There are needs for analytics everywhere, and the normal presumption is that you need big data to get quality analytics. But for emerging markets, getting to big data is not that easy. You might, in fact, have to start with small data to get there.

The challenges with small data collection

Jim Lee, Phillip Savio, and Carey Carpenter of Deloitte Consulting recently described efforts to understand enough about the need for vaccines in Southern Sudan to at least develop a forecast. They also wrote about Ukraine, where data limitations in logistics monitoring and evaluation were such that inventory stock-outs occurred.

The problems for companies trying to do business in these areas are limited access to information and analytics that can tell them enough about these markets to enable them to accurately gauge supply and demand. Most of these emerging markets are not highly digitalized, so generating the “raw material” data that feeds into analytics, whether data is structured or unstructured, is a major hurdle. When it comes to looking through purchase orders, invoices, inventory, balance sheets, and information on consumer preferences, the information is most likely to be found in spreadsheets, paper-based files and reports, and manually maintained ledgers. None are readily convertible to digitized data that can be uploaded into analytics software.

To deal with these challenges, organizations rely on social media reports from the field; these reports assist them in understanding the needs and consumption levels of the many mom-and-pop type stores that characterize rural areas in developing countries. They learn about product design for economies where consumers lack large refrigerators and/or shelf space for Costco-size goods, and where consumers prefer buying produce often and fresh.

If companies have a problem with logistics, they handcraft homemade tracking tools that begin to give them a picture of how their logistics are performing, and where improvements can be made. In some cases, they do manual charting to see which products are most popular in which regions of a country. All of these efforts are small data approaches to marketing in emerging countries that lack the infrastructure and the trained personnel to implement and carry out sophisticated big data and analytics systems.

How important are these small data analytics efforts?

Gartner reports that companies are predicting 40 to 60% of their growth in revenues will come from emerging markets over the next decade, and McKinsey & Company predicts that in 12 years, 57% of the nearly one billion households with earnings greater than $20,000 a year will live in the developing world.

“Companies need analytics to cope with these new markets,” said Richard Howells, vice president of Go to Market for SAP. “This means harnessing intelligence from both structured and unstructured data. When you talk about unstructured data, there are millions of bits of data, and 95 percent of it is ‘noise.'”

This is exactly what major corporations that are entering emerging markets are discovering as they advance small data approaches while waiting for the big data analytics and sophistication that will be available in the future. As these companies wrestle with makeshift analytics and work through paper-based documents and social media reports from the far reaches of developing countries, they are also developing insights and business savvy in these markets that will put them ahead in their future analytics that one day will use both the currently unstructured information that is available to them and the structured information from newly installed systems of records that are bound to appear as these emerging economies and countries grow in their data sophistication.

Is your company collecting small data? If not, do you foresee it being part of your organization’s analytics strategy in the near future? Let us know in the discussion.

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