AI is seen as an enabler for enhanced customer marketing, it enhances the speed, precision and effectiveness of human efforts. In financial institutions, for example, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks.
There are many types of artificial intelligence including machine learning, where instead of being programmed what to think, machines can observe, analyse and learn from data and mistakes just like our human brains can. This technology is influencing consumer products and has led to significant breakthroughs in healthcare and physics as well as altered industries as diverse as manufacturing, finance and retail.
Data underpins all AI projects and therefore proper data management is a must not only to guarantee the reliability of AI and decisions made as a result of it but to reduce the risk and cost involved in delivering and sustaining an AI solution. Comprehensive data management means understanding and controlling data as it moves through your systems even when it’s in relatively static storage. Master data management systems can bring clarity to the processes and help to find and resolve problems as well as address several data problems that would otherwise have to be addressed by a typical AI data pipeline.
In this webinar we will discuss the inherent risks involved in any data initiatives, in particular AI, where the root cause of the risks is and how tech and technique like MDM mitigates the risk and reduces cost for an enterprise.
This webinar is aimed at CDO’s, Heads of AI & ML, Heads of Analytics, Heads of Data Management and everyone else who is interested in the successful use of AI.