The Wide, the Small and the Big Data
Timea Toltszeki
According to a Gartner study released in 2021, 70% of the organization will move to a wide and small data, providing more context to analytics by 2025. Current data leaders think this shift in data & analytics techniques will make organizations more adaptable and data literate.
We started to use the term ‘Big Data’ not a long time ago, however the concept of large datasets dates back to the 1960s when the first data centres and the development of relational databases were getting started. Big data is data that contains greater variety,
arriving in increasing volumes and with more velocity. These big da sets are so voluminous
that traditional data processing software just can’t manage them, nevertheless giving the
possibility to businesses to analyze problems and tackle these issues.
In other words, big data is a great way to figure out a particular market trend and visualize it, or understand the distribution pattern of the market components. Ultimately, big data is expensive. Extracting insights and values from big data sets is not just complex but requires a robust IT infrastructure and expensive FTEs.
What is “wide data” and “small data”? Let’s unpack it.
The common denominator is picking out more specific information and distinct insights from individual data components and drawing valuable comparisons. When an analyst is combining a variety of small and large, structured and unstructured data then we talk about wide data technique. Small data is focused on applying analytical techniques that look for useful information within small, individual sets of data.
A real-life example of wide data is customer segmentation and behavioral analytics. Joining customer (structured and clean) and product (clean) data with customer behavioral (unstructured interactions) data could show spending habits so it's possible to target the different customer clusters and build an effective marketing strategy based on wide data
insights.
Small data analyzes focus on deep diving in smaller and structured data sets from a single organization. It is extracted from existing and maintained data sources and databases. It is basically the opposite of big data as small data is not gained from big data sets. Data set around a product launch, how it is bringing the sales and through which channels in a certain period of time would be serving as a real-life example of small data as it takes on more individual and company-specific insights.
Both wide and small data approaches can be beneficial to organizations as they enable better scaling up in analytics and help to reduce dependency on big data. But still allowing situational awareness, providing a pulse check on the business performance and providing a helicopter view. Combined with the correct data strategy, these data sources can help organizations uncover useful insights in small and even micro data tables.
The volume of data exponentially grows day by day especially when companies move to the cloud, organizations will have to focus on small and wide data to support the business, establish data-driven culture and decision-making.