Historically, organisations gathered siloed data about their customers, products and processes as a side effect of their operations. Then, as analytics software became both more prevalent and powerful, some began to combine that data in order to turn it into meaningful information informing their decision-making. This more sophisticated approach to data is also being driven by the shift from product focus to strong customer focus. While some organisations are realising the first business benefits, other organisations are now starting to revamp their data strategies to ensure that they are extracting as much value as possible from the information that they gather.
Today, organisations are looking at their data from a different point of view, and are putting analytics – and the useful insights they generate – front and centre when designing any system that captures data.
Recent research by Cognizant's Center for the Future of Work found that 99 percent of companies globally believe Big Data analytics – making meaning from business data and information – will have a significant effect on work by 2020.
When companies create a new product or service, or update an existing one, they should ask themselves: 'What do I need to learn from this new business model, and what data do I need to analyse whether that business model is really working or requires optimization?' Data and analytics are integral parts of every new offering.
With data, which end of the telescope companies look through can make a huge difference to the business models that can be enabled further down the line.
When organisations query existing data, they learn about how their business works now, and potentially how it can be improved in the future. But when they put the analytics first and foremost, organisations can use data to do business better straight away.
Take the case of machine learning, for example. With the right analytics, aligned with business processes, machine learning can be used to streamline operations without the need for human intervention or querying the data manually. Systems that have machine learning embedded can take the data that the organisation generates, combine it with external data, confront it with feedback on whether previous actions were successful, and use algorithms to continuously learn and refine the best course of action.
Artificial intelligence takes those efficiencies and supercharges them. Rather than simply studying data and refining a single existing process, AI opens the door to systems that think for themselves. Artificial intelligence was once considered futuristic but organisations are already adopting it for uses from enabling vending machines to have smarter interactions with customers to creating chatbots that can interact with clients autonomously. By designing the appropriate data handling systems today, organisations can enable a new generation of solutions that will take their business to the next level tomorrow.
There can be no doubt that data is a resource: learn how to use it right and there's no telling how much value it can create.