Olivia Duane Adams, co-founder of Alteryx, explains how major brands are making the most of their data with self-service data analytics
Big data – a phrase often thrown around but seldom understood.
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 about as unhelpful as it is concise.
What is very clear, however, is that over the past decade, leveraging data has become essential for business success.
Research from NewVantage Partners ‘ Annual Big Data Executive Survey 2018 revealed that almost all (97%) of the 57 large corporations surveyed were exploring either data analytics or big data.
However, harnessing data it’s not always straightforward. With data skills in short supply and demand for data-related roles set to continue to rise within the next four to five years, companies are increasingly establishing a culture of data analytics and bridging the gap with the right technology to fit the business needs.
While some brands still face challenges to make the most of the data they hold, Coca Cola, Ford and Adidas are three global industry giants that are successfully unlocking the value of their data through the use of self-service data analytics.
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. “The business needs to understand every single step of our customers’ journey to continue delivering on our promise of happiness. I remember building out a prototype for ten stores using a self-service data analytics platform. It ran in less than one minute. I was standing at my desk jumping up and down, saying, ‘This can’t be right!’ People outside my office were asking if I was okay. I did a little victory dance and yelled, ‘It worked!’ I can’t even imagine how long it would have taken me to build and run that kind of analysis.” Caplan recalls.
How to deliver the personalised experiences that today’s customers demand
Using data analytics, Caplan was able to collate and summarise historical data and automatically generate and send over 600 personalised reports to restaurant owners. Caplan’s analysis enabled restaurant owners to understand and optimise their inventory, reducing out of stocks and increasing profit margins.
“One of my biggest wins as of late involves the Coca-Cola Freestyle machine, a touch screen fountain that allows users to create their own perfect mixture of flavours. Consumers love it because they have the freedom to customise and choose what makes them happy,” added Caplan. Not only that but from the data collected, the analysts receive a level of insight into the Coca-Cola experience that has never been possible before that can in turn help to influence customer-led business decisions and product roadmaps.
Ford – Democratisation of data
For Ford, the challenge was one of business architecture. Ford was faced with 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 (GDIA), 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.
How augmented analytics tools will impact the enterprise
Logistics and purchasing data was a critical challenge for Ford. The company tended to renegotiate parts, commodity and distribution shipping contracts as they came up for annual renewal. But shipping cost can change quickly as fuel prices, tariffs, customs, duty fees and available shipping capacity fluctuate. As a solution, GDIA codified previously manual data-collection and analysis processes into automated and repeatable workflows using an analytics platform. These enhanced analyses included geospatial data and visual workflows so all stakeholders in purchasing and logistics could analyse rates, routes and costs. The Rates & Routes rebidding process now operates on analytic steroids. “We’ve lowered the cost of our routes in North America and globally,” said Adam Blacke, lead data scientist at Ford GDIA. “We went from what we used to get in a single year in dollar reductions in shipping cost and we’re now doing that in slightly less than a quarter.”
Adidas Group – Actionable insight
Due to the size of Adidas, keeping track of all products on its e-commerce activity is a difficult task. Johannes Wagner, senior business analyst at Adidas Group explained, “across Europe, there are two brands (Adidas and Reebok), 17 markets and over 9,000 individual articles”. Combined, this creates a mammoth task with a huge number of data points.
This 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. Secondly, merchandisers were unable to perform in-season management of their items as they were not equipped with the appropriate solutions, meaning they had to contact the analysis team instead.
Clearing the blurred lines around real-time analytics
Today’s increasingly crowded and competitive business environment has led to an explosion in the popularity of real-time and near real-time systems, with enterprises across the globe devoting more and more of their attention to these issues
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 explained how the nature of analytics allowed merchandisers to perform data tasks and in-season management on their own without consulting analyst teams. This increased the teams’ independence and allowed the analysts to spend more time on high-value tasks. This, along with newfound granular product tracking, had an overall outcome of higher profit margins.
Big business needs big data
It’s no news that nowadays, big data is essential to business growth. It can be embedded into every facet of business but if not applied effectively, it can also cause problems. Having full access and visibility of collected data can be the difference between a high-performance organisation and a slowly failing one. As these examples demonstrate, data analytics is delivering tangible insights to improve both the business and consumer experience. Personalisation, democratisation and real value extracted from such large data sets would all be impossible without a culture of analytics and the analytics solution behind them.
Related: Data Analytics Trends in 2019
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