Lost in translation
"Data science is a composite of a number of pre-existing disciplines," so Wikipedia tells us — including data engineering, maths, statistics, advance computing, and visualisation, to name a few.
In fact, the term 'data science' was coined only in 2001 and exploded as a field in the 2010s. Meaning, it is still a nascent industry. Shiny. With a new-car smell! However, it also comes with all the familiar teething problems of a new school of thought. Least of all, a thorough understanding of the word data itself.
Regularly, 'data' is mistaken as simply a table, database, or some sort of technology infrastructure project (à la an ERP system roll out). Or – worse still – an IT cost! Whereas, in a business context, data could be:
• Point of Sale & transaction information
• Customer/client database(s)
• Supply chain tracking
• Stock, Warehouse/DC indicators
• Equipment & maintenance logs
• Logistics/transport tracking
• Production line & performance indicators
Seen in this broader context, it is evident why gathering, mining, and augmenting a company’s data assets to transform them into value-enhancing insights is a strategic imperative.
Yet, so few are using data beyond its basic functionality.
At DataDiligence, we partner clients to cross the ‘analytics chasm’. Shifting data usage from traditional, business intelligence outputs (such as rear-view-mirror reporting) to predictive, data-led insights which optimise operations, enhance top-line performance, and sharpen competitive edges.