Approximately 2.5 quintillion bytes of data is generated every day from countless sources such as social networking sites, smartphone apps, e-commerce transactions, and martech platforms. Considering the massive volumes of data available today, data scientists need automated tools to help them with data preparation and other repetitive tasks. And thanks to advanced technologies like machine learning and APIs, much of the mundane tasks necessary in data science work can be automated. In fact, Gartner estimates that by 2020, 40% of data science tasks will be automated which will boost the productivity of professional data scientists.
Today’s post highlights how Zylotech can help data scientists in the marketing technology industry boost their productivity by automating many data preparation tasks.
Automation boosts the productivity of data scientists
Every data scientist worth their salt knows that data prep is nearly 80% of the battle. Nearly every data scientist spends hours, often days, completing tedious, repetitive work such as combining records, deduping data, and connecting related records. Data science should not have to mean spending most of your time on tedious, repetitive data prep work. Automation allows data scientists to get to the actual data science work quickly.
Today, data scientists can find numerous general-purpose automation tools that can be used for a variety of non-specific, basic use cases like data integration, data cleaning, and data enhancement. Some automation tools specialize in industry-specific automation. For example, Zylotech is a customer data platform (CDP) that automates much of the marketing workflow, from integrating multiple disparate customer records to enabling machine learning-driven customer analytics. Our platform not only automates the repetitive tasks marketers would rather not be doing but also automates the repetitive tasks most data scientists do not enjoy doing.
How does Zylotech help data scientists?
Zylotech provides data scientists with validated, verified, and clean marketing data sets. Our platform cleans and standardizes data pulled from multiple disparate sources automatically. Data scientists do not have to worry about duplicate records, figuring out if similar records are related, or combining related records.
Data scientists can skip the marketing data prep and perform whatever type of segmentation they require that may be different from what we support in our platform. Zylotech ensures that when marketing data is pulled from multiple sources, data scientists are given clean, quality data that they can leverage for additional analysis.
Automation can’t replace critical thinking and creativity
Joel Shapiro, Clinical Associate Professor of Data Analytics and Academic Director at Kellogg School of Management, is quoted in a recent Innovation Enterprise article as saying “most businesses have unique processes, goals, and contexts that make the link from data to action fraught with nuance. Analytics still rests fundamentally on good critical thinking skills – how to ask great questions and rigorously assess evidence that can lead to action.”
Machine learning can be used to automate many repetitive data science tasks. But machine learning cannot take the place of data scientists (or marketers for that matter) when it comes to tasks that require critical thinking skills and creativity. The role of a data scientist often involves understanding the business objectives of marketing teams and senior executives, and implementing solutions based on those objectives- these are things machine learning cannot do, at least for the time being.
Janet Wagner is a Zylotech contributing writer.
If you liked this post, check out our other blog post on the new role of marketing operations and technology