Data for Enterprise AI: at the very forefront of innovation

2020 may well go down as the year where what seems impossible today, did become possible tomorrow. It’s been a year filled with disruption and uncertainty. One day we were all going to the office, and the next we were working from home. Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things. But out of disruption, we’ve seen incredible innovation born into the enterprise. Machine Learning (ML) and Artificial Intelligence (AI), while still emerging technologies inside of enterprise organisations, have given some companies the ability to dynamically change their fortunes and reshape the way they are doing business — that is if they are brave enough to experiment and explore the unknown. The imperative to deliver meaningful change and value through innovation is why the Data for Enterprise AI category at the Data Impact Awards has never been more of the moment than it is today.

But before we look forward to what’s to come, let’s take a look back. Last year’s winner, in the Data for Enterprise AI category, United Overseas Bank Group (UOB) is an excellent example of using ML and AI to both drive innovation and deliver meaningful change. UOB used deep learning to improve detection of procurement fraud, thereby fighting financial crime. The company’s advanced AI models can today detect suspicious transactions and rank these transactions with a score so that fraud investigation teams can best prioritise cases that require immediate mitigation — something that’s imperative as business team members work remotely. But UOB didn’t stop there. The organisation also wanted to ensure better customer service. Through its Enterprise AI and Data Science (EAI) and Digital Bank Engagement Labs (eLabs) UOB collaborated to implement AI and data science solutions to understand customer behaviours and characteristics from transaction and interaction data. This has helped UOB to deliver more personalised features and services as part of the TMRW digital banking application. 

Personally, it’s examples such as UOB — ones solving real-world problems and delivering value through data analytics while employing ML and AI — that makes this category really exciting. This year, we hope to see even more stories of ML and AI driven innovation among the finalists. We’re looking for companies that have found interesting ways to automate their business and ones that are finding more effective ways of simplifying their operations. We are especially looking for businesses that are thinking about solving futuristic problems, today. We want the winners to really think about what the real-world business issues they are facing, and showcase how they can use powerful analytics to solve these challenges while also taking their entire business on this journey. 

Given the year we’re experiencing, we’re also looking forward to future entries for next year. We believe this year will deliver some real treasures in terms of ML and AI innovation. So when working on your new projects keep in mind that successful implementations require:

  • An almost pioneer spirit and a positive mindset — You have to believe that you will be able to solve problems in new ways. In fact, this belief is critical. But you also have to encourage the right mindset. It is not, and cannot be about just the technology being used. It’s about how you approach AI and ML. Organisations that are successful in this space take the time to reshape the way their organisations and people think about analytics and investment into it.
  • Buy-in at every level — Contrary to popular belief ML and AI are not just for the techies amongst us. They really are a team sport. They require buy-in from the executive level and they require the right people with the right knowledge to be there. Most importantly, they require the right buy in from everybody in the organisation to really think AI-first. 
  • Acceptance that it will be an experiment — ML really requires a lot of experimentation, and often times you don’t know what’s going to be successful. So, the business has to accept and be willing to fail at it. That’s really important. It’s a willingness to understand that not everything you do is going to be successful. But understanding that the things that do work are going to work really, really well and potentially solve problems that hadn’t been solved before.
  • Down to the technology — Last but not least, the technology you choose plays a critical role in your road to innovation. It has to be able to encompass the entire data lifecycle, and you have to be able to employ AI and ML across all the various incarnations of the data, with no silos. The right technologies that are specifically designed for these types of very large workloads, very comprehensive use cases, is absolutely essential. You need something that’s secure, governed by something that isn’t going to compromise your security. This will inspire trust in the organisation, that all their data and information continues to be secure. This in turn will allow them to trust the outcomes of these processes. So again we find ourselves at the intersection of the right technology and the right people and mindset.

At the end of the day, with the right attitude and technology in place, the possibilities for ML and AI at this moment in time are endless — almost magical. We can’t wait to read your entries and showcase how Cloudera customers are changing entire industries for the better. Good luck!

For more on past winners and finalists, visit the archive site. And keep an eye on this year’s awards at www.cloudera.com/DIA. We hope to see your entry next year!

Santiago Giraldo
Santiago Giraldo

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