Big data is often described using the three V’s: volume (large quantities of data), variety (many different types of data), and velocity (data that is generated and processed very quickly). It is widely perceived as an infinite source of information with the only barrier between us and a deep, scientific understanding of the needs and behaviours of clients and others, being our ability to analyse it effectively. It promises a lot but the return on investment in new data systems and SaaS products is limited without understanding these four key factors.
1.Garbage in, garbage out
As the name suggests, big data means a lot of data. In fact, too much data is floating around to control its quality. Who hasn’t entered false information in an online registration form (according to Facebook, I was born in 1904) or doesn’t know an aunt or uncle who creates a new profile every year?
2. False positives and flawed analysis
What’s more, big data tends to provide information based on people’s interactions with and reactions to what they are looking at on a website or on social media rather than what is possibly more important to them. Even if they avoid the trap of believing the “false positive” conclusions that big data sometimes can lead to, decision makers are left to play a game of guesswork to understand what matters most to their potential clients.
3. Not really that big everywhere
In new and emerging markets, concerns regarding the availability and reliability of big data are greater. Infrastructure limitations resulting in unreliable and slow communications, inequality of digital access and reliance on paper-based records render big data patchy in these contexts. Even in Nigeria, Africa’s second largest economy, the World Bank puts internet penetration at only 36% of the population.
4. Smoke and mirrors in hard-to-reach places
Beyond big data, challenges arise with traditional and even, tech-based solutions for localised data collection in emerging markets. Biassed, outdated and inaccurate data are amongst the many problems confronted by decision makers despite their willingness to pay for data collection solutions. For example, logistics company Omega, discovered that the provincial economic growth data they were about to use for target setting in China was vastly overstated and would have led to unrealistic targets and unsuccessful sales strategies.
Sometimes small data is better: a way forward
“Businesses need to be aware that more data is not necessarily better and that by starting with a highly localised approach and collecting data from local consumers themselves or by partnering with firms with local expertise, they’re more likely to develop successful strategies for global expansion.” https://toppandigital.com/translation-blog/the-big-data-challenge-in-emerging-markets/
Even as big data grows in importance in decision-making, its potential limitations will need addressing. Moreover, in new and emerging markets, it will be a while before sufficient and quality big data becomes available to address the needs of decision makers who want to sustainably operate in these environments. In this section, Emani will spotlight solutions that help overcome the data sourcing problem.
This month, our spotlight is on microsurveys which replace long, complex surveys with minisurveys to enhance audience responsiveness, especially in low literacy contexts. Microsurveys originate from micropolls which are typically single question polls that companies can add to their website or blog. The results of the poll are automatically displayed to the users taking the poll. Micropolls, in turn, owe their popularity to the demand for frequent pulse checks on voter sentiment during elections.
Read more here to learn more about Emani’s approach to microsurveys.