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Machine Learning is Building Better Salesforce Systems

Our interactions with machine learning change the way we think about computers. Rather than simply automating activities such as data processing, machine learning attempts to reproduce how humans think. Once properly trained, computers can take over decision-making with minimal human intervention.

When it comes to Salesforce systems, machine learning can assist users in managing their data and facilitate developers in creating more efficient applications. As James Ward, a platform evangelist at Salesforce, summarizes in his video Adding Intelligence to Salesforce Systems with Machine Learning, saying, “We put data in and ask the machine to give us a prediction back.”

In this example, Ward is using machine learning with PredictionIO to build better Salesforce AppExchange applications. How does he do it? Simply put, it’s pattern recognition via ones and zeros. Start with data, create an algorithm, apply that algorithm, which then makes decisions based on what it sees in the data. Voilà, you get a prediction. Let’s dive deeper.

Finding Differences in Similarities for Cleaner Salesforce Data
Based on the above definition, we can say that, generally, machine learning is a technique for taking data inputs and turning them into predictions. And when it comes to applications in Salesforce, machine learning offers an opportunity to clean up data — and keep it clean. Especially when it comes to duplicate management, machine learning can weed out duplicates (or those records with similarities) accurately and quickly.

First, it’s important to understand what is meant by similarities in our data. Essentially, there are three types of “similarity”:

String similarity: if the sequence of characters between two names is similar then the meanings of the names is likely to be similar.
  • Ex: Stephen/Steven/Steve
Set similarity: If a name contains many of the same words, then the meanings of the names are likely to be similar.
  • Ex: Ray Jones/Raymond Jones/Raymond Robinette Jones
Semantic similarity: If two names are surrounded by similar words, then the meanings of the names are likely to be similar.
  • Ex: Margaret is a Salesforce admin at Ajax/Maggie works with Salesforce at Ajax Corp
When there are multiple similarities, we can conclude that some or all of them are duplicates. And duplicates in the data degrade the integrity of our database. But plucking out the duplicates can be tedious and time-intensive … unless we can apply algorithms to do the deduping for us.

How Machine Learning in Salesforce Makes Data Integrity a Priority
The Salesforce Platform is no stranger to the power of machine learning to transform the efficiency of data management across a variety of business landscapes. As noted in Smart Money: AI and Machine Learning Are Changing Business Forever, AI can be “put to work automating finances and balancing budgets, detecting anomalies and managing large volumes of finance data easily.” It is in that data management that machine learning really shines in Salesforce applications.

For example, Salesforce Einstein was the first fully inclusive, integrated AI for CRM. Einstein empowers businesses to employ machine learning and advanced deep learning concepts to all of their processes and data — customer, industry, and in-house — for increased accuracy and automation. That accuracy in data is important because, according to Forrester, 60% of companies have an overall data health that is unreliable. But increasingly, machine learning models are being applied to discover complex patterns in the data with a goal of combatting those areas of unreliable data.

As James Ward describes in his video, noted above, pattern recognition is crucial in improving data integrity. By providing a forum for machine learning predictions, the Salesforce AppExchange can assist users in improving the quality of their data.

For example, currently, the AppExchange is populated by deduplication apps that mostly have one thing in common: they are all rule-based. But the landscape is changing. The AppExchange offers an opportunity to tap into a resource that enables users to enhance and customize the functionality and usability of the SFDC platform. By increasingly applying machine learning, apps on the AppExchange streamline not just deduplication but also signing documents, streamlining permission management, and improving the overall health of data.

Clean Salesforce Data Means Better Business & Happy Customers
Every company’s dataset is unique and has its own challenges. Understanding the current state of data hygiene is the first step to cleaning data. The way to do that is to run a data quality assessment. Whenever a human determines that a set of records is duplicates, the system that has been trained on machine learning will “learn” from these actions and tweak the algorithm with the goal of improving the identification of future duplicates without human interaction. This process, known as “active learning,” will continue to modify the weights assigned to each field based on user interaction and consequently improve duplicate detection.

In this way, businesses can use machine learning to recognize patterns and then make predictions about what will appeal to customers, improve operations, or help make a product better. Machine learning applied in Salesforce systems is the pathway for companies to dramatically improve their products and services with the end result of improving the user and customer experience.

​​​​​​Steve Pogrebivsky, the president and co-founder of DataGroomr, is an expert in data and content management systems with over 25 years of experience.

​​​​​​This article originally appeared on AppExchange and Salesforce Ecosystem.

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