Facing data scientist shortage, organizations turn to ML automation and embedded analytics

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Data-driven insights are wholly dependent on the ability of enterprises to collect and analyze data for actionable information about customers, markets, product development, and operations.

Up until recently this has meant hiring data scientists who know how to create machine-learning algorithms, build predictive computing models, integrate data from multiple sources (including unstructured data), and uncover the information most relevant to business strategy and goals.

But data scientists remain in great demand, which means they are hard to find and command salaries that can strain IT budgets. The shortage of trained data scientists has left many enterprises at a competitive disadvantage in the digital economy.

Automated Machine Learning

To fill the void, some organizations have opted to “democratize” data science by training numerate employees in different departments in areas such as data visualization, data manipulation, dynamic reporting, and R programming (an open-source programming language in data science and statistics).

Additionally, enterprises increasingly are turning to technologies such as artificial intelligence (AI), machine learning (ML), automation, and analytics programs pre-embedded in applications to collect and interpret data from consumers, devices, applications, databases, and other sources.

“Machine Learning APIs make it easy for developers to apply machine learning to a dataset to add predictive features to their applications,” writes Khushbu Shah in data and analytics website KDnuggets. “Machine Learning APIs provide an abstraction layer for developers to integrate machine learning in real world applications without having to worry about scaling the algorithms on their infrastructure and getting into the details of the machine learning algorithms.”

Google AutoML, AWS Sagemaker, and BigML are among the machine-learning APIs available to enterprises seeking to leverage data. Cloud-based AutoML is a suite of ML tools designed to help developers without extensive ML experience and skill train high-quality models that reveal data of importance to the business. Sagemaker provides users with one-click deployment of ML models and real-time metrics during the training process. BigML offers monthly subscriptions to its ML platform ranging in price from free to $10,000, along with private deployments and personalized training.

Automated ML frees up the large amounts of time data scientists spend on model testing, data prep, and other important but mundane activities. AutoML tools aren’t intended to “supplant data scientists,” writes Priya Dialani in Analytics Insight. Instead, they allow data scientists to “offload their routine work and streamline their procedure to free them and their teams to concentrate their energy and consideration on different parts of the procedure that require a more significant level of reasoning and creativity.”

Another way automated ML delivers value is scalability. Humans have limits to their time, energy, and workload capacity. By deploying automated ML, enterprises can transcend these limitations that hold back data science initiatives.

Benefits of embedded analytics

Some enterprises are meeting their data analytics needs by deploying software with pre-embedded analytics functions. In a study by Nucleus Research, enterprises using embedded analytics in their host applications reported numerous benefits, including reduced software build time up to 85%, lower costs from ongoing development and maintenance, and greater user productivity.

Further, Nucleus Research analyst Daniel Elman says, “because the analytics functionality is built into the business application, the user always has situational context around their data and insights, allowing non-data scientists to more comfortably create reports, derive insights, and understand the significance of their data.”

The best data in the world won’t do an organization any good if it is unable to extract and analyze that data. Automated ML and pre-embedded analytics are emerging tools that can enable enterprises with small (or even no) data science teams to be data-driven and data-smart.

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