The digitalisation of business and consumption brought on by the widespread use of the Internet has created a dilemma for companies: what to do with all that data? The concept of Big Data has been borne out of this excess information lying on servers, devices and web pages across vast networks and mainframes. The growing realisation that data can actually be harvested, monetised and strategised is the underlying ideology behind the implementation of Big Data, which is creating entire industries and offering businesses additional revenue streams from previously dormant sources. Opportunities in Big Data are considerable, but strong human and capital resources are necessary to organise and utilise it accordingly.
Broadband Business Use and Population Internet Penetration in Selected Economies: 2013
Source: Euromonitor International from UNCTAD/Eurostat/International Telecommunications Union/OECD/national statistics
Big Data Comes of Age as Technology Enables its Storage and Analysis
In its simplified form, Big Data is the gathering of large pools of data for a strategic purpose. Akin to a lab experiment, the terminology has come of age due to the growing digitalisation of society. Mobile phones, PCs and the Internet are all tools that collect data today at an unprecedented rate. With 37.5% of the world’s population online in 2013 and the vast majority of businesses operating through the web, collecting data has become a simple question of software tracking tools and databases.
However, while accessing data can be straightforward, making actual incisive use of it is the hard part, and this is where the opportunities truly lie.
Big Data Offers Cost Savings and New Revenue Streams
Big Data is a big thing among analytical businesses, but its reach can extend to almost all platforms that process information:
- Big Data is already an integral part of some segments. The online marketing segment, for example, is a major user of data-based solutions, whether through social media analytics or click-through statistics. Social networks such as Twitter and Facebook have become huge databases of consumer habits and opinions. Global online adspend per household reached US$45.0 in 2013;
- Finding a suitable buyer for accumulated Big Data has become a business in itself. Online classifieds platforms have been able to profit handsomely by recognising markets where their data is valuable. Major auto classifieds businesses, for example, are selling their data to car insurance firms;
- Gaining efficiency through Big Data analysis is becoming a vital component of operational policy, and can be especially vital for public organisations that lack strategic cohesion. A report by the McKinsey Global Institute recognised as early as 2011 that the US healthcare system could save US$300 billion a year through better analysis of everything from clinical trials to health insurance transactions.
Lack of Qualified Expertise and High Initial Input Costs Offer Barriers to Continued BD Implementation
Growing business connectivity will make Big Data a logical part of strategy for most firms in the future (business fixed broadband use was already at a high 93.2% in Western Europe in 2013), yet some clear challenges remain in the drive for Big Data optimisation.
Since the segment is still a relatively niche and specialist domain, there is a significant lack of human resources able to implement the necessary technical and operational aspects of Big Data. This is especially an issue in emerging economies where IT personnel are already in high demand due to a deficit in expertise.
A secondary issue is the input cost necessary to develop the requisite Big Data infrastructure, a particular challenge for small and medium-sized companies. Although cloud computing resources and specialist software packages have raised affordability of IT operations, the process of locating, extracting and organising raw data can in itself be highly capital intensive. Investing in external Big Data consultancy can ultimately pay off in the long run, but for many the industry is still taking shape and presents a speculative venture. Getting into Big Data and its big rewards will require some big risks.