing and standardizing structured databases as well as processing multiple types of data in single databases. Veracity refers to the unknown, or uncertainty, of data. Beyond a lack of trust in data quality from one in three business leaders, poor data quality costs the United States $1.3 trillion per year. Finally, value "through insights from superior analytics" is the desired outcome associated with big-data usage. Data collection and data generation must have accurate and substantial value to serve any purpose.

Leaders are recognizing that big-data and analytics enable better prediction capacity to closer meet customer needs which gives their organizations a competitive edge. Marshall et al. estimates that when organizations exploit big-data and analytics to drive innovative decisions, they are 36% more likely to outperform competitors who do not. This revolution is transformative and allows data-driven decision making to largely replace intuitive, gut-based decision making.

It is indisputable that this era of big-data and analytics will affect every aspect of our society. Therefore, it is vital to address the key characteristics and attitudes that support and enable leaders to embrace big-data and analytics as integrated parts of their organizational culture. The following section will enumerate these characteristics and describe how leaders can adopt and emulate these to transform their culture.