The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations. The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what's common among these errors is that they are related to humans reading, processing, analysing, and interpreting data.
You receive a notification on your phone that a critical shipment from your China factory has missed its filing deadline with the customs broker. Your logistics manager is alerted that there is an 80% chance that the components he's waiting for are likely to be delayed another 48 hours by excessive port traffic and your GTM software advises diverting the shipment to an alternate port facility. Your compliance officer is informed that there is a 95% chance that a shipment of parts from Malaysia is likely to be held for up to three days to be subjected to a detailed customs inspection. If you think this type of information would be of great assistance to your supply chain business planning and operations, you are not alone. It is this type of integrated data and communications that are becoming the backbone of the Big Data led revolution underway in supply chain.
Laura Timms, Product Strategy Manager at MHR Analytics gives us a breakdown of the future effects of artificial intelligence on the human resources space. According to a recent survey, 82% of HR leaders believe their roles will be completely different in a decade's time. Big things are happening, with Artificial Intelligence (AI) taking a starring role. More than a third of the 500 companies we recently polled said they had adopted some form of AI in the past year, and almost half of the HR leaders we surveyed said that machine learning – a form of AI – will improve their HR function. AI is already being put to work in key areas such as recruitment, onboarding and employee development.
More than half of the organisations responding to a recent survey stated that they weren't yet treating data as a business asset, with a similar number admitting that they weren't competing on data and analytics. This suggests these businesses are missing out on a huge opportunity – after all, when used correctly, the value of data to business is immeasurable. For businesses today, effective data collection and analytics can be key to maintaining a competitive edge – informing business decisions, predicting trends, optimising operations and improving efficiencies. Take finance teams, for example. From business travel to supplier relationships, invoices to expenses, they have access to a wealth of data, analysis of which could provide them with invaluable insight into the state of their business, and – perhaps more importantly – into trends upon which they could capitalise for future growth.
Political uncertainty, low productivity, a tech skills gap and slow wage growth are all factors contributing to an environment that is making it harder than ever to predict the outlying events that influence future trends. In the age of Big Data – of mega data – it appears counterintuitive that this would be the case. With all this access to information, shouldn't analytics be easier? The emergence of Big Data has brought with it huge positives. We now have a better understanding of how people live, how they spend their money, their approaches to services (e.g.