The digital world around us is supported by the evolving ecosystem of applications and services. Data flowing into this ecosystem is consumed in use case scenarios that help businesses drive better insights. The process of turning ‘crude’ data into meaningful information makes it analogous to the oil running our machines. Now organizations are striving to reduce technology debt by utilizing machine learning algorithms and statistical models to derive business decisions.
Amy Withrow, global Data Center of Excellence Leader, Kraft Heinz; and Harpreet Singh, specialist leader, Deloitte Consulting LLP, offer insights on how data, technology and the human experience combine to run a kinetic enterprise.
From books in the 80s, to Google searches in the 90s, to mobile devices in the 00s, the source of data has evolved significantly, and so has its value. Real-time delivery and volume has us drowning in data.
But we need the right technology to capture and process it, if only to prevent data lakes from becoming data swamps, but more importantly to drive decision-making. Says Singh, “If there is no enhanced technology, no advanced ways of fetching the information from this data around us, then it’s of no use. Technology is the enabler to get us to the right decisions.”
Big data means the opportunity to convert patterns, analogies, and algorithms into meaningful perspectives for business. And technology like machine learning and AI is processing data to help companies build better detail and processes to make decisions at pace – while developing new data. Withrow offers a supply chain example. “We can see the temperature of our trucks, speed, track our inventory and send data back out to make the processes better.” Having the right technology means the ability to interpret valuable data, generate insights and react quicker. “That’s so key to keeping up that value of data.”
While data is driving business decisions, it’s also driving the human experience. The consumer products industry uses advanced technologies to interpret vast amounts of data and purchaser sentiments (think: product reviews) to help refine products and define market patterns. Medicine is using data to help us live longer lives. Singh cites a Harvard studythat combines pathologists’ analyses with an AI approach to improve accuracy in breast cancer diagnosis to 99.5% accuracy rates. “Data has changed lives of people and AI is a big deal in this.”
Internet of Things gives businesses opportunities to collect real-time an increasingly detailed data about the habits, lives, activities, and preferences of consumers – whether or not we’re aware of it.
The dark world of data selling and data brokers notwithstanding, Singh points out there’s good at play in that our outflowing data benefits others. Withrow offers how her car’s GPS gives her the best route based other drivers’ data to save her time, and a similar feedback loop helps businesses: “Trucks are feeding that data back and giving companies the right insight to fix issues quickly. But they are also helping strengthen the organization’s master data which is a core foundation.”
Unstructured data oceans
In 1998, Merrill Lynch posited unstructured data comprised the vast majority of any company’s data; in 2012, IDC and Dell EMC projected it would hit 40 zettabytes (one zettabyte = one trillion gigabytes) ; estimates now hold the global data sphere will grow to 163 zettabytes by 2025. We are awash in oceans of unstructured data.
But not adrift. AI and machine learning are part of the kinetic enterprise technology backbone and the only capabilities that can possibly turn mind boggling amounts of data – structured or unstructured – into valuable insights. Data is the new oil in the engine that runs companies, driving revenues and value, helping solve problems, and create better human experiences.
Want more transformation insights from enterprise leaders? Visit deloitte.com/SAP to download future podcast episodes or listen to previous ones.
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