Why the Key to AI Success is a Tidy Data House
Strong data management is critical to predictive and AI technology.
Despite all the talk about artificial intelligence (AI), adoption has yet to reach its pinnacle. Many organizations are taking smart steps to implement new technologies. Instead of buying into the hype, they ask critical questions to garner the strongest ROI, resulting in a delay in broad adoption.
Unfortunately, this is standard within the market. Organizations tend to struggle to get new applications of technology off the ground. For example, security considerations kept many organizations from adopting cloud technology, and with business intelligence (BI) in general, most adoptions follow similar paths, as companies create solutions but struggle to gain value from their endeavors.
Strategic organizations have realized strong data management is a core foundation for predictive and AI technology and are therefore focusing on getting their data house in order.
Over the last few years, the focus has been on dashboards and data visualizations. Data scientists and analysts created numerous views of the world and ways to gain insights, but organizations have struggled to manage and analyze all available data. With a multitude of sources — from internal and external customers, consumers, partners, and suppliers — organizations have found it difficult to create a single view of the truth. AI has the potential to support stronger data management initiatives and address a human’s limited ability to accurately analyze and spot trends in the mass of data that now flows through the modern enterprise.
Early adopters of AI and machine learning (ML) must understand the underlying requirements to ensure project success for all implementations — not just those aimed at improving internal data initiatives. Organizations look to build AI models but have not always aligned these goals with strong data management or the complexities required to create strong AI outputs. They need to understand potential biases in their data and whether they have enough data to provide valid and reliable outcomes.
Taking full advantage of AI and ML requires an understanding of the data, where it resides, what related data is required, and, finally, what initial business questions exist.
What You Should Be Doing Now
Data management is central to the emerging technologies puzzle. To this point, most organizations have faced one or more data quality problems, but the amount of data now flowing into the enterprise magnifies the issue and increases your need for a solution because as more processes are automated, inaccurate data becomes exponentially more damaging.
