In simplest terms, machine learning occurs when AI algorithms process and analyze large volumes of data to provide insight not previously known.
If you want to see machine learning in action, look no further than Amazon, Audible and Netflix. All are analyzing data in real-time—learning from your searches and purchases and comparing them with millions of other transactions to provide you with suggestions and recommendations based on your interests, needs and tastes. These “smart” systems are providing the same services to everyone on these websites simultaneously.
Automated systems that use machine learning can take over many mundane processes while providing customers with lightning-fast response rates for services that only a few years ago would have been impossible, such as 24/7 online customer service.
Inside the sales department, machine learning is at the heart of “guided selling” strategies that score sales opportunities by likelihood to close, the america cell phone number list value of the deal, projected lifetime value and more. It’s at the center of predictive sales forecasting, finding new market opportunities, documenting best practices that will improve sales reps’ productivity and developing optimal pricing strategies.
Machine learning replaces intuition with insight. But it all requires clean data.
Steps Companies Can Take to Deliver Clean Data
How clean does your data need to be? The simple answer is the cleaner, the better. As the saying goes, “Garbage in, garbage out.”
Whether or not you believe data is the new oil, there’s little doubt that clean data is your competitive edge. It can deliver better customer insights, more accurate forecasting and highly predictive lead scoring. Also, it can provide prescriptive sales processes that cut the time spent chasing leads and focus reps on serious prospects. Thus, clean data can allow your salespeople to spend more time building relationships with prospects and customers.
Traditionally, dirty data has referred to any data that is incomplete, rife with misspellings and missing fields, out of date, or includes many duplicates. But we need to expand our thinking to consider the data itself. For example, will it add to a machine’s knowledge base? Which data sets are necessary for machine learning? What do you do with outliers, for example, values that fall way outside the norm?
And then there is the question of how data is structured and managed. It’s not only critical for the best machine-learning results. It’s also about security and privacy—both within your company and to comply with GDPR and the California Consumer Privacy Act.
While your data engineers are likely responsible for the work to set up data management, governance and cleaning protocols, you must have a sense of the processes involved. Ultimately, the results will have a direct impact on your sales.
Here’s a brief list of what it takes to make data clean enough for a machine-learning world:
