Have you ever found yourself in a foreign country, unable to understand the local dialect? Just load a translator app on your smartphone and use it to listen and translate speech into your own language, and vice versa. Speech recognition is just one example of machine learning in action.
Machine learning, true to its name, is a method by which a computer learns from data to build an analytical model. The model can identify patterns in a dataset similar to the training data and make decisions without human involvement. Continuing with our speech recognition example, translator applications are trained to recognize words and their meanings. When the app finds a similarity between what a person is saying and the phrases/commands it has already learned, an accurate prediction can be made as to what the words mean in another language. Essentially, the app is fed data (speech) and makes a prediction (language and translation of words). Machine learning is revolutionary because it removes the need for a human to develop a model. Instead, the model is developed based on training data. The ultimate key factor for a successful machine learning model is a plethora of clean data. (You can read our previous article on data hygiene here.)
Machine learning has come a long way since its inception, but pattern recognition has always been foundational to its success. As machine learning has increased in relevance, the speed at which patterns are recognized has also increased to the point where it will take a program mere milliseconds to complete its said task. Previously machine learning models would take minutes to comb through 100s of records. Current models can handle millions of records in seconds. This advancement is of the utmost importance as we face the never ending growth of collected data.
Another evolution of machine learning is the ability to adapt to new training data. For instance, if new words are added to the dictionary, speech recognition models can adapt from that training data and recognize these words in speech. It is precisely this aspect of machine learning that makes it such an important tool. In the past, every time new patterns were introduced to a model, the patterns would have to be manually coded. Today’s models can adapt independently which removes the tedious task of manual updates.
A common misconception about machine learning is that it is solely utilized in the technology space. This could not be further from the truth. It is a fact that hi-tech companies adapted this technology very early on, but machine learning can be applied to a wide variety of applications and industries. For instance, nonprofits have been eyeing the technology as a solution to some of the limitations they face with staffing and finances. These organizations tend to collect or have access to a lot of data, both for research and fundraising purposes. At the same time, they don’t have access to the expertise and personnel available in the commercial sector due to their limited budgets. Machine learning models can comb through these large datasets and complete tasks that would otherwise consume hundreds of hours of human labor. Essentially, this technology removes middlemen and streamlines the process of data analysis for a variety of organizations.
DataGroomr leverages machine learning to identify and remove duplicate records in Salesforce databases. Say there is a Jane Doe that has filled out a survey twice. DataGroomr will identify that the two Jane Doe records are indeed duplicates and allow you to merge them into a single record that retains all the core data from both records. Machine learning makes this whole process seamless and quick. Essentially, DataGroomr offers curated models to identify duplicate records based on a user’s previous choices. As a user identifies and removes duplicates manually, DataGroomr recognizes patterns in how duplicates are being identified and removed. Going forward, DataGroomr will automatically mimic such pattern recognition and retain/remove the appropriate records.
DataGroomr takes out the requirement for a human being to manually analyze thousands of records and speeds up the process exponentially. Furthermore, DataGroomr’s identification model continues to adapt as the user corrects and assesses the duplicates found. Returning back to our non-profit example, DataGroomr can save such an organization countless hours by going through available donor data and removing any duplicates. This also has potential for significant cost savings in fundraising efforts.
Although the task of removing duplicates is not extremely difficult, it is just a fact that humans cannot work at the accuracy and speed of a computer. Machine learning removes human error and greatly decreases the time necessary to complete such tasks.
