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TECHNOLOGY
Supercharging Organizational Decision Making with Artificial Intelligence BY DAN HIGGINS
T
he far-reaching effects of economic volatility, social and political upheaval, and global health emergencies are being felt by organizational leaders under intensified pressure to produce results. But even under ideal circumstances, leaders know that making quick and confident decisions can be extremely difficult, particularly when they do not have access to trustworthy data. In fact, recent data from Gartner uncovered that 65 percent of leaders feel they are forced to make more complex decisions today compared to two years ago. Moreover, the same survey found that over half (53 percent) of those surveyed noted a greater need to justify or explain their decisions, highlighting a clear gap between hastened automation and a thorough comprehension of what is being automated and why. Individuals and businesses are under pressure to make fast, consistent, and fact-based decisions, so called ‘decision ❱ DMN.CA
intelligence’. And it’s here where we often see enterprises deploy technologies such as artificial intelligence (AI) and machine learning (ML) to augment their decision-making abilities to do just this. However, one of the most significant hurdles customers face is that organizations frequently attempt to deploy these technologies without fully comprehending the importance of context, or in other words, without a clear idea of the bigger picture driving what data is collected, examined, and how it is applied for decision making. Without context, AI predictions and decisions lack strength and dependability, potentially ushering in a range of long-term automation challenges and other setbacks. Enter entity resolution — the process of parsing, cleaning, and standardizing data by using advanced AI and machine learning models to accurately identify entities. This process connects records related to each entity, creates a list of attributes for each entity, and generates labelled
links between entities and source records, and is significantly more efficient and effective than the conventional record-to-record matching method used by MDM systems. A single source of truth: Leveraging quality data for AI business value When it comes to AI and ML, the data you use is everything. That’s why data scientists are laser-focused on using reliable and transparent data to make the best algorithms possible. For instance, if you build a classifier to distinguish between photos of a raven and a crow, data scientists would ideally like an input image dataset certified by an ornithologist. If they are unable to source this, then the obvious next best place to find this might be online. But this is where the risks of input errors and misclassification begin to emerge. Another challenge is presented by inconsistent data entry, wherein a single entity may be referenced by varying names. For instance,
take actor and comedian Jim Carrey. Hi name may appear in its full form, James Eugene Carrey, just as it may be listed as James Carrey, Mr. Carrey, or in a like manner. This also holds true for companies, which can be referred to by their full legal name or an abbreviated form. It is imperative that the algorithm can recognize and learn from a multitude of diverse names and formats. The successful operation of the algorithm hinges upon its ability to recognize and learn from a broad spectrum of names and formats. To make accurate distinctions between names and said formats, the algorithm must possess the capability to learn from a complete range of them. By harnessing powerful AI and machine learning algorithms, entity resolution efficiently processes, structures, and divides data to identify like entities in a comprehensive approach. In contrast, the standard record-torecord matching methodology that most MDM systems use is quite outdated. But with entity APRIL 2023