Companies in the tech industry are always looking to push the boundaries of what self-determining computers are capable of, especially given all of the latest developments in AI and machine learning. We now have models like GPT-3 that can compose texts eerily similar to a human, while companies like FedEx and Amazon have developed an army of AI-powered delivery robots, voice recognition systems, and other cutting-edge products. The question is, would these sort of technologies fall under the category of automation or machine learning? Or is there really a difference between the two? Let’s explore this in greater detail.
Machine Learning vs. Automation for Deduplication
Machine learning is actually one of several computer technologies that mimics human functions such as “learning” and “problem solving” and is commonly grouped under the label of Artificial Intelligence. Machine learning is a later-stage development where systems are able to take in data on their own, analyze the information, and identify certain signals or patterns relevant for future outcomes. As far as deduplication in Salesforce is concerned, the machine learning system will be able to calculate and remember the “weights” given to each field inside records and use this criteria to identify future duplicates. This is something we covered in detail under a previous blog post, How Machine Learning Algorithms Get Duplicates in Salesforce.
Automation is frequently confused with AI because both were created to streamline tasks and speed workflows. The biggest difference is that automation is fixed solely on repetitive tasks where a machine will perform a prescribed action and nothing else. To be fair, most of the deduplication tools on the AppExchange offer a high level of automation by running rule-based deduplication jobs. The repetitive jobs identify duplicates based on static matching rules which are executed regardless of changes to the underlying data.
For example, imagine one of your sales reps discovers a duplicate and notifies the Salesforce admin about this issue. The Salesforce admin will then create a new rule to prevent such a duplicate from recurring. Each time a new duplicate scenario is identified, the same process must be repeated because the system is not capable of learning and spotting duplicates on its own. Basically, instead of you having to manually compare every single field, the system will automate this process for you. Therefore, like we mentioned above, it does a specific job and that’s it. No matter how persistent you are, it’s virtually impossible to create rules for every possible duplicate scenario. This is where the machine learning approach has a major advantage. So, how do you start? Let’s explore this next.
Building AI Systems Using a Pyramid Model
When data begins interacting with the technology, it is referred to as “instrumentation”. At the instrumentation stage, technology is used to observe or measure the digitized data as information moves between systems or individuals. It is worth noting that the system is working with only the data it possesses, without producing any new insights. At this stage, automation and AI complement each other as automated machines collect the training data and pass it along to the next stage where the AI tries to understand it.
The analytics stage is reached when data science and mathematics begin to manipulate the digitized and instrumentalized data. Analytics permits meaningful insights to be gleaned from big data to lead organizations though the process of decision-making.
3. Machine Learning
Machine learning starts when the programs are able to take the analytics and apply them without any explicit programming. In fact, to a certain degree, the outcomes of machine learning will be independent of its programming. The machines are now able to take in new data, analyze them on their own, and improve the results in ways that exceed what an analytic model can provide. The algorithms will gradually keep improving through experience, basically learning on the go. This is an essential aspect of any AI model with practical applications across many areas.
4. Artificial Intelligence
AI is the apex of the pyramid that we mentioned in the very beginning of the process. A big part of AI is based on machine learning. However, if we look at it in a much broader sense, AI transcends machine learning by creating capabilities that were previously reserved for only humans, such as visual processing and language comprehension.
How Does Automation Match Up with AI in a Head-to-Head Comparison?
Although automation can complement AI, they are at different levels of evolution. Simply put, if a certain job can be performed by automation, there is no need for AI. Automation can be traced back several centuries to the industrial revolution with the iconic Henry Ford’s assembly lines, and to even earlier times when traditional milling was automated with river water spinning a wheel, which would then turn the millstone. Although seemingly primitive, simple automation is something that organizations heavily rely on. For example, automatically sending out invoices as soon as all of the inputs have been confirmed by the accounting department.
Automation is simply replicating human tasks, whereas AI allows machines to approach replication of the human thought process. This translates to machines being able to reflect on its own procedures and make decisions outside the scope of its own programming.
AI is the Best Approach for Deduplication
Since deduplication is a very tedious and time-consuming process, we want to reduce (or even eliminate) human involvement, but we also want to go about this in a very smart way. If all of your duplicates were carbon copies or the exact same field inside every record had the exact same mistake, then deduplication could be solved with simple automation. However, given the wide variety of duplicates, you need a deduplication tool that can think and reason like a human. This is why AI is the best approach.
One of the biggest advantages offered by machine learning is that it does all the work. There is no need to set up or maintain any matching rules since the system learns to identify duplicates based on user actions. As information is collected, each relevant field will have a “weight” assigned. The more information collected, the more accurate this weight becomes. When implemented properly, the process can be highly accurate and scalable in detecting duplicates.