Not too long ago, artificial intelligence (AI) and machine learning was something that only really existed at universities and big tech companies. It was difficult to find enough servers and computing power to deliver on the promise of AI and machine learning. Furthermore, it was also a problem finding enough data to derive true value from AI solutions. But things move quickly - particularly in the technology space. 

Today, even consumer laptops are capable of using AI to analyze vast quantities of data. Everyday examples of AI and machine learning include smart personal assistants like Amazon’s Alexa and fraud detection solutions used by the banking sector. Part of the reason why these tools are becoming more ubiquitous is that they have access to more data than ever before. Estimates indicate that 1.145 trillion MB of data are created every day (consisting of, amongst other things, 306.4 billion emails and 500 million Tweets).

For businesses, it is impossible to overstate the promise hidden within this vast quantity of data. If organizations can connect the dots correctly, this data has a fascinating story to tell - one that could set them up for long-term success.

Expect the unexpected

At first, the volume of data created by sources like social networks, IoT sensors, software platforms, as well as countless other digital tools may not look of much importance. It can be difficult to discern any value at all from a mass of numbers in a spreadsheet. In a large data set, a human observer may not find a single valuable correlation. However, a correlation matrix leveraging machine learning could discover something that is worth investigating further in the blink of an eye.

The kinds of connections that AI can identify in large data sets will cause businesses to ask questions that they would never have previously thought of, answer them in ways that they could never have anticipated, and deliver innovations that would have seemed unimaginable not long ago.

Some businesses are already making the most of data-driven innovation. For example, when employees at multinational engineering firm Smiths Group found that they had to manually extract data from 800 different applications to gain a holistic view across the organization, they realized that unnecessary bottlenecks were being created. As a result, the company decided to pool its resources into a ‘data lake,’ allowing staff to make faster strategic decisions and come up with new innovation opportunities. 

Other examples of data-driven innovation are not hard to find. The German e-commerce retailer Otto has implemented a big data strategy, including a proprietary algorithm called “Neurobase” to automate its dynamic pricing, improving sales planning by roughly 40% and saving Otto more than €10 million. With so much data available, more businesses should be searching for these hidden benefits stored within.

The human touch

Although more organizations are aware of the power that data has to revolutionize their operations, many are still not tapping into this potential. According to a Harvard Business Review report, 91% of business leaders agreed that effective data and analytics strategies were essential, yet only 20% rated their organizations as mature in these areas. The problem is not necessarily one of technology though. 

The field of data science is still relatively young - in business terms at least. Five years ago, job postings for data scientists did not really exist. Companies used data analysts or business intelligence specialists, but hiring a data scientist was rare. Slowly, the importance of data scientists is gaining greater appreciation - these are individuals that can carry out all the number-crunching required to engage with large datasets but, crucially, they can do so in a business-savvy way. They do not simply provide a spreadsheet containing thousands of numbers - they explain the story behind the data in a way that non-technical employees can understand.

The purpose of data-driven innovation is not to make human creativity obsolete; it is to bring together data, digital technologies like AI, and individual creativity to ignite new possibilities, the likes of which would scarcely have been possible just a few years ago. 

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