There’s no doubt that AI has usurped big data as the enterprise technology industry’s favorite new buzzword. After all, it’s on Gartner’s 2017 Hype Cycle for emerging technologies, for a reason.
While progress was slow during the first few decades, AI advancement has rapidly accelerated during the last decade. Some people say AI will augment humans and maybe even make us immortal; other pessimistic individuals say AI will lead to conflict and may even automate our society out of jobs. Despite the differences in opinion, the fact is, only a few people can identify what AI really is. Today, we are surrounded by minute forms of AI, like the voice assistants that we all hold in our smart phones, without us knowing or perceiving the efficiency of the service. From Siri to self-driving cars, a lot of promise has already been shown by AI and the benefits it can bring to our economy, personal lives and society at large. The question now turns to how enterprises will benefit from AI. But, before companies or people can obtain the numerous improvements AI promises to deliver, they must first start with good quality, clean data. Having accurate, cleansed and verified information is critical to the success of AI. The data that fuels AI-driven applications must be trusted, on time and of the highest quality.
Data Quality and Intelligence Must Go Hand-in-Hand
Data is currently used by organizations to extract numerous informational assets that are then used to assist strategic plans. The strategic plans dictate the future of the organization and how it fairs within the rising competition. Considering the importance of data, the impact that can be caused by low quality information is indeed intimidating to think of. In fact, bad data costs the US about 3 trillion per year. Recently, I had the opportunity to interview Nicholas Piette and Jean-Michel Franco from Talend, which is one of the leading big data and cloud integration company. Nicholas Piette, who is the Chief Evangelist at Talend, has been working with integration companies for nine years now and has been part of Talend for over a year. When asked about the link between both Data Quality and Artificial Intelligence, Nick Piette responded with authority that you cannot do one without the other. Both data quality and AI walk hand-in-hand, and it’s imperative for data quality to be present for AI to be not only accurate, but impactful. To better understand the concept of data quality and how it has an impact on AI, Nick used the help of the five R’s method that he mentioned was taught to him by David Shrier, his professor in MIT. The five R’s mentioned by Nicholas include:
If the data you are using to fuel your AI driven initiatives ticks off each one of these R’s, then you are off to the right start. All five of these hold a particular importance, but relevancy rises above the rest. Whatever data you have should be relevant to what you do, and should serve as a guide and not as a deterrent.
We might reach a point where the large influx of data we have at our fingertips is too overwhelming for us to realize what elements of it are really useful vs what is disposable. This is where the concept of data readiness enters the fold. Having mountains of historical data can be helpful for extracting patterns and forecasting cyclical behavior or re-engineering processes that lead to undesirable outcomes.
