Big Data: 3 Must-Haves to Go Beyond the Jargon

Big Data: 3 Must-Haves to Go Beyond the Jargon

This article is going to identify the three must-haves for building Big Data capabilities, including a layman’s definition of Big Data and key industry trends with use cases.

Technology buzzwords build up like folklore – an accumulation of stories, thoughts and principles. The term Big Data is no different, having been around since at least the 1990s, but its usage today can be colored by context and application. 

This article is going to take a closer look at

  • What Big Data is – the key concepts, facets that make data ‘big’
  • 3 must-haves for companies embarking on Big Data initiatives
  • Key industry trends and sample use cases towards the adoption and implementation of Big Data

Originally viewed from the eponymous size lens, the ‘Bigness’ of Data has now evolved with Gartner’s 3Vs definition now expanded to five:  

Volume:

The immensity of data sets, such as bulk transaction streams from financial systems, or sensor-generated data flows from industrial systems

Variety:

How heterogeneous the data is – from myriad sources and types with variations in size. Everything from structured; such as tables, hierarchies, metrics, measurements and documents, to unstructured; such as audio, video, images and plain text

Velocity:

How fast the data is generated and how its flow varies – such as periodic or non-periodic peaking, continuous or event-triggered, batch mode or real-time streaming  

Veracity:

How accurate and consistent the data is – the quality aspect, affected by biases, noises and subjectivity, such as social media ‘sentiment’ and user feedback    

Value:

To what degree the data enables business decision making through actionable analysis, such as consumer buying behavior patterns or predicting failure of machinery for preventive maintenance

So how do you go about handling these challenges? Here are three must-haves to go beyond the jargon and shore up your Big Data management capabilities:

#1. The Chief Data Officer

With information becoming the key source of sustainable competitive advantage, it is near-imperative to formalize strategic and governance functions around efficient and effective maintenance of information assets. These are typically embodied in the role of the Chief Data Officer (CDO). In fact, a Gartner study estimated that half of all companies in regulated industries will have a CDO in place by 2017.

The office of the CDO is responsible for defining the business need and value of data, along with the governance mechanisms and policies. It thus needs close coordination with the CIO’s, to minimize overlap and enable business and IT to speak the same language.

It is also important for the CDO’s organization to ensure that the business, rather than IT are the true owners of data. This is key to driving transformational change leveraging the power of Big Data.

#2. The Big Investment – building analytics and scalability

Deriving big benefits from Big Data means putting in systems, platforms and infrastructure for the long haul – from enterprise data warehousing to analytics engines and machine learning. IDC estimates the Big Data industry to grow to $203 billion by 2020.

While the appetite stems from the business need and strategic digital roadmap, there’s a plethora of best-of-breed point solutions to end-to-end BI (business intelligence) platforms in the market to suit your organization’s needs. Also keep in mind to build the technology stack and architecture for business and technical scalability from the ground up as well as integration with the enterprise legacy ecosystem.  

Though Big Data is one of the biggest technology investments the CIO may need to make, the payback is bigger still. A report by Nucleus Research estimates returns of 13 times over for each dollar spent on analytics.

#3. Data security and privacy

With an increasing focus on how companies manage customers’ data, specifically personally identifiable (PI) data, no Big Data initiative can take off without clearly identifying how data security and privacy would be managed. The legal ramifications, such as those imposed by the GDPR in the EU and the UK, necessitate a substantial revamp, if not a complete overhaul of traditional data protection approaches.

While the office of the CDO is tasked with the overarching policy aspect, the CIO organization is integral to its systemic implementation and operationalization.

Beyond securing Big Data, applying deep analytics on security data itself – from physical security systems such as access control, CCTV and alarms, along with incident and system error logs, and phishing detection – would take your organization’s intrusion detection and prevention capabilities to the next level.

Think of an automotive insurer utilizing vehicle sensor data to analyze driving patterns and behavior – honing their risk models and customize ‘usage-driven’ insurance plans. Consider an online retailer coupling real-time consumer behavior on the app with predictive analytics to reduce cart abandonment rates. Banks teaming up to leverage deep analytics across organizational boundaries and channels with machine learning for fraud detection and prevention.

These and many more use cases harnessing Big Data are transforming business models and revenue streams across industries. While a key cog in the digital revolution, keep in mind that data is just that, unless analyzed to turn it into actionable business insight.

Author: Aman Vijh is a technology strategist and trainer with over 11 years of experience enabling C-suite clients to solve business problems by leveraging technology. In his current avatar he runs Digital and IT strategy consultancy Vidhi Vici, and blogs on tech consulting at www.vidhivici.in/blog. He is @vijhilante on Twitter.

Insights for Professionals