There’s a lot of buzz around Big Data and data analytics in leading supply chain organizations. But what’s all the buzz about? And how can supply chain organizations apply data analytics to improve their operations and outcomes?
In layman’s terms, Big Data is equivalent to “massive amounts of data” or perhaps even “all data.” It means leveraging the data available to the organization – whether generated internally or externally, both structured and unstructured. Unstructured data is often text-heavy data, which in the past was not easily consumable by systems, such as reports, news articles, and social media.
Data analytics is the process and technology used to make sense of large amounts of data. Data analytics enables your organization to engage with Big Data to answer your toughest business questions, solve persistent problems, and uncover patterns and insights. You can also use data analytics to drive innovation and pursue breakthrough ideas.
Let’s look at the different types of data analytics available to you:
Answer the question: What happened? Descriptive analytics helps your organization get in touch with reality, providing visibility and a single source of the truth. Apply descriptive analytics to provide greater levels of visibility across the supply chain, for both internal and external systems and data.
Answer the question: What might or will happen? Predictive analytics helps your organization understand the most likely outcome or future scenario – and its business implications. For example, using predictive analytics, you can project and mitigate disruptions and risks.
Answer the question: What should we do about it? Prescriptive analytics helps your organization solve problems and collaborate for maximum business value. Harness prescriptive analytics to collaborate with logistic partners to reduce time and effort in mitigating disruptions.
Answer complex questions in natural language – in the way a person or team of people might respond to a question. Cognitive analytics can actually help you think through a complex problem or issue. Use cognitive analytics to answer a query like: How might we improve or optimize X?
In assessing a question, problem, or issue to be addressed by data analytics, start with identifying a business pain point. Then look at the veracity, volume, and variety of data consumed and available to your organization. Determine what data is available and what data you may need to attain or access to answer a question or solve a particular problem.
For those starting to explore data analytics, you can start small and build a stronger program. Data analytics can have positive impact at any scale, addressing most any problem. The experience gained, and value derived, fosters a deeper data analytics program.
Data analytics is also a great foundation for application of cognitive technologies. Cognitive technologies, which are fast-emerging and being applied at many Fortune 1000 companies, understand, reason, learn, and interact like a human – but at enormous capacity and speed.
IDC predicts that, by 2020, 50% of all business analytics software will incorporate cognitive computing functionality. The Pew Research Center, which studied the current state of cognitive technologies (also known as artificial intelligence or AI), concluded: “By 2025, artificial intelligence will be built into the algorithmic architecture of countless functions of business and communication, increasing relevance, reducing noise, increasing efficiency and reducing risk across everything from finding information to making transactions.”
Supply chain organizations aspiring to work smarter can set a foundation for the cognitive future by growing with data analytics today.
For more information about cognitive technologies and data analytics, download the “Top Supply Chain Trends for 2017” report – available on the Watson Supply Chain microsite.
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