How big companies triumph with self-service data analytics
Posted on 03 May 2019
Big data– a phrase often heard by many a lot but hardly understood by anyone.
Gartner defines big data as “high-volume, high-velocity and/or high variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making and process automation.” While detailed, this explanation is not that helpful or succinct. However, it is clear that over the past decade, leveraging data has become essential for business success.
Coca-Cola- Personalised analytics- During his first year in business, Coca-Cola inventor Dr John S. Pemberton sold approximately nine glasses of Coca-Cola a day. Today, over 1.9 billion Coca-Cola beverages are enjoyed daily around the globe. Coca-Cola uses a high-performance data analytics platform to merge, prepare, and analyse data from multiple disparate sources and make the insights accessible across the organisation. It’s the key to unlocking the value of their data. “Every group in the business is dependent on data, from audit and finance to production and delivery” explains Jay Caplan, senior business analytics manager at Coca-Cola.
Ford- Democratisation of data- For Ford, the main challenge was of business architecture. Ford faced problems such as analytics expertise being contained within pockets of the organisation and some business units lacking access to this analytics expertise. To solve this, the company created its Global Data Insight and Analytics, a centralised unit to work with all aspects of the business. As part of this, advanced analytics was brought on board to help democratise data analytics through its self-service capabilities. Logistics and purchasing data was a critical challenge for Ford.
Adidas Group– Actionable insight- Due to the size of Adidas, keeping track of all products on its e-commerce activity is a difficult task. High volume of data means that, before implementing data analytics, the workload was very high and all areas of the business were being pushed for time. The solution was based around the collection of transactional data from multiple sources and enabling this data to dictate the direction of business decisions through analytics insights. Using an analytics platform, Wagner, senior business analyst at Adidas Group, explained how the nature of analytics allowed merchandisers to perform data tasks and in-season management on their own without consulting analyst teams.
Big business needs big data- From the above examples, one can surely say that big data is very important and necessary for business growth; and for handling large data, analytics’ knowledge is highly required.